a

Wearable Sensors in 

Transportation—Exploratory 

Advanced Research Program 

Initial Stage Investigation

The Exploratory Advanced Research Program

MARCH 2016
b

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Technical Report Documentation Page

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TECHNICAL	
  REPORT	
  DOCUMENTATION	
  PAGE

1. Report No.

FHWA-HRT-16-034

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Wearable Sensors in Transportation—Exploratory 

Advanced Research Program Initial Stage Investigation

5. Report Date

March 2016

6. Performing Organization Code: 

7. Author(s)

Elizabeth Machek, Jonathan Frazier, Amy Ingles, and Tom 

Morton

8. Performing Organization Report No. 

9. Performing Organization Name and Address

U.S. Department of Transportation Volpe Center

55 Broadway

Cambridge, MA 02142

Woodward Communications, Inc.

1420 N Street, NW, Suite 102

Washington, DC 20005

10. Work Unit No.

11. Contract or Grant No.

Contract DTFH61-14-V-00025

Contract DTFH61-15-A-00001

12. Sponsoring Agency Name and Address

Office of Corporate Research, Technology, and Innovation 

Management

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered 

Research Report, May–November 2015

14. Sponsoring Agency Code 

HRTM-30

15. Supplementary Notes

FHWA’s Contracting Officer’s Task Manager (COTM): Zachary Ellis, HRTM-30

Technical Contact: David Kuehn, HRTM-30

16. Abstract

This report summarizes an initial stage investigation into wearable sensors for transportation research 

applications. The Federal Highway Administration (FHWA) has observed significant activity in this area and 

seeks to obtain an understanding of the breadth of uses that researchers and practitioners envision for this 

technology, both now and in the future. Staff from the John A. Volpe National Transportation Systems Center 

prepared this report on behalf of FHWA’s Exploratory Advanced Research Program. The authors of this report 

discuss research into air quality, physiological, and activity sensors, which have specific transportation 

applications and are fairly well-developed. The authors include a summary of observations made by the project 

team regarding the use of wearable sensors for public sector transportation research. 

17. Key Words

Wearable sensors, transportation, technology, airquality sensors, physiological sensors, activity 

sensors, location sensors, smartphones, consumer 

devices, commercial products, tracking, data 

collection, passenger flow, transit planning.

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

52

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii
iii

Table of Contents

INTRODUCTION

 What Are Wearable Sensors?

 Wearable Sensors in Transportation

REPORT METHODOLOGY

 FHWA Interest

Office of Federal Lands Highway

Office of Planning, Environment, and Realty

Office of Highway Policy Information

 Federal Outreach

Environmental Protection Agency

Centers for Disease Control

National Institutes of Health

 Researcher Outreach

FINDINGS

 Findings From Local Governments and Universities

Sensor Maturity

Availability of Smartphones

Expensive Sensors

Motivated Participants

Transportation Agency Research

Privacy Issues

 Environmental and Air Quality Sensors

 Physiological and Activity Sensors

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 Research Methods, Data Analysis, and Privacy

Data Science Expertise

Privacy Issues

Data Management

Research Ethics

Monetization of Datasets

Smartphone Compatibility

Data Quality

Sensor Calibration

CASE STUDY: WEARABLE SENSORS IN LOCAL GOVERNMENT

CONCLUSION

APPENDIX A: LITERATURE REVIEW

APPENDIX B: LIST OF INTERVIEWEES

REFERENCES

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List of Figures

Figure 1. Test array of low-cost sensors. 4

Figure 2. Evaluation of an industrial hygiene volatile organic compound 4

sensor for citizen science value.

Figure 3. Deployment of a novel volatile organic compound sensor 7

for evaluation.

Figure 4. Deployment of a particulate matter sensor in a near-road test bed. 7
vi

List of Acronyms and Abbreviations

CDC Centers for Disease Control

EAR Exploratory Advanced Research

EPA Environmental Protection Agency

FHWA Federal Highway Administration

FLH Federal Lands Highway

GPS global positioning system

MPO metropolitan planning organization

NIH National Institutes of Health

PMI Precision Medicine Initiative
vii
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Report Summary
1

Introduction

Recent advances in hardware, connectivity, 

and data analysis have converged, 

making cheap and reliable wearable 

sensors available to transportation researchers. These advances make fine-grained data 

collection possible, where data fields are 

subdivided into multiple fields for detailed 

analysis. Researchers can analyze each field 

in isolation to address a wide variety of 

transportation research questions, allowing 

for new forms of research. 

The authors of this report document an initial 

stage investigation into wearable sensors 

for transportation research applications. The 

Federal Highway Administration (FHWA) 

has observed significant activity in this area 

and seeks to obtain an understanding of 

the breadth of uses that researchers and 

practitioners envision for this technology, 

both now and in the future. Transportation 

agencies now have a wealth of new tools 

and techniques at their disposal to measure 

key variables at several scales. Staff from the 

John A. Volpe National Transportation Systems 

Center prepared this report on behalf of 

FHWA’s Exploratory Advanced Research (EAR) 

Program, which focuses on longer term, higher 

risk research areas with a high payoff potential. 

For this report, the project team focused on 

air quality, physiological, and activity sensors, 

which have specific transportation applications 

and are fairly well-developed. The project 

team concluded this report with a summary 

of observations regarding the use of wearable 

sensors for public sector transportation research. 

WHAT ARE WEARABLE SENSORS?

Wearable sensors can be defined as sensors 

that are small and robust enough to be 

carried directly on a person’s body, such 

as a wristband or jewelry, or built into a 

smartphone. They are frequently used 

to collect data on environmental, physiological, activity, and location variables. 

Miniaturization and cost reduction of sensors, along with improvements in wireless 

connectivity, have led to an explosion of 

off-the-shelf consumer devices. Consumers 

and corporations track physical activity, 

sleep patterns, travel patterns, and more, 

and the data collected are feeding “big data” 

analytics and enabling new fields of research. 

Although commercial products are already 

in wide use, active research into more sophisticated sensors is ongoing. Regardless 

of format, wearable sensors collect data for 

one or more variables and are carried on a 

person’s body. 

WEARABLE SENSORS IN 

TRANSPORTATION

Transportation researchers are interested 

in the application of wearable sensors to 

improve existing research and to open up 

avenues for new research. For example, 

wearable sensors could provide a way to use 

global positioning system (GPS) technology 

to track subjects’ movements in a way that 

is less intrusive and more accurate than are 

traditional travel diaries, and potentially less 

intrusive than using a subject’s smartphone. 
2

Wearable sensors also enable travel-behavior 

studies in areas and among populations where 

smartphone use may not be high (e.g., among 

the elderly or impoverished). This could result 

in the following benefits:

• More robust multimodal travel-behavior 

surveying. This would allow measurement 

of passenger travel to the same degree as 

parcel freight on a total trip, door-to-door 

basis, unlike current intermodal travel and 

travel by multiple services, which are not 

easily studied.

• Increased understanding of passenger flows 

and facility crowding. This would improve 

transit facility planning and operations.

By combining GPS data with physiological 

sensors, a whole new range of data can emerge. 

This could lead to the following additional 

benefits:

• Achieving a better understanding of multiple 

data fields (i.e., fine-grained data) related 

to transportation impacts. This could 

be accomplished through combined 

physiological and environmental sensors 

that monitor the wearer’s travel patterns, 

localized air quality, and physiological 

markers.

• Monitoring local residents’ exposure to 

transportation-related environmental 

impacts in remote areas. These impacts 

are difficult to observe with traditional 

monitoring equipment.

• Acquiring a fuller understanding of the 

relationship between transportation 

and health. This could be achieved by 

monitoring individual travel patterns, 

physical activity, and health indicators.

• Assessing risk factors for transportation 

workers. This could be achieved by 

tracking environmental exposure.

• Improving assessment of operator 

fitness for duty (e.g., fatigue). This could 

enable the development of conditionbased, rather than time-based, “hours of 

service” regulations.
3

The project team conducted a research 

scan and identified a group of researchers 

who are developing technologies and 

strategies for wearable sensors designed to 

collect data through personal monitors.(1) This 

follows earlier initial stage efforts in this topic 

area, including a literature review on wearable 

sensors designed for assessing transportation 

impacts (from 2010 to 2014). The literature 

review is included in Appendix A.

The project began in the spring of 2015 when 

the project team held discussions with several 

FHWA researchers to learn about gaps in existing 

sensors and how they thought wearable sensors could improve their work. The project 

team then interviewed Federal agencies, local 

government representatives, and subject matter experts who have research experience in 

using wearable sensors. The goal was to identify 

best practices for public agencies and to understand the state of the practice. The list of 

interviewees is included in Appendix B.

FHWA INTEREST

To better understand the current gaps in existing 

sensors and how wearable sensors could potentially improve their work, the project team 

members conducted interviews with FHWA 

researchers. They interviewed staff from the 

Office of Federal Lands Highway (FLH); Office 

of Planning, Environment, and Realty; and 

Office of Highway Policy Information. The 

interests of these FHWA researchers included 

major research applications for wearable 

sensors related to activity, location, health, 

and environmental conditions. 

Office of Federal Lands Highway

FLH works to improve transportation on 

Federal and tribal lands, which are 

frequently in rural and remote locations 

and have a high share of unpaved roads. 

Unpaved roads produce large amounts of 

airborne dust, which create health and 

safety risks as a particulate pollutant. FLH 

is currently running a pilot dust control 

project in an Alaskan native community 

but faced high costs to transport and 

operate traditional fixed air monitors. FLH is 

interested in the use of wearable sensors as 

an alternative or supplement to traditional 

dust-monitoring methods, especially in 

locations where these methods are 

difficult and expensive. Wearable particulate sensors available on the commercial 

market were not ready for use when this 

project began in the summer of 2015, but 

FLH remains interested. 

Office of Planning, Environment, and 

Realty

The Livability Team in the Office of Planning, 

Environment, and Realty is interested in 

collecting air quality and emissions data to 

support environmental justice, especially by 

working with community members and 

citizen scientists. Good data can be 

collected today with traditional sensors and 

methods, but this is usually costly, and 

coverage is poor. Wearable air quality 

sensors that are simple and inexpensive can 

be used to both expand sensor coverage 

and improve community engagement in 

environmental justice.

Report Methodology
4

Office of Highway Policy Information

The Office of Highway Policy Information 

specializes in highway data. Interests 

include the use of wearable sensors to 

detect and quantify active transportation, 

such as walking and cycling, without 

requiring manual traffic counts or trip 

diaries. These data can be used when 

modeling regional health or to create better 

bicycle and pedestrian travel models. 

FEDERAL OUTREACH

To better understand the current use 

of wearable sensors in Federal agency 

research, the project team contacted 

representatives from the Environmental 

Protection Agency (EPA), Centers for Disease 

Control (CDC), and National Institutes of 

Health (NIH) to discuss their respective 

experiences and current activities. 

Environmental Protection Agency

EPA’s Office of Research and Development 

advances environmental science and has 

been working with wearable air quality sensors 

for several years. EPA scientists saw a flood 

of cheap sensors being used by members of 

the public. Enthusiasm was high, but sensors 

were inaccurate and methods for using 

the data were unsophisticated. EPA has 

since been working with sensor hardware 

manufacturers and citizen scientists to 

improve the quality of both hardware and 

research methods. EPA is helping lead the 

Exposure Science in the 21st Century Federal 

Working Group, which is coordinating more 

than 20 Federal agencies working together 

to promote the use of modern technologies 

and methods for environmental science. 

EPA initiatives like the Citizen Scientist 

Toolbox are helping to improve the quality 

of data collected by members of the public 

who are using cheap sensors, with the hope 

that they can fill gaps in regional air-quality–

monitoring networks. EPA is funding the 

environmental research community to investigate wearable air quality sensors, most 

recently through a $4.5-million Air Pollution 

Monitoring for Communities grant that will:

• Study the use of portable and wearable 

sensors to monitor air pollution (shown in 

figure 1 and figure 2).

• Encourage the community use of sensors 

and data to help advocate for cleaner air.

• Develop different methods for working 

with data collected by novel air quality 

sensors.

• Compare new sensor technologies and 

practices against current methods.

Figure 1. Test array of low-cost sensors.

Protection Agency© Ron Williams, U.S. Environmental 

Figure 2. Evaluation of an industrial hygiene volatile 

organic compound sensor for citizen science value.

Protection Agency© Ron Williams, U.S. Environmental 
5

Centers for Disease Control

CDC’s Epidemiology and Surveillance 

Team is one of several teams working on 

the agency’s fight against obesity, which 

has been recognized as a major public 

health issue. One of their research topics 

is the use of accelerometers that quantify 

walking and cycling, a technique that is 

rapidly advancing thanks to the broad 

adoption of cheap, commercially available 

activity sensors.

CDC and the Public Health Service participate 

in research—working with other Federal 

agencies, university researchers, and local 

governments—to make use of this data 

on both national and personal scales. 

Widespread use of sensors coupled with 

modeling makes broad surveillance of 

physical activity possible. Researchers 

and policymakers will be able to gauge 

the effectiveness of various public health 

campaigns and active transportation infrastructure improvements. On a smaller 

scale, wearable activity sensors can be 

a motivational tool to help people lose 

weight by encouraging them to walk, run, 

or bike more. 

National Institutes of Health

NIH is the Nation’s premier health research 

agency and has funded extensive research on the use of wearable sensors. 

NIH staff help advance wearable sensor 

research in many different health topics, 

including air quality and physical activity sensors of interest to transportation 

researchers. The agency’s funding announcements draw considerable interest from 

academic researchers as well as from 

private companies and non-profits. The 

agency uses a variety of grant and contract 

mechanisms, including the Small Business 

Innovation Research program, for developing 

hardware and algorithms. 

NIH is taking a leading role in the Precision 

Medicine Initiative (PMI), a massive datacollection experiment that hopes to recruit 

a million participants. Data from wearable 

sensors are just some of the many types of 

data that will be collected from volunteers. 

The dataset will include genetic information, 

behavior samples, and wearable sensor data 

to inform prevention and treatment. PMI will 

begin by pulling participants from existing 

cohorts and importing data (donated by 

researchers and companies) that have 

already been collected.

Using this dataset, researchers will be able 

to test correlations among physical activity 

and location, behavior, genetics, and other 

variables collected in an electronic health 

record. NIH is also using the experiment as a 

chance to test wearable sensors against each 

other and the validity of data collected from 

laboratory-grade sensors with commercial 

mass-produced devices. 

RESEARCHER OUTREACH

During the summer of 2015, FHWA conducted 

several interviews with the following subject 

matter experts from local government and 

universities: 

• Stephen Intille, Northeastern University. 

• Kevin Patrick, University of California, San 

Diego.

• Francis Tsow, Arizona State University.

• Rochelle Carpenter, Nashville Area Metropolitan Planning Organization (MPO).

• Santosh Kumar, University of Memphis. 

• William Riley, NIH.

Appendix B contains more information 

regarding the interviews. The researchers 

have a wide range of expertise across several 

sensors types. Lessons learned from these 

discussions are shared in the following section.
6

FINDINGS FROM LOCAL 

GOVERNMENTS AND UNIVERSITIES

The subject matter experts from local 

government and universities highlighted the 

following lessons during their discussions 

with the project team.

Sensor Maturity

Sensors are sufficiently mature for use in 

transportation research. Wearable sensors 

are generally small enough, efficient 

enough, and accurate enough for many 

transportation uses. Techniques and tools 

to analyze large volumes of data collected 

from wearable sensors are becoming 

more widespread. 

Availability of Smartphones

Wearable sensor research benefits from 

off-the-shelf technology. In addition to hosting 

their own range of sensors, the widespread 

diffusion of low-cost smartphones makes 

the use of dedicated wearable sensors far 

easier. As a result, an emerging best practice 

is to have a “dumb” wearable sensor collecting raw data (i.e., the sensor collects data 

but does not engage in processing tasks), 

which is then transmitted via low-power 

Bluetooth® to a study participant’s tethered smartphone. Software installed on 

the connected phone can handle all datacollection and processing tasks and transmit the information back to the researcher 

for analysis.

Expensive Sensors

Research-grade wearable sensors are still 

expensive. A lack of demand means that 

few research-grade wearable sensors are 

mass-produced and commercially available, 

especially for air-quality monitoring. Many 

of the sensors used for research are custom 

orders or are assembled by the researchers 

themselves and can have cost, supply, and 

usability issues often associated with smallbatch prototypes. 

Motivated Participants

Study participants are willing to help when 

research produces actionable results. Privacy 

and data access issues concern researchers 

and institutional review boards; however, 

volunteers typically are enthusiastic if they 

can be motivated by the potential benefits 

of the study. They are especially willing to 

participate in studies when the information 

produced is personally meaningful and 

something that they can use to make improvements in their daily lives. A single 

person cannot do much to change the air 

quality of his or her region, but if a person 

can see that his or her daily physical 

activity levels are low, then he or she may 

be encouraged to replace car trips with 

walking and biking. 

Transportation Agency Research

Transportation agencies are moving slowly in 

this area. The literature review and outreach 

revealed only one transportation agency that 

has used wearable sensors to collect data 

related to health and active transportation 

(see the Nashville Area MPO case study at 

the end of this section). Transportation 

agencies are just beginning to conduct their 

own wearable sensor studies or purchase 

data collected by others.

Findings
7

Privacy Issues

Privacy issues make academic and Government 

use more difficult. Many personal datasets, 

particularly when location data is included, 

come with serious privacy concerns. Public 

sector researchers must carefully safeguard 

personally identifiable information, and academic researchers must face institutional 

review boards that are unaccustomed to 

large scale use of sensitive data.

Environmental and Air Quality Sensors

Several of the interviewees have developed 

or studied the use of different types of 

environmental sensors. Some applications 

of wearable environmental sensors for public 

agency researchers include the following:

• Monitoring exposure to exhaust from 

automobiles, especially nitrous oxide.

• Measuring volatile organic compounds to 

determine exposure rates and speciation 

of volatile organic compounds encountered by an individual throughout his or 

her day (figure 3).

• Monitoring exposure to particulates from 

exhaust, construction, dust, and other 

transportation sources (figure 4). 

• Measuring exposure to noise pollution on a 

personal scale.

Transportation agencies use environmental 

sensors for a wide variety of tasks, from 

regional monitoring and modeling of 

emissions to project-level construction 

monitoring and mitigation. Most of the 

limitations of the current environmental 

sensor technology have to do with their size 

and power efficiency. The most effective 

particulate sensors are bulky and may 

require active motors, which draw a lot of 

power. For example, sensors that are able 

to perform speciation require extra power 

and have lower resolution than those that 

only measure exposure. All of these 

limitations to traditional environmental 

sensors are usually carried over to wearable 

sensors as well. These problems have 

caused particulate sensors to lag behind 

other wearable sensors. Researchers in the 

field of hardware research and development 

are currently working on improving size, 

accuracy, and power efficiency.

Wearable environmental sensors are relatively 

expensive because demand for them is low. 

There is not much consumer interest in 

wearable environmental sensors because 

the data collected are not actionable for the 

average person. For example, a reading of 

poor localized air quality in a study participant’s neighborhood is certainly cause for 

alarm; however, there is not much that the 

average person can do to improve his or her 

localized air quality, beyond moving to 

another neighborhood or advocating to 

Figure 3. Deployment of a novel volatile 

organic compound sensor for evaluation.

Protection Agency© Ron Williams, U.S. Environmental 

Figure 4. Deployment of a particulate 

matter sensor in a near-road test bed.

Protection Agency© Ron Williams, U.S. Environmental 
8

mitigate pollution from the source. Higher 

demand would encourage large-scale 

manufacturing, eventually bringing the cost 

down. Lower equipment costs would 

improve the ability of researchers to conduct 

field studies.

PHYSIOLOGICAL AND ACTIVITY 

SENSORS

A growing use of wearable sensors is for the 

measurement of physiological responses to 

the environment and to user activities, often 

for fitness and other personal health purposes. Some example applications include 

the following:

• Measuring levels of walking or cycling by 

using GPS and accelerometers on both the 

individual and the population scales.

• Estimating vehicle operators’ stress levels 

by monitoring their cardiovascular, respiratory, and thermoregulatory systems and 

then associating moments of stress with a 

particular road segment or intersection by 

using GPS.

• Identifying pre-asthma attacks and other 

acute health crises that could affect a 

vehicle operator and monitoring the intake 

of medication.

• Estimating cardiovascular function by 

measuring the release of carbon dioxide, 

thereby assessing efficiency of respiration 

and exertion levels. 

Unlike environmental sensors, there has 

been considerable public interest in physiological sensors, with a particular interest in 

fitness sensors. Sensors that are commercially available and smartphone applications 

made by private companies—such as Strava, 

Fitbit, and Runkeeper—can track activity by 

using GPS and built-in accelerometers and 

estimate metabolic expenditure by using 

heart rate. Interest in losing weight and 

improving physical fitness has driven the 

popularity of these wearable sensors, amassing 

a wealth of activity- and health-related data 

for thousands of users. The use of smartphone technology in activity sensor suites 

takes advantage of the fact that smartphones 

are nearly ubiquitous and are powerful 

enough to handle much of the processing 

from an external sensor. Accelerometers are 

now ubiquitous in smartphones, and although 

they are less accurate than fitness bands, 

they enable every smartphone owner to 

track their own activity without purchasing 

dedicated hardware.

A challenge lies in accessing these data because 

the device manufacturers maintain ownership, 

and most have, until recently, been hesitant to 

sell their raw data or processed analyses. In 

addition, these datasets are unlikely to capture 

the entire population, as the users are often 

early adopters who are educated, wealthy, and 

fitness or technology hobbyists. The wealth of 

information that these types of sensors are 

beginning to create is essentially a cross-section 

of its current users. Once this population of users is 

broad enough to represent the larger population, 

researchers will be able to develop individualscale computational models based on statistical 

analysis of the whole dataset.

RESEARCH METHODS, DATA ANALYSIS, 

AND PRIVACY

New data collection is enabling large advances 

in the methods and tools used to analyze these 

new datasets. Study design and experimental 

regimens are also improving, but legal and 

ethical questions still remain. Several of these 

questions limit the research that can be 

performed by government and university 

researchers, many of whom are partnering with 

commercial and non-profit organizations that 

have more freedom to act. These questions are 

outlined in the following section.
9

Data Science Expertise

The large scale and fine fidelity of data collected 

by wearable sensors are a challenge to 

transportation agencies without data 

science expertise. Having robust data storage 

and analysis capabilities are essential to 

using some of the new datasets that are 

now available with wearable sensors. Costs 

are high but are declining as more people 

are trained in modern data-analysis methods. 

Privacy Issues

Privacy remains a major concern for 

many. Study participants’ opinions about 

information-sharing may vary widely depending on demographics, immigration 

status, and other factors. An educated 

technology enthusiast may be willing to 

participate in a study that tracks his or her 

exposure to emissions along different walking 

routes, but an undocumented immigrant 

might be wary, especially if the research is 

conducted directly by a Government agency. 

In addition, these sensors are meant to be 

worn on the user’s person, which may increase the sense of intrusiveness. Hacking of 

personal data is also a concern, particularly 

for health data. 

Data Management

Privacy measures may compromise data 

management. Catering to privacy concerns 

may compromise the researchers’ ability to 

manage the datasets effectively. For example, participants in the PMI will be able to 

select the types of data that they want to 

submit, providing a level of personal privacy. 

This means that the researchers will have to 

determine how to handle missing data 

points that are not randomized but are 

instead selected by individual participants.

Research Ethics

Existing systems of research ethics are not 

yet suited to working with data science and 

wearable sensors. Institutional review boards 

at universities struggle with aggregate data 

and permissions issues. Government research 

departments also struggle with ethical issues 

surrounding the data collected by wearable 

sensors. Many research frameworks were 

developed for traditional biomedical research, and researchers have not yet had 

time to answer the questions raised by the 

use of always-on sensors measuring very 

personal variables. Working with companies 

that collect data from wearable sensors may 

make issues with institutional research 

boards and consent irrelevant, assuming 

that companies have placed clear language 

in their terms of service. Some of the data 

already collected by companies may not be 

usable, because research purposes were not 

included in the terms of service.

Monetization of Datasets

Companies tend to guard the data they 

collect from commercial wearable sensors 

carefully. A few now sell raw data or analyses 

to researchers, but many others have not 

yet been able to monetize their datasets. 

Even when data are available for sale, high 

prices can be out of reach for many smaller 

research projects. Many wearable sensor 

companies are hiring data scientists and 

other researchers to work internally with 

their own datasets and have released 

occasional samples or one-off studies. In addition, algorithms within commercial sensor 

applications that convert the accelerometer 

data to activities are propriety. Different 

companies use different assumptions and 

sensor hardware to collect the data; thus, 

raw data varies highly from one product 

to another.

Smartphone Compatibility

Leveraging smartphones simplifies research. 

The likelihood of proper use of a smartphone by study participants is higher than 

for a separate dedicated sensor because 

most people are used to handling them, car-
10

rying them at all times, and keeping them 

charged. One challenge related to this 

approach is the variation present among 

smartphone compatibility, operating systems, and hardware sensor differences. 

Developing for a wide range of smartphones 

and keeping an application up to date can 

be a challenge for a small research team.

Data Quality

Multiple sensors improve data quality. 

A suite of sensors could be placed on various points of the body and connect wirelessly to a smartphone, allowing for the 

measurement of different physiological 

characteristics with only one device. Using 

multiple sensors improves data quality 

because the data from different sensors can 

be cross-checked and correlated with one 

another. For measuring activity and physiological response, single sensors worn at the 

wrist are fairly accurate; however, placing 

multiple sensors on different parts of the 

body (e.g., arm, chest, and leg) can capture 

different types of movement to enable the 

characterization of different types of activities 

(e.g., walking and bicycling). This is an 

important difference when using activity 

data to understand transportation mode 

and route choice.

Sensor Calibration

Commercial sensor calibration could reduce 

costs for transportation researchers. PMI, 

described earlier in this report, will collect 

location and activity data from the vast 

majority of participants, whereas a smaller 

cohort will be used to compare commercialand research-grade wearable activity 

sensors for calibration purposes. The 

calibration of commercial wearable sensors 

would help transportation agencies who are 

interested in using this type of data to plan 

active transportation facilities, identify 

safety concerns for bicyclists and pedestrians, and understand which routes are 

most used by bicycles or pedestrians.
11

CASE STUDY: WEARABLE 

SENSORS IN LOCAL 

GOVERNMENT

Local governments have a strong interest 

in using wearable sensors to measure 

the effects of infrastructure and other 

improvements on transportation habits of 

their constituents. The Nashville Area MPO 

is leading the way among transportation 

agencies with respect to investigating 

the relationships between transportation 

planning and public health issues. One 

study in particular, the Middle Tennessee 

Transportation and Health Study, was 

sponsored by the Nashville Area MPO, the 

Clarksville Urbanized Area MPO, and the 

Tennessee Department of Transportation. 

The researchers used wearable sensors 

to connect transportation behaviors with 

health-related data. In 2012, as part of their 

household travel survey, the Nashville 

Area MPO instructed some participants to 

wear GPS devices and/or accelerometers 

to supplement the data collected 

from the survey. The travel survey also 

contained health-related questions along 

with the typical transportation questions. 

The Nashville Area MPO was able to 

determine how many people in the region 

use active transportation for some, or all, 

of their travel. The agency is analyzing 

the data with the aim of associating 

active transportation behaviors with 

health outcomes. The Nashville Area MPO 

is using a model called the Integrated 

Transport and Health Impact Modelling 

Tool to identify connections among 

health-related behavior, such as active 

transportation and health outcomes. This 

tool, which can estimate changes in 14 

diseases associated with physical activity 

increases, has also been used by the San 

Francisco Metropolitan Transportation 

Commission in coordination with the 

California Department of Public Health, 

and by the Oregon Department of 

Transportation in coordination with the 

Oregon Health Authority. 

The limited use of this tool and others may 

be related to the difficulty and expense 

of collecting a broad dataset of active 

transportation behavior. The Nashville 

Area MPO found affordability to be a 

barrier to expanding their data collection 

beyond 6,000 households, which proved 

to be too small a sample size to answer 

some of the larger questions about the 

public health implications of active 

transportation.

For more information about this and other 

health and transportation initiatives at 

the Nashville Area MPO, visit http://www.

nashvillempo.org/regional_plan/health/.
12

In summary, the project team found that 

wearable sensor technology is generally 

ready for a range of transportation applications. The devices are small, efficient, 

and accurate. Research-grade devices are 

fairly expensive, but commercial-grade 

sensors that take advantage of the power 

of smartphones can create a wealth of usable 

data that are becoming easier to obtain 

and analyze. Techniques and tools to analyze large volumes of data collected from 

wearable sensors are becoming more 

widespread, but data management is still 

an area that needs improvement. Although 

privacy can still be an issue in certain 

contexts and for specific populations, several 

of the subject matter experts interviewed for 

this project observed during their own 

research with wearable sensors that 

participants are willing to contribute their 

personal data if the results are understandable and actionable for them. These 

privacy concerns can be barriers for 

academic research because standard 

institutional review boards have not 

caught up with big data trends and are not 

unaccustomed to large-scale use of sensitive 

data. These limitations have caused 

transportation agencies to move slowly 

on using wearable sensors to complement 

their other data-collection efforts; however, 

the potential benefits are compelling, and 

more agencies are expected to adopt 

these methods in the future.

Conclusion
13
14

Appendices
15

APPENDIX A: LITERATURE REVIEW

Reference Results

Environment 

Personal pollution monitoring: Mobile real-time air quality in daily life

Author(s): Bales, E.;1

 Nikzad, N.;1 Ziftci, C.;1 Quick, N.;2 Griswold, W.;1 Patrick, K.2

Year: 2014

Author affiliation: 1

Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 

92093-0404, United States; http://www.cse.ucsd.edu/

2

School of Medicine, University of California, San Diego, La Jolla, CA 92093-0602, United States; 

http://som.ucsd.edu/

Poor air quality is a growing global health concern that impacts millions of people worldwide. Although

the scientific community is beginning to understand the health impacts of air pollution, it remains a 

challenge to provide people with the information they need to be able to make health-conscious choices. 

The CitiSense system gives individuals the real-time tools they need to identify when and where they are 

exposed to poor air. The authors present the results of a qualitative study regarding a 4-week “in the wild” 

deployment of the CitiSense air quality sensor and system. The authors focus on how the 16 participants 

in the study responded to their new-found information about their environment, how they shared 

information, and what kinds of actions were enabled by having access to real-time air quality data. 

Quantitative data gathered through the course of the study framed participant responses by showing 

what levels of pollution were experienced and what activities heightened exposure. The authors found 

that CitiSense’s real-time graphical displays and everywhere-monitoring provided a critical bridge 

between data and experience, enabling sophisticated in-the-world sense-making and sharing with those 

nearby. This, in turn, affected behavior and attitudes, leading to shifts in how users reasoned about their 

world and how they assessed their personal choices and impact.

An app-based air quality social sensing system built on open source hardware/software tools

Author(s): Capezzuto, I.;1

 Abbamonte, L.;1 De Vito, S.;1 Fattoruso, G.;1 Massera, E.;1 Buonanno, A.;1 Formisano, 

F.;1

 Di Francia, G.1

Year: 2015

Source: Lecture Notes in Electrical Engineering, vol. 319, pp. 309–313

Publisher: Springer Verlag

Author affiliation: 1

UTTP-MDB Department, ENEA, Italian Agency for New Technologies Energy and Sustainable Economic 

Development, 80055 Portici, Italy; http://www.enea.it/en/research-development

The use of conventional tools of analysis for air quality has shown strong limitations in spatio-temporal 

coverage of urban scenarios. The required level of service needs a distributed approach, based on a 

dense network of mobile sensor nodes. Developments in sensor technologies and communications, 

together with constant social innovation, now make it possible for citizens to cooperate with each other, 

producing and socializing data for the simultaneous benefit of the individual and the community. The 

authors aim to develop this concept and apply it to the distributed monitoring of air quality. The goal is 

to enable individuals to monitor their exposure to air pollution while contributing to the construction of a 

map of the state of urban air quality through the sharing of data.
16

Design and clinical feasibility of a personal wearable monitor for measurement of activity and 

environmental exposure 

Author(s): Fletcher, R. R.;1

 Oreskovic, N. M.;2 Robinson, A. I.3

Year: 2014

Source: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology 

Society (EMBC), pp. 874–877

Publisher: IEEE

Author affiliation: 1

D-Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; http://d-lab.mit.edu/

2

Harvard Medical School, Boston, MA 02115, United States; https://hms.harvard.edu/

3

Division of General Academic Pediatrics, MassGeneral Hospital for Children, Boston, MA 02114, United 

States; http://www.massgeneral.org/children/research/general-academic-pediatrics-ongoing-projects.aspx

Human exposure to specific environmental factors (e.g., air quality, lighting, and sound) is known to play 

an important role in the pathogenesis of many chronic diseases (e.g., asthma) and mental health disorders 

(e.g., anxiety). Conventional fixed environmental monitoring stations are, however, sparsely located and, 

despite environmental models, cannot adequately assess individual exposure levels. New forms of lowcost portable monitors have begun to emerge that enable the collection of higher spatial density “crowd 

sourced” data; however, the first generation of these low-cost environmental monitors has generally not 

been suitable for clinical environmental health studies due to practical challenges such as calibration, 

reproducibility, form factor, and battery life. The authors present a wearable environmental monitor that 

overcomes these challenges and can be used in clinical studies The new device, called “Eco-Mini,” can be 

used without a smartphone and is capable of locally sampling and recording a variety of environmental 

parameters (ozone, sulfur dioxide, volatile organic compounds, humidity, temperature, ambient light 

color balance, and sound level) as well as individual activity (3-axis accelerometer) and location (Global 

Positioning System). The authors also report their findings and discuss lessons learned from a feasibility 

study conducted with pediatric patients for 1 week as part of an ongoing asthma research study.

Air pollution exposure estimation and finding association with human activity using wearable 

sensor network

Author(s): Hu, Ke;1

 Davison, Timothy;1 Rahman, Ashfaqur;2 Sivaraman, Vijay1

Year: 2014

Source: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, 

pp. 48–55

Publisher: Association for Computing Machinery

Author affiliation: 1

School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 

2052, Australia; http://www.engineering.unsw.edu.au/electrical-engineering/

2

Commonwealth Scientific and Industrial Research Organisation (CSIRO), Battery Point, TAS 7004, 

Australia; http://www.csiro.au/

Air quality and pollution monitoring services are provided by many countries and cities. Individuals are, 

however, more concerned about personal exposure and dosage, which can rarely be estimated due to the 

low spatial resolution of air pollution data and lack of personal data. In recent years, an increasing number 

of research groups have focused on increasing the spatial resolution of air pollution data using ubiquitous 

sensor networks. These works did raise the spatial granularity compared with data from fixed air pollution 

monitoring sites. The authors combine air pollution and human energy expenditure data to give individuals 

real-time personal air pollution exposure estimates. In particular, the authors describe their experiences 

with developing a personal air pollution exposure estimation system utilizing participatory air pollution 

monitoring system and energy expenditure data collected from wearable activity sensors. Their system 

and applications will benefit the understanding of the relationship between air pollution exposure and 

personal health. The authors also conducted a trial to get a full day of air pollution inhalation dosage data 

for one participant, and applied multiple data-mining techniques to find associations between activity 
17

mode, location, and the inhaled pollution. Results show that sleep, having meals, working in a campus, 

and general home activities like reading books will lead to a low air pollution dosage, although working 

out, walking, and driving will cause a higher inhaled dose. Furthermore, classification results in the study 

based on activity modes, locations, and dosage data, which are collected in the trial, show that up to 94 

percent classification accuracy can be achieved.

Wearable apparatus for multiple types of physiological and/or environmental monitoring 

Author(s): LeBoeuf, Steven Francis;1

 Tucker, Jesse Berkley;1 Aumer, Michael Edward1

Year: 2014

Patent No.: US20140243617 A1

Original Assignee: Valencell, Inc.

Author affiliation: 1

Valencell, Inc., Raleigh, NC 27609, United States; http://www.valencell.com/

The authors provide wearable pieces of apparatus for monitoring various physiological and environmental 

factors. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors 

integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental 

data are collected and wirelessly transmitted into a wireless network, where the data are stored and/or 

processed. There is growing market demand for personal health and environmental monitors (e.g., for 

gauging overall health and metabolism during exercise, athletic training, dieting, and physical therapy). 

Traditional health monitors and environmental monitors, however, may be bulky, rigid, and uncomfortable—

generally not suitable for use during daily physical activity. There is also growing interest in generating 

and comparing the health and environmental exposure statistics of the general public and particular 

demographic groups. For example, collective statistics enable the healthcare industry and medical 

community to direct healthcare resources to where they are most highly valued; however, methods of 

collecting these statistics may be expensive and laborious, often utilizing human-based recording/analysis 

steps at multiple sites. As such, improved ways of collecting, storing, and analyzing personal health and 

environmental information are needed. In addition, improved ways of distributing raw and analyzed 

personal health and environmental information are desirable to support efforts to enhance healthcare 

quality and reduce costs.

Using Global Positioning Systems and temperature data to generate time–activity classifications for 

estimating personal exposure in air monitoring studies: An automated method 

Author(s): Nethery, Elizabeth;1

 Mallach, Gary;1 Rainham, Daniel;2 Goldberg, Mark S.;3,4 Wheeler, Amanda J.1,5

Year: 2014

Source: Environmental Health, vol. 13, no. 33

Publisher: BioMed Central

Author affiliation: 1

Water and Air Quality Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada; 

http://www.hc-sc.gc.ca/ahc-asc/branch-dirgen/hecs-dgsesc/sep-psm/waccb-beacc-eng.php

2

Environmental Science Program, Dalhousie University, Halifax, NS B3H 4R2, Canada; 

http://www.dal.ca/faculty/science/environmental-science-program.html

3

Department of Medicine, McGill University, Montreal, QC H4A 3J1, Canada; 

http://www.mcgill.ca/deptmedicine/home

4

Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC H3A 1A1, Canada; 

http://www.clinepi.mcgill.ca/

5

School of Natural Science, Edith Cowan University, Joondalup, WA 6027, Australia; 

http://www.ecu.edu.au/schools/natural-sciences/overview

Personal exposure studies of air pollution generally use self-reported diaries to capture individuals’ time–

activity data. Enhancements in the accuracy, size, memory, and battery life of personal Global Positioning 

Systems (GPS) units have allowed for higher resolution tracking of study participants’ locations. Improved 
18

time–activity classifications combined with personal continuous air pollution sampling can improve 

assessments of location-related air pollution exposures for health studies. Data were collected using GPS 

and the personal temperatures from 54 children with asthma living in Montreal, Canada, who participated 

in a 10-day personal air pollution exposure study. A method was developed that incorporated personal 

temperature data and then matched a participant’s position against available spatial data (i.e., road 

networks) to generate time–activity categories. The diary-based and GPS-generated time–activity 

categories were compared and combined with continuous personal PM2.5 data to assess the impact of 

exposure misclassification when using diary-based methods. There was good agreement between the 

automated method and the diary method; however, the automated method (means: outdoors = 5.1 

percent, indoors other = 9.8 percent) estimated less time spent in some locations compared to the diary 

method (outdoors = 6.7 percent, indoors other = 14.4 percent). Agreement statistics (AC1 = 0.778) suggest 

"good" agreement between methods over all location categories. Location categories (“outdoors and 

transit”), however, where less time is spent, show greater disagreement (e.g., mean time “indoors other”): 

using the time–activity diary was 14.4 percent compared to 9.8 percent using the automated method. 

Although mean daily time “in transit” was relatively consistent between the methods, the mean daily 

exposure to PM2.5 while “in transit” was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 

using the daily diary. Mean times spent in different locations as categorized by a GPS-based method were 

comparable to those from a time–activity diary, but there were differences in estimates of exposure to 

PM2.5 from the two methods. An automated GPS-based time–activity method will reduce participant 

burden, potentially providing more accurate and unbiased assessments of location. Combined with 

continuous air measurements, the higher resolution GPS data could present a different and more accurate 

picture of personal exposures to air pollution.

A sensor-based method for occupational heat stress estimation

Author(s): Pancardo, Pablo;1

 Acosta Escalante, Francisco D.;1 Hernández-Nolasco, José Adán;1 Wister, 

Miguel A.;1

 López-de-Ipiña, Diego2

Year: 2014

Source: Ubiquitous Computing and Ambient Intelligence: Personalisation and User Adapted Services. 

Lecture Notes in Computer Science, pp. 251–258

Publisher: Springer Verlag

Author affiliation: 1

División Académica de Informática y Sistemas, Juarez Autonomous University of Tabasco, Cunduacán,

TB 86690, Mexico; http://www.ujat.mx/dais

2

DeustoTech, Deusto University, 48007 Bilbao, Spain; 

http://www.deustotech.deusto.es/cs/Satellite/ingenieria/en/deustotech?cambioidioma=si

Occupational Heat Stress (OHS) happens when a worker is physically active in hot environments. OHS 

can produce a strain on the body that leads to discomfort and, eventually, to heat illness and even death. 

Related International Organization for Standardization (ISO) standards contain methods to estimate OHS 

and to ensure the safety and health of workers, but they are subjective, impersonal, performed a posteriori, 

and are even invasive. The authors hypothesize that a real-time automated method is more effective and 

objective for estimating OHS if it fuses data from environmental sensors and unobtrusive physiological 

body sensors, and takes into account the user profile. The authors propose a personalized method based 

on ergonomic calculations to offer a solution. They found that this method allows for estimating the 

personalized effort levels, energy expenditure, and drudgery of work for each worker and enables the 

user to make informed decisions to control OHS. The authors believe that ISO standards could consider 

technological advances to propose real-time personalized methods of estimating OHS.
19

The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

Author(s): Piedrahita, R.1

; Xiang, Y.;2 Masson, N.;1 Ortega, J.;1 Collier, A.;1 Jiang, Y.;3 Li, K.;4 Dick, R. P.;2 Lv, Q.;3

Hannigan, M.;1 Shang, L.4

Year: 2014

Source: Atmospheric Measurement Techniques Discussions, vol. 7, no. 3, pp. 2425–2457

Publisher: European Geosciences Union

Author affiliation: 1

University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO 80309, United

States; http://www.me.colorado.edu/

2

University of Michigan, Department of Electrical and Computer Engineering, Ann Arbor, MI 48109, United 

States; http://www.eecs.umich.edu/ece/

3

University of Colorado Boulder, Department of Computer Science, Boulder, CO 80309, United States; 

http://www.colorado.edu/cs/

4

University of Colorado Boulder, Department of Electrical, Computer, and Energy Engineering, Boulder, 

CO 80309, United States; http://ecee.colorado.edu/

Advances in embedded systems and low-cost gas sensors are enabling a new wave of low-cost air quality 

monitoring tools. The authors have been engaged in the development of low-cost wearable air quality 

monitors (M-Pods) using the Arduino platform. The M-Pods use commercially available metal oxide 

semiconductor (MOS) sensors to measure CO, O3, NO2, and total volatile organic compounds, and uses 

nondisperive infrared sensors to measure CO2. MOS sensors are low in cost and show high sensitivity near 

ambient levels; however, they display nonlinear output signals and have cross-sensitivity effects. Thus, a 

quantification system was developed to convert the MOS sensor signals into concentrations. Two 

deployments were conducted at a regulatory monitoring station in Denver, CO. M-Pod concentrations 

were determined using laboratory calibration techniques and co-location calibrations, in which the 

authors placed the M-Pods near regulatory monitors to then derive calibration function coefficients using 

the regulatory monitors as the standard. The form of the calibration function was derived based on 

laboratory experiments. The authors discuss the various techniques used to estimate measurement 

uncertainties. A separate user study was also conducted to assess personal exposure and M-Pod reliability. 

In this study, 10 M-Pods were calibrated via co-location multiple times over 4 weeks and sensor drift was 

analyzed, with the result being a calibration function that included drift. The authors found that colocation calibrations perform better than laboratory calibrations. Laboratory calibrations suffer from bias 

and difficulty in covering the necessary parameter space. During co-location calibrations, median standard 

errors ranged between 4.0–6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for 

CO2. Median signal-to-noise ratios for the M-Pod sensors were higher for M-Pods than for the regulatory 

instruments: for NO2, 3.6 compared to 23.4; for O3, 1.4 compared to 1.6; for CO, 1.1 compared to 10.0; and 

for CO2, 42.2 compared to 300–500.The user study provided trends and location-specific information on 

pollutants and affected change in user behavior. The authors demonstrated the utility of the M-Pod as a 

tool to assess personal exposure.

Air quality data-collection and processing platform

Author(s): Rizea, D. O.;1

 Olteanu, A. C.;1 Tudose, D. S.1

Year: 2014

Source: RoEduNet Conference 13th Edition: Networking in Education and Research Joint Event RENAM 

8th Conference, pp. 1–4

Publisher: IEEE

Author affiliation: 1

Faculty of Computer Science and Engineering, University Politehnica of Bucharest, 060042 Bucharest,

Romania; http://www.upb.ro/en/
20

The degree of pollution in the atmosphere is increasing, leading to more and more people being affected 

by breathing health issues. People are, however, becoming increasingly aware of the need to monitor their 

environment. The authors propose a platform that leverages a wearable sensor device and mobile devices 

to collect large amounts of air quality data on a central real-time data-processing system that can process 

all this information, aggregate it, and disseminate it back to the users in a relevant way. The authors 

designed and developed wearable sensor devices that are capable of measuring CO2, NO, and particulate 

matter. They also designed and developed a mobile application that pulls data from the sensor device in 

an energy-friendly way, as described by their adaptive query algorithm, and pushes it in the central 

processing system. The authors present a real-time processing platform for wireless sensor networks that 

satisfies the characteristics of scalability, fault tolerance, and real-time processing needed to serve as 

their central processing system.

Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments

Author(s): Steinle, Susanne;1

 Reis, Stefan;1 Sabel, Clive E.;2 Semple, Sean;3 Twigg, Marsailidh M.;1 Braban, 

Christine F.;1 Leeson, Sarah R.;1 Heal, Mathew R.;4 Harrison, David;5 Lin, Chun;4 Wu, Hao1,4

Year: 2015

Source: Science of the Total Environment, vol. 508, pp. 383–394

Publisher: Elsevier

Author affiliation: 1

NERC Centre for Ecology and Hydrology (CEH), Midlothian EH26 0QB, Scotland, United Kingdom; 

http://www.ceh.ac.uk/

2

School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, United Kingdom; 

http://www.bristol.ac.uk/geography/

3

Scottish Centre for Indoor Air, Division of Applied Health Sciences, University of Aberdeen, Aberdeen 

AB25 2ZD, United Kingdom; http://www.abdn.ac.uk/iahs/research/respiratory/scia-279.php

4

University of Edinburgh, School of Chemistry, Edinburgh EH9 3FJ, United Kingdom; 

http://www.chem.ed.ac.uk/

5

Bureau Veritas, London E1 8HG, United Kingdom; http://www.bureauveritas.co.uk/

Adverse health effects from exposure to air pollution present a global challenge and are of widespread 

concern. Recent high ambient concentration episodes of air pollutants in European cities highlighted the 

dynamic nature of human exposure and the gaps in data and knowledge about exposure patterns. In 

order to support a health-impact assessment it is essential to develop a better understanding of individual 

exposure pathways in people's everyday lives by taking account of all environments in which people 

spend time. Here the authors describe the development, validation, and results of an exposure method 

applied in a study conducted in Scotland. A low-cost particle counter based on light-scattering 

technology—the Dylos 1700—was used. Its performance was validated in comparison with equivalent 

instruments (TEOM/FDMS) at two national monitoring network sites (R2 = 0.9 at a rural background site, 

R2 = 0.7 at an urban background site). This validation also provided two functions to convert measured 

particle number concentrations (PNCs) into calculated particle mass concentrations for direct comparison 

of concentrations with equivalent monitoring instruments and air quality limit values. This study also used 

contextual and time-based activity data to define six microenvironments (MEs) to assess everyday 

exposure of individuals to short-term PM2.5 concentrations. The Dylos was combined with a Global 

Positioning System receiver to track movement and exposure of individuals across the MEs. Seventeen 

volunteers collected 35 profiles. Profiles may have a different overall duration and structure with respect 

to times spent in different MEs and activities undertaken. Results indicate that, due to the substantial 

variability across and between MEs, it is essential to measure near-complete exposure pathways to allow 

for a comprehensive assessment of the exposure risk that a person encounters on a daily basis. Taking 

into account the information gained through personal exposure measurements, this work demonstrates 

the added value of data generated by the application of low-cost monitors.
21

Stress-sensor classification and stress-analysis algorithms

Author(s): Sysoev, Mikhail;1

 Sedlar, Urban;1 Kos, Andrej;1 Pogačnik, Matevž1

Year: 2014

Source: Elektrotehniški Vestnik, vol. 81, no. 5, pp. 263–271

Publisher: Electrotechnical Society of Slovenia

Author affiliation: 1

Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, 1000

Ljubljana, Slovenia; http://www.ltfe.org/english/about/

A prolonged exposure to stress can lead to mental disorders and psychosomatic diseases (e.g., declining 

of health). In this survey, the authors review sensors, human body parameters, and context information 

correlated with stress level, and investigate various stress analysis methods. Sensors for stress 

determination are classified and divided into four groups with regard to the physiological and physical 

parameters of the human body, context information, expert assessment, and questionnaires. Future work 

to be done in the field of stress recognition is also discussed.

Next generation air measuring research

Author(s): U.S. Environmental Protection Agency Office of Research and Development

Year: 2014

Institution: U.S. Environmental Protection Agency

Traditionally, air pollution is measured by expensive, stationary, and complex air-monitoring instrumentation. 

Only a few organizations, like federal and State governments and some industries, typically collect data 

of such high quality. Even so, this limits the amount of environmental monitoring data that are often 

available for exposure and health assessments. As air quality management problems become more 

complex, there is a need for enhanced air quality and exposure monitoring capabilities.

Temperature measurement systems in wearable electronics

Author(s): Walczak, S.;1

 Gołębiowski, J.2

Year: 2014

Source: Proceedings: SPIE 9291, 13th International Scientific Conference on Optical Sensors and Electronic 

Sensors 

Publisher: SPIE

Author affiliation: 1

Research and Innovation Center Pro-Akademia, 95-050 Konstantynów Łódzki, Poland; 

http://www.proakademia.eu/en/

2

Lodz University of Technology, 90-924 Lodz, Poland; http://www.p.lodz.pl/en

The aim of the authors is to present the concept of a temperature measurement system adapted to 

wearable electronics applications. Temperature is one of the most commonly monitored factors in smart 

textiles, especially in sportswear, medical, and rescue products. Depending on the application, measured 

temperature could be used as an initial value of alert, heating, lifesaving, or analysis systems. The authors 

elaborate on the concept of the temperature measurement multipoint system, which consists of flexible, 

screen-printed resistive sensors placed on a T-shirt connected with the central unit and the power supply.
22

Review of next generation air monitors for air pollution 

Author(s): Wang, Annie;1

 Brauer, Michael1

Year: 2014

Author affiliation: 1

School of Population and Public Health (SPPH), University of British Columbia, Vancouver, 

BC V6T 1Z3, Canada; http://spph.ubc.ca/

Traditional air quality monitoring networks are the foundation for air quality management, policy and 

regulations, population exposure assessments, and health effects research. With increasing awareness of 

spatial variability in air pollution concentrations within cities and the importance of pollution gradients 

from traffic and neighborhood sources, such as residential wood combustion, there is an increasing need 

to also evaluate air pollution variability at local or neighborhood scales. Although passive sampler 

networks, mobile monitoring campaigns, and the application of land use regression and dispersion 

models have provided useful information on air pollution spatial variability, none of the approaches can 

fully characterize both short-term temporal and spatial gradients in pollutant concentrations. Recently, 

there has been a proliferation of low-cost, low-power, miniaturized, autonomous (and typically wireless) 

air quality monitoring units that have the potential to be applied in dynamic dispersed high-resolution air 

quality monitoring networks and to be used in “Citizen Science” applications. In this report, the authors 

review new air quality monitoring technologies and discuss their use in the context of conventional 

monitoring and new applications. The authors summarize information on sensor performance and provide 

recommendations regarding future applications.

Flexible technologies for self-powered wearable health and environmental sensing

Author(s): Misra, V.;1

 Bozkurt, A.;1 Calhoun, B.;2 Jackson, T.;4 Jur, J.;3 Lach, J.;2 Lee, Bongmook;1 Muth, J.;1

Oralkan, O.;1

 Ozturk, M.;1 Trolier-McKinstry, S.;4 Vashaee, D.;1 Wentzloff, D.;5 Zhu, Yong6

Year: 2015

Source: Proceedings of the IEEE, vol. 103, no. 4, pp. 665–681

Publisher: IEEE

Author affiliation: 1

Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC 27695-

7911, United States; http://www.ece.ncsu.edu/ 

2

Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904-

4743, United States; http://www.ece.virginia.edu/

3

College of Textiles, North Carolina State University, Raleigh, NC 27606, United States; 

https://textiles.ncsu.edu/

4

Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 

16802, United States; http://www.matse.psu.edu/

5

Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 

48109-2122, United States; https://www.eecs.umich.edu/

6

Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 

27695-7910, United States; http://www.mae.ncsu.edu/

The authors provide the latest advances from the National Science Foundation’s Advanced Self-powered 

Systems of Integrated Sensors and Technologies (ASSIST) center. The work in the center addresses the 

key challenges in wearable health and environmental systems by exploring technologies that enable 

ultra-long battery lifetime, user comfort and wearability, robust medically validated sensor data with 

value added from multimodal sensing, and access to open architecture data streams. The vision of the 

ASSIST center is to use nanotechnology to build miniature, self-powered, wearable, and wireless sensing 

devices that can enable monitoring of personal health and personal environmental exposure and enable 

correlation of multimodal sensors. These devices can empower patients and doctors to transition from 

managing illness to managing wellness and create a paradigm shift in improving healthcare outcomes. 
23

The authors present the latest advances in high-efficiency nanostructured energy harvesters and storage 

capacitors; new sensing modalities that consume less power; low-power computation and communication 

strategies; and novel flexible materials that provide form, function, and comfort. These technologies span 

a spatial scale ranging from underlying materials at the nano scale to body-worn structures, and the 

challenge is to integrate them into a unified device designed to revolutionize wearable health applications.

Reference Results

Transport

Wearable technologies for automotive user interfaces: Danger or opportunity?

Author(s): Caon, Maurizio;1 Tagliabue, Michele;1 Angelini, Leonardo;1 Perego, Paolo;2

 Mugellini, Elena;1 

Andreoni, Giuseppe1

Year: 2014

Source: Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and 

Interactive Vehicular Applications. AutomotiveUI '14, pp. 1–5

Publisher: Association for Computing Machinery

Author affiliation: 1

Technology for Human Wellbeing Institute (HumanTech), School of Engineering and Architecture of

Fribourg, 1705 Fribourg, Switzerland; http://humantech.eia-fr.ch/Pages/Accueil.aspx

2

Technology and Design for Health, Politecnico di Milano, CAP 20158, Milan, Italy; 

http://www.tedh.polimi.it/index.php?lang=it

Wearable technologies are spreading into the everyday lives of human beings. Smart bracelets, watches, 

and glasses promise to provide us with ready-at-hand access to several information and interaction 

possibilities. What happens when these technologies enter the car? Are they a mere danger or can they 

also offer information and interaction opportunities that can improve the vehicle inhabitants’ user 

experience and safety? The authors aim to discuss all the opportunities and dangers that such technologies 

not only are already bringing to current vehicles but could also bring to future semiautonomous and 

autonomous vehicles.

Connecting personal-scale sensing and networked community behavior to infer human activities

Author(s): Lane, Nicholas D.;1

 Pengyu, Li;2 Zhou, Lin;3 Zhao, Feng1

Year: 2014

Source: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous 

Computing, pp. 595–606

Publisher: Association for Computing Machinery

Author affiliation: 1

Microsoft Research Asia, Beijing 100080, China; http://research.microsoft.com/en-us/labs/asia/

2

Department of Mathematics, Beijing University of Posts and Telecommunications, Beijing 100876, China; 

http://english.bupt.edu.cn/ 

3

Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106-5110, 

United States; https://www.cs.ucsb.edu/

Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a largescale. Slower progress is being made, however, in activity recognition, which remains often unreliable in 

everyday environments. The authors investigate how to leverage the increasing capacity to gather data 

at a population-scale toward improving existing models of human behavior. The authors specifically 

consider the various social phenomena and environmental factors that cause people to develop correlated 

behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning 
24

correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions), which 

shape options and decisions; and cases of adopted behavior, as people learn from each other or assume 

group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of 

these phenomena and recognize activities solely on the basis of sensor data captured from a single 

individual. The authors propose the Networked Community Behavior (NCB) framework for activity 

recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns 

of community behavior are mined to identify social ties that can signal correlated behavior; this information 

is used to augment sensor-based inferences available from the actions of individuals. The authors' 

evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four 

mobile sensing datasets that collectively require a diverse set of activities to be recognized.

Leveraging directional antenna capabilities for fine-grained gesture recognition 

Author(s): Melgarejo, Pedro;1 Zhang, Xinyu;1

 Ramanathan, Parameswaran;1 Chu, David2

Year: 2014

Source: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous 

Computing, pp. 541–551

Publisher: Association for Computing Machinery

Author affiliation: 1

Department of Electrical and Computer Engineering, University of Wisconsin—Madison, Madison, WI 

53706-1691, United States; http://www.engr.wisc.edu/ece.html

2

Microsoft Research, Redmond, WA 98052, United States; http://research.microsoft.com/en-us/default.aspx

The authors present a recognition scheme for fine-grain gestures. The scheme leverages a directional 

antenna and short-range wireless propagation properties to recognize a vocabulary of action-oriented 

gestures from American Sign Language. Because the scheme only relies on commonly available wireless 

features such as Received Signal Strength (RSS), signal phase differences, and frequency sub-band 

selection, it is readily deployable on commercial off-the-shelf IEEE 802.11 devices. The authors have 

implemented the proposed scheme and evaluated it in two potential application scenarios: gesturebased electronic activation from a wheelchair and gesture-based control of a car infotainment system. 

The results show that the proposed scheme can correctly identify and classify up to 25 fine-grain 

gestures with an average accuracy of 92 percent for the first application scenario and 84 percent for 

the second scenario.

Detecting smoothness of pedestrian flows by participatory sensing with mobile phones

Author(s): Nishimura, Tomohiro;1

 Higuchi, Takamasa;1 Yamaguchi, Hirozumi;1 Higashino, Teruo1

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers. ISWC '14, pp. 15–18

Publisher: Association for Computing Machinery

Author affiliation: 1

Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871 Japan; 

http://www.ist.osaka-u.ac.jp/english/

The authors propose a novel system for estimating crowd density and smoothness of pedestrian flows in 

public space by participatory sensing with mobile phones. By analyzing the walking motion of pedestrians 

and ambient sound in the environment that can be monitored by accelerometers and microphones in offthe-shelf smartphones, their system classifies the current situation at each area into four categories that 

well-represent the crowd behavior. Through field experiments using Android smartphones, the authors 

show that their system can recognize the current situation with accuracy of 60—78 percent.
25

Assessment of driver stress from physiological signals collected under real-time semi-urban 

driving scenarios

Author(s): Singh, Rajiv Ranjan;1

 Conjeti, Sailesh;2 Banerjee, Rahul3

Year: 2014

Source: International Journal of Computational Intelligence Systems, vol. 7, no. 5, pp. 909–923

Publisher: Taylor and Francis

Author affiliation: 1

Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, 

Pilani, Rajasthan 333031, India; http://www.bits-pilani.ac.in/pilani/ElectricalElectronicsInstrumentation/Home

2

School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal 

721302, India; http://www.smstweb.iitkgp.ernet.in/

3

Department of Computer Science, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan 

333031, India; http://www.bits-pilani.ac.in/pilani/computerscience/ComputerScience

Designing a wearable driver-assist system requires extraction of relevant features from physiological 

signals, such as galvanic skin response and photoplethysmograms collected from automotive drivers 

during real-time driving. In the discussed case, four stress classes were identified using a cascade-forward 

neural network (CASFNN), which performed consistently with minimal intra- and inter-subject variability. 

Task-induced stress trends were tracked using a Trigg’s Tracking Variable-based regression model with 

CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation 

procedures during accidents and emergencies.

Reference Results

Physiology

Automatic activity classification and movement assessment during a sports training session using 

wearable inertial sensors

Author(s): Ahmadi, A.;1

 Mitchell, E.1; Destelle, F.;1 Gowing, M.;1 O'Connor, N. E.;1 Richter, C.;2 Moran, K.2

Year: 2014

Source: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 

pp. 98–103

Publisher: IEEE

Author affiliation: 1

Insight Centre for Data Analytics, Dublin City University, Belfield, Dublin 04, Ireland; 

https://www.insight-centre.org/

2

School of Health and Human Performance, Dublin City University, Glasnevin, Dublin 9, Ireland; 

http://www4.dcu.ie/shhp/index.shtml

Motion analysis technologies have been widely used to monitor the potential for injury and enhance 

athlete performance. Most of these technologies, however, are expensive, can only be used in laboratory 

environments, and examine only a few trials of each movement action. The authors present a novel 

ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an 

athlete’s activities in an outdoor training environment. The authors first present a system that automatically 

classifies a large range of training activities using the discrete wavelet transform in conjunction with a 

random forest classifier. The classifier is capable of successfully classifying various activities with up to 98 

percent accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the 

relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from 

which the flexion–extension knee angle is calculated. Finally, a curve shift registration technique is applied 

to both generate normative data and determine if a subject's movement technique differed to the 
26

normative data in order to identify potential injury-related factors. It is envisaged that the proposed 

framework could be utilized for accurate and automatic sports activity classification and reliable 

movement technique evaluation in various unconstrained environments.

Noninvasive wearable electrochemical sensors: A review

Author(s): Bandodkar, Amay J.;1

 Wang, Joseph1

Year: 2014

Source: Trends in Biotechnology, vol. 32, no. 7, pp. 363–371

Publisher: Elsevier

Author affiliation: 1

Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093-0448, United 

States; http://nanoengineering.ucsd.edu/

Wearable sensors have garnered considerable recent interest owing to their tremendous promise for a 

plethora of applications; yet the absence of reliable noninvasive chemical sensors has greatly hindered 

progress in the area of on-body sensing. Electrochemical sensors offer considerable promise as wearable 

chemical sensors that are suitable for diverse applications owing to their high performance, inherent 

miniaturization, and low cost. A wide range of wearable electrochemical sensors and biosensors has been 

developed for real-time, noninvasive monitoring of electrolytes and metabolites in sweat, tears, or saliva 

as indicators of a wearer's health status. With continued innovation and attention to key challenges, such 

noninvasive electrochemical sensors and biosensors are expected to open up new exciting avenues in the 

field of wearable wireless sensing devices and body-sensor networks, and thus find considerable use in a 

wide range of personal healthcare monitoring applications, as well as in sport and military applications.

PhysioDroid: Combining wearable health sensors and mobile devices for a ubiquitous, continuous, and 

personal monitoring

Author(s): Banos, Oresti;1

 Villalonga, Claudia;2 Damas, Miguel;1 Gloesekoetter, Peter;3 Pomares, Hector;1

Rojas, Ignacio1

Year: 2014

Source: The Scientific World Journal, vol. 2014

Publisher: Hindawi Publishing Corporation

Author affiliation: 1

Department of Computer Architecture and Computer Technology, Research Center for Information and 

Communications Technologies, University of Granada, 18014 Granada, Spain; http://citic.ugr.es/?lang=en

2

CGI Information Systems and Management Consultants España, 28050 Madrid, Spain; 

http://www.cgiespana.es/

3

Department of Electrical Engineering and Computer Sciences, Münster University of Applied Sciences, 

48565 Steinfurt, Germany; https://en.fh-muenster.de/hochschule/index.php

Technological advances on the development of mobile devices, medical sensors, and wireless 

communication systems support a new generation of unobtrusive, portable, and ubiquitous healthmonitoring systems for continuous patient assessment and more personalized health care. There exists a 

growing number of mobile apps in the health domain; however, little contribution has been specifically 

provided so far to operate these kinds of apps with wearable physiological sensors. The PhysioDroid 

presented in this paper provides a personalized means to remotely monitor and evaluate users’ conditions. 

The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, 

heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and 

improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an 

Android app that provides gathering, storage, and processing features for the physiological sensor data. 

The versatility of the developed app allows its use for both average users and specialists, and the reduced 
27

cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment 

and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps 

include generalization to other mobile platforms and health monitoring devices.

Toward EMG control interface for smart garments 

Author(s): Benatti, Simone;1

 Farella, Elisabetta;2 Benini, Luca1

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers: Adjunct 

Program. ISWC '14 Adjunct, pp. 163–170

Publisher: Association for Computing Machinery 

Author affiliation: 1

Department of Electrical, Electronic, and Information Engineering, University of Bologna, 40126 Bologna, 

Italy; http://www.dei.unibo.it/en

2

Center for Information and Communication Technology (FBK ICT Center), I-38123 Povo, Trento, Italy; 

http://ict.fbk.eu/

Wearable computing devices can greatly enhance the quality of life, aiding interaction with smart 

environments, activity recognition, and healthcare applications. Smart garments offer the opportunity to 

integrate sensors and electronics in unobtrusive wearable systems. The authors present a case study of 

an embedded hand-gesture–recognition system, which uses electromyography electrodes embeddable 

in smart clothes. The authors analyze the main challenges of a real-time system for pattern recognition, 

and the results of the proposed experiment demonstrate the feasibility of a real-time system for pattern 

recognition, which can be integrated in smart clothes.

Development of a smartphone application to measure physical activity using sensor-assisted selfreport

Author(s): Dunton, Genevieve Fridlund;1

 Dzubur, Eldin;1 Kawabata, Keito;1 Yanez, Brenda;1 Bo, Bin;2 Intille, Stephen3

Year: 2014

Source: Frontiers in Public Health, vol. 2

Publisher: Frontiers Media SA

Author affiliation: 1

Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90032, United 

States; https://pm.usc.edu/

2

Department of Psychology, University of Southern California, Los Angeles, CA 90089-1061, United States; 

https://dornsife.usc.edu/psyc/

3

College of Computer and Information Science, Northeastern University, Boston, MA 02115, United States; 

http://www.ccs.neu.edu/

Despite the known advantages of objective physical activity monitors (e.g., accelerometers), these 

devices have high rates of nonwear, which lead to missing data. Objective activity monitors are also 

unable to capture valuable contextual information about behavior. Adolescents recruited into physical 

activity surveillance and intervention studies will increasingly have smartphones, which are miniature 

computers with built-in motion sensors. The authors describe the design and development of a smartphone 

application (“app”) called Mobile Teen that combines objective and self-report assessment strategies 

through (1) sensor-informed context-sensitive ecological momentary assessment (CS-EMA) and (2) 

sensor-assisted end-of-day recall. The Mobile Teen app uses the smartphone’s built-in motion sensor to 

automatically detect likely bouts of phone nonwear, sedentary behavior, and physical activity. The app 

then uses transitions between these inferred states to trigger CS-EMA self-report surveys measuring the 

type, purpose, and context of activity in real-time. The end-of-day recall component of the Mobile Teen 

app allows users to interactively review and label their own physical activity data each evening using 
28

visual cues from automatically detected major activity transitions from the phone’s built-in motion 

sensors. Major activity transitions are identified by the app, which cues the user to label that “chunk,” or 

period, of time using activity categories. Sensor-driven CS-EMA and end-of-day recall smartphone apps 

can be used to augment physical activity data collected by objective activity monitors, filling in gaps 

during nonwear bouts and providing additional real-time data on environmental, social, and emotional 

correlates of behavior. Smartphone apps such as these have potential for affordable deployment in largescale epidemiological and intervention studies.

A framework for collaborative computing and multisensor data fusion in body sensor networks

Author(s): Fortino, Giancarlo;1

 Galzarano, Stefano;1 Gravina, Raffaele;1 Li, Wenfeng2

Year: 2015

Source: Information Fusion, vol. 22, pp. 50–70

Publisher: Elsevier

Author affiliation: 1

Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, CS, 

Italy; http://www.dimes.unical.it/

2

School of Logistics Engineering, Wuhan University of Technology, 430063 Wuhan, China; 

http://english.whut.edu.cn/ or http://www.whut.edu.cn/2015web/index.htm

Body sensor networks (BSNs) have emerged as the most effective technology that enables not only new 

e-Health methods and systems but also novel applications in human-centered areas such as electronic 

health care, fitness/wellness systems, sport performance monitoring, interactive games, factory worker 

monitoring, and social–physical interaction. Despite their enormous potential, they are currently mostly 

used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster 

novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework 

for collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill 

a common goal. They can support the development of novel smart wearable systems for cyberphysical 

pervasive computing environments. Collaboration, therefore, relies on interaction and synchronization 

among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, 

collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for 

managing services among the collaborating CBSNs. C-SPINE also natively supports multisensor data 

fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration, and 

classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative 

CBSN system for the detection of emotions. The system is based on a multisensor data fusion schema to 

perform automatic detection of handshakes between two individuals and capture of possible heart-rate–

based emotional reactions due to the individuals’ meeting. 

The manumeter: A wearable device for monitoring daily use of the wrist and fingers

Author(s): Friedman, N.;1

 Rowe, J. B.;1 Reinkensmeyer, D. J.;2 Bachman, M.3

Year: 2014

Source: IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 1804–1812

Publisher: IEEE

Author affiliation: 1

Department of Biomedical Engineering, University of California, Irvine, Irvine, CA 92697, United States; 

https://www.eng.uci.edu/dept/bme

2

Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA 92697, 

United States; http://engineering.uci.edu/dept/mae

3

Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA 

92697, United States; http://engineering.uci.edu/dept/eecs/contact-us

Nonobtrusive options for monitoring the wrist and hand movement are needed for stroke rehabilitation 
29

and other applications. The authors describe the “manumeter,” a device that logs total angular distance 

travelled by wrist and finger joints using a magnetic ring worn on the index finger and two triaxial 

magnetometers mounted in a watch-like unit. The authors describe an approach to estimate the wrist and 

finger joint angles using a radial basis function network that maps differential magnetometer readings to 

joint angles. They tested this approach by comparing manumeter estimates of total angular excursion 

with those from a passive goniometric exoskeleton worn simultaneously as seven participants completed 

a set of 12 manual tasks at low-, medium-, and high-intensity conditions on a first testing day, 1–2 days 

later, and 6–8 days later, using only the original calibration from the first testing day. Manumeter estimates 

scaled proportionally to the intensity of hand activity. Estimates of angular excursion made with the 

manumeter were 92.5 percent ± 28.4 (standard deviation), 98.3 percent ± 23.3, and 94.7 percent ± 19.3 of 

the goniometric exoskeleton across the three testing days, respectively. Magnetic sensing of wrist and 

finger movement is nonobtrusive and can quantify the amount of use of the hand across days.

Garment-integrated bend sensor

Author(s): Gioberto, Guido;1

 Dunne, Lucy E.2

Year: 2014

Source: Electronics, vol. 3, no. 4, pp. 564–581

Publisher: MDPI AG

Author affiliation: 1

College of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, United 

States; https://www.cs.umn.edu/

2

College of Design, University of Minnesota, St. Paul, MN 55108, United States; http://design.umn.edu/

Garment-integrated sensors equip clothes with a smart-sensing capability while preserving the comfort 

of the user. This benefit can, however, be to the detriment of sensing accuracy due to the unpredictability 

of garment movement (which affects sensor positioning) and textile folds (which can affect sensor 

orientation). Sensors integrated directly into garments or fabric structures can also be used to detect the 

movement of the garment during wearing. In particular, a textile-bend sensor could be used to sense 

folds in the garment. The authors tested a garment-integrated stitched sensor for five types of folds, 

stitched on five different weights of unstretchable denim fabric, and analyzed the effects of fold complexity 

and fabric stiffness under uninsulated and insulated conditions. Results show that insulation improves the 

linearity and repeatability of the sensor response, particularly for higher fold complexity. Stiffer fabrics 

show greater sensitivity, but less linearity. Sensor response amplitude is larger for more complex fold 

geometries. The authors discuss the utility of a linear bending response (insulated) and a binary shorting 

response (uninsulated). Overall, the sensor exhibits excellent repeatability and accuracy, particularly for a 

fiber-based, textile-integrated sensor.

Multimodal physiological sensor for motion artifact rejection

Author(s): Goverdovsky, V.;1

 Looney, D.;2 Kidmose, P.;3 Mandic, D. P.1

Year: 2014

Source: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology 

Society (EMBC), pp. 2753–2756

Publisher: IEEE

Author affiliation: 1

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United 

Kingdom; http://www3.imperial.ac.uk/electricalengineering

2

Department of Kinesiology, University of Connecticut, Storrs, CT 06269, United States; 

http://kins.uconn.edu/

3

Department of Engineering—Biomedical Engineering, Aarhus University, 8200 Aarhus N, Denmark; 

http://eng.au.dk/en/
30

The authors introduce a novel physiological sensor, which combines electrical and mechanical modalities 

in a co-located arrangement, to reject motion-induced artifacts. The mechanically sensitive element 

consists of an electret condenser microphone containing a light diaphragm, allowing it to detect local 

mechanical displacements and disregard large-scale whole-body movements. The electrically sensitive 

element comprises a highly flexible membrane, conductive on one side and insulating on the other. It 

covers the sound hole of the microphone, thereby forming an isolated pocket of air between the membrane 

and the diaphragm. The co-located arrangement of the modalities allows the microphone to sense 

mechanical disturbances directly through the electrode, thus providing an accurate proxy to artifacts 

caused by relative motion between the skin and the electrode. This proxy is used to reject such artifacts 

in the electrical physiological signals, enabling enhanced recording quality in wearable health applications.

Ballet Hero: Building a garment for memetic embodiment in dance learning

Author(s): Hallam, James;1

 Keen, Emily;1 Lee, Christa;1 McKenna, Alison;1 Gupta, Mudit1

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers: Adjunct 

Program. ISWC '14 Adjunct, pp. 49–54

Publisher: Association for Computing Machinery

Author affiliation: 1

School of Industrial Design, Georgia Institute of Technology, Atlanta, GA 30332-0155, United States; 

http://www.id.gatech.edu/research/labs

The authors describe the analysis and design of a wearable technology garment intended to aid with the 

instruction of ballet technique to adult beginners. A phenomenological framework is developed and used 

to assess physiological training tools. Following this, a garment is developed that incorporates visual 

feedback inspired by animation techniques that more directly convey the essential movements of ballet. 

The authors present the garment design and a discussion is provided on the challenges of constructing 

an e-textile garment using contemporary materials and techniques.

Enhancing action recognition through simultaneous semantic mapping from body-worn motion sensors

Author(s): Hardegger, Michael;1

 Nguyen-Dinh, Long-Van;1 Calatroni, Alberto;1 Roggen, Daniel;2 Tröster, Gerhard1

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers. ISWC '14, pp. 

99–106

Publisher: Association for Computing Machinery

Author affiliation: 1

Wearable Computing Laboratory, ETH Zurich, 8092 Zurich Switzerland; http://www.ife.ee.ethz.ch/

2

Department of Engineering and Design, University of Sussex, Brighton BN1 9QJ, United Kingdom; 

http://www.sussex.ac.uk/engineering/

Locations and actions are interrelated: some activities tend to occur at specific places. For example, a 

person is more likely to twist his wrist when he is close to a door (to turn the knob). The authors present 

an unsupervised fusion method that takes advantage of this characteristic to enhance the recognition of 

location-related actions (e.g., open, close, switch, etc.). The proposed LocAFusion algorithm acts as a 

post-processing filter: At run-time, it constructs a semantic map of the environment by tagging action 

recognitions to Cartesian coordinates. It then uses the accumulated information about a location to (1) 

discriminate between identical actions performed at different places and (2) correct recognitions that are 

unlikely, given the other observations at the same location. LocAFusion does not require prior statistics 

about where activities occur, which allows for seamless deployment to new environments. The fusion 
31

approach is agnostic to the sensor modalities and methods used for action recognition and localization. 

For evaluation, the authors implemented a fully wearable setup that tracks the user with a foot-mounted 

motion sensor and the ActionSLAM algorithm. At the same time, the authors recognize hand actions 

through template matching on the data of a wrist-worn inertial measurement unit. In 10 recordings with 

554 performed object interactions, LocAFusion consistently outperformed location-independent action 

recognition (8 percent to 31 percent increase in F1 score), identified 96 percent of the objects in the 

semantic map, and overall correctly labeled 82 percent of the actions in problems with up to 23 classes.

Including affect-driven adaptation to the Pac-Man video game

Author(s): Harris, Ahbiya;1

 Hoch, Andrew;1 Kral, Ryan;1 Teposte, Michael;1 Villa, Alyza;1 Chavez-Echeagaray, 

Maria Elena;1

 Gonzalez-Sanchez, Javier;1 Atkinson, Robert K.1

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers: Adjunct 

Program. ISWC '14 Adjunct, pp. 1–4

Publisher: Association for Computing Machinery

Author affiliation: 1

Advancing Next Generation Learning Environments (ANGLE) Lab, Arizona State University, Tempe, AZ 

85281, United States; http://angle.lab.asu.edu/site/

Building affect-driven adaptive environments is a task geared toward creating environments able to 

change based on the affective state of a target user. In this project, the environment is the well-known 

game, Pac-Man. To provide affect-driven adaptive capabilities, the authors utilized diverse sensors to 

gather a user's physiological data, and an emotion recognition framework was used to fuse the sensed 

data and infer affective states. The game-changes driven by those affective states aim to improve the 

user experience by keeping or increasing player's engagement.

Using electrodermal activity to recognize ease of engagement in children during social interactions

Author(s): Hernandez, Javier;1

 Riobo, Ivan;2 Rozga, Agata;2 Abowd, Gregory D.;2 Picard, Rosalind W.1

Year: 2014

Source: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous 

Computing. UbiComp '14, pp. 307–317

Publisher: Association for Computing Machinery

Author affiliation: 1

MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, United States; 

http://www.media.mit.edu/

2

Ubiquitous Computing Group, Georgia Institute of Technology School of Interactive Computing, Atlanta, 

GA 30332-0760, United States; http://www.ic.gatech.edu/

The recent emergence of comfortable wearable sensors has focused almost entirely on monitoring 

physical activity, ignoring opportunities to monitor more subtle phenomena, such as the quality of social 

interactions. The authors argue that it is compelling to address whether physiological sensors can shed 

light on the quality of social interactive behavior. This work leverages the use of a wearable electrodermal 

activity (EDA) sensor to recognize ease of engagement of children during a social interaction with an 

adult. In particular, the authors monitored 51 child–adult dyads in a semistructured play interaction and 

used support vector machines to automatically identify children who had been rated by the adult as more 

or less difficult to engage. The authors report on the classification value of several features extracted from 

each child’s EDA responses, as well as several other features that capture the physiological synchrony 

between the child and the adult.
32

Monitoring activities of daily living based on a wearable wireless body sensor network

Author(s): Kantoch, E.;1

 Augustyniak, P.;1 Markiewicz, M.;2 Prusak, D.1

Year: 2014

Source: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology

Society (EMBC), pp. 586–589

Publisher: IEEE

Author affiliation: 1

AGH University of Science and Technology, 30-059 Krakow, Poland; http://www.agh.edu.pl/en#

2

Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, 30-348 Krakow, 

Poland; http://www.fais.uj.edu.pl/start-en

With recent advances in microprocessor chip technology, wireless communication, and biomedical 

engineering, it is possible to develop miniaturized ubiquitous health monitoring devices that are capable 

of recording physiological and movement signals during daily life activities. The aim of this research is to 

implement and test the prototype of a health monitoring system. The system consists of the body central 

unit with a Bluetooth® module and wearable sensors, the custom-designed electrocardiogram sensor, the 

temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated 

with clothes, and a network gateway to forward data to a remote medical server. The system includes a 

custom-designed transmission protocol and remote Web-based graphical user interface for remote realtime data analysis. Experimental results for a group of humans who performed various activities (e.g., 

working, running, etc.) showed maximum 5 percent absolute error compared to certified medical devices. 

The results are promising and indicate that a developed wireless wearable monitoring system faces 

challenges of multisensor human health monitoring while performing daily activities and opens new 

opportunities in developing novel healthcare services.

Technologies to measure and modify physical activity and eating environments 

Author(s): King, Abby C.;1

 Glanz, Karen;2 Patrick, Kevin3

Year: 2015

Source: American Journal of Preventive Medicine, vol. 48, no. 5, pp. 630–638

Publisher: Elsevier

Author affiliation: 1

Department of Health Research and Policy and Stanford Prevention Research Center, Department of 

Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, United States; 

http://med.stanford.edu/hrp.html and http://prevention.stanford.edu/

2

Perelman School of Medicine and School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, 

United States; http://www.med.upenn.edu/ and http://www.nursing.upenn.edu/Pages/default.aspx

3

Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and 

Calit2's Qualcomm Institute, University of California, San Diego, La Jolla, CA 92093-0811, United States; 

http://cwphs.ucsd.edu/ and http://www.calit2.net/

The explosion of technologic advances in information capture and delivery offer unparalleled opportunities 

to assess and modify built and social environments in ways that can positively impact health behaviors. 

This paper highlights some potentially transformative current and emerging trends in the technology 

arena that are applicable to environmental context-based assessment and intervention relevant to 

physical activity and dietary behaviors. A team of experts convened in 2013 to discuss the main issues 

related to technology-use in assessing and changing built environments for health behaviors, particularly 

those relevant to obesity prevention. Each expert was assigned a specific domain to describe, 
33

commensurate with his or her research and expertise in the field, along with examples of specific 

applications. This activity was accompanied by selective examination of published literature to cover the 

main issues and elucidate relevant applications of technologic tools and innovations in this field. Decisions 

concerning which technology examples to highlight were reached through discussion and consensusbuilding among the team of experts. Two levels of impact are highlighted: the “me” domain, which 

primarily targets measurement and intervention activities aimed at individual-level behaviors and their 

surrounding environments; and the “we” domain, which generally focuses on aggregated data aimed at 

groups and larger population segments and locales. The authors conclude the paper with a set of 

challenges and opportunities for significantly advancing the field. Key areas for progress include data 

collection and expansion, managing technologic considerations, and working across sectors to maximize 

the population potential of behavioral health technologies.

Effect of time–activity adjustment on exposure assessment for traffic-related ultrafine particles 

Author(s): Lane, Kevin J.;1, 2 Levy, Jonathan I.;2

 Scammell, Madeleine Kangsen;2 Patton, Allison P.;3 Durant, 

John L.;3 Mwamburi, Mkaya;4 Zamore, Wig;5 Brugge, Doug4

Publisher: Macmillan

Year: 2015

Source: Journal of Exposure Science and Environmental Epidemiology, vol. 25, no. 5, pp. 506–516

Author affiliation: 1

Yale School of Forestry and Environmental Studies, New Haven, CT 06511, United States; 

http://environment.yale.edu/

2

Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, 

United States; http://www.bu.edu/sph/academics/departments/environmental-health/

3

Department of Civil and Environmental Engineering, Tufts University, Medford, MA 02155, United States; 

http://engineering.tufts.edu/cee/

4

Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 

02111, United States; 

http://medicine.tufts.edu/Education/Academic-Departments/Clinical-Departments/Public-Health-andCommunity-Medicine

5

Somerville Transportation Equity Partnership, Somerville, Massachusetts, MA, United States;

http://www.somervillestep.org/

Exposures to ultrafine particles (<100 nm, estimated as particle number concentration (PNC)) differ from 

ambient concentrations because of the spatial and temporal variability of both PNC and people. The goal 

of this research was to evaluate the influence of time–activity adjustment on exposure assignment and 

associations with blood biomarkers for a near-highway population. A regression model based on mobile 

monitoring and spatial and temporal variables was used to generate hourly ambient residential PNC for a 

full year for a subset of participants (n=140) in the Community Assessment of Freeway Exposure and 

Health study. The authors modified the ambient estimates for each hour using personal estimates of 

hourly time spent in five microenvironments (inside home, outside home, at work, commuting, other) as 

well as particle infiltration. Time–activity adjusted (TAA)-PNC values differed from residential ambient 

annual average (RAA)-PNC, with lower exposures predicted for participants who spent more time away 

from home. Employment status and distance to highway had a differential effect on TAA-PNC. The 

authors found associations of RAA-PNC with high sensitivity C-reactive protein and interleukin-6, although 

exposure–response functions were nonmonotonic. TAA-PNC associations had larger effect estimates and 

linear exposure–response functions. The authors’ findings suggest that time–activity adjustment improves 

exposure assessment for air pollutants that vary greatly in space and time.
34

Wearable sensors for human activity monitoring: A review

Author(s): Mukhopadhyay, S. C.1

Year: 2015

Source: IEEE Sensors Journal, vol. 15, no. 3, pp. 1321–1330

Publisher: IEEE

Author affiliation: 1

School of Engineering and Advanced Technology, Massey University, Palmerston North 4442, New 

Zealand; 

http://www.massey.ac.nz/massey/learning/colleges/college-of-sciences/about/engineering-technology/

engineering-and-advanced-technology-about.cfm

An increase in world population, along with a significant aging portion, is forcing rapid rises in healthcare 

costs. The healthcare system is going through a transformation in which continuous monitoring of 

inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded 

systems, wireless communication technologies, nanotechnologies, and miniaturization makes it possible 

to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect 

abnormal and/or unforeseen situations by monitoring physiological parameters along with other 

symptoms; therefore, necessary help can be provided in times of dire need. The author reviews the latest 

reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed 

to tackle the challenges.

Wearable silver nanowire dry electrodes for electrophysiological sensing

Author(s): Myers, Amanda C.;1

 Huang, He;2 Zhu, Yong1

Year: 2015

Source: RSC Advances, vol. 5, no. 15, pp. 11627–11632

Publisher: Royal Society of Chemistry

Author affiliation: 1

Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 

27695-7910, United States; http://www.mae.ncsu.edu/

2

College of Engineering, North Carolina State University, Raleigh, NC 27695, United States; 

http://www.engr.ncsu.edu/

The authors present wearable dry electrodes made of silver nanowires for electrophysiological sensing, such 

as electrocardiography and electromyography. The dry electrodes perform as well as the Ag/AgCl wet 

electrodes when the subject is resting and show fewer motion artifacts, but without the electrolytic gel. The 

nanowire electrodes show no signs of skin irritation, which is desirable for long-term health monitoring.

Are we there yet? Feasibility of continuous stress assessment via wireless physiological sensors

Author(s): Rahman, Md. Mahbubur;1

 Bari, Rummana;2 Ali, Amin Ahsan;1 Sharmin, Moushumi;1 Raij, Andrew;1

Hovsepian, Karen;2

 Hossain, Syed Monowar;1 Ertin, Emre;2 Kennedy, Ashley;3 Epstein, David H.;3 Preston, Kenzie 

L.;3 Jobes, Michelle;3 Beck, J. Gayle;4 Kedia, Satish;5 Ward, Kenneth D.;5 al'Absi, Mustafa;2 Kumar, Santosh1

Year: 2014

Source: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health 

Informatics. BCB '14, pp. 479–488

Publisher: Association for Computing Machinery

Author affiliation: 1

Department of Computer Science, University of Memphis, Memphis, TN 38152-3240, United States; 

http://www.memphis.edu/cs/
35

2

MD2K Center, University of Memphis, Memphis, TN 38152, United States; https://md2k.org/

3

National Institute on Drug Abuse Intramural Research Program, National Institutes of Health, Baltimore, 

MD 21224, United States; http://irp.drugabuse.gov/index.php

4

Department of Psychology, University of Memphis, Memphis, TN 38152-3230, United States; 

http://www.memphis.edu/psychology/

5

Department of Social and Behavioral Sciences, School of Public Health, University of Memphis, Memphis, 

TN 38152, United States; http://www.memphis.edu/sph/

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug 

use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can 

be used for timely delivery of a variety of interventions to reduce or avoid stress. The authors investigate 

the feasibility of continuous stress measurement via two field studies using wireless physiological 

sensors—a 4-week study with illicit drug users (n = 40), and a 1-week study with daily smokers and social 

drinkers (n = 30). The authors find that 11+ hours/day of usable data can be obtained in a 4-week study. 

Significant learning effect is observed after the first week and data yield is seen to be increasing over time 

even in the fourth week. The authors propose a framework to analyze sensor data yield and find that 

losses in wireless channel are negligible; the main hurdle in further improving data yield is the attachment 

constraint. The authors show the feasibility of measuring stress minutes preceding events of interest and 

observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.

Wearable accelerometry-based technology capable of assessing functional activities in neurological 

populations in community settings: A systematic review

Author(s): Steins, Dax1

; Dawes, Helen;1 Esser, Patrick;1 Collett, Johnny1

Year: 2014

Source: Journal of Neuroengineering and Rehabilitation, vol. 11, no. 1

Publisher: BioMed Central

Author affiliation: 1

Movement Science Group, Department of Sport and Health Sciences, Oxford Brookes University, Oxford 

OX3 0BP, United Kingdom; http://www.shs.brookes.ac.uk/research/movement-science

Integrating rehabilitation services through wearable systems has the potential to accurately assess the 

type, intensity, duration, and quality of movement necessary for procuring key outcome measures. The 

authors aim to explore wearable accelerometry-based technology (ABT) capable of assessing mobilityrelated functional activities intended for rehabilitation purposes in community settings for neurological 

populations. In this review, the authors focus on the accuracy of ABT-based methods, types of outcome 

measures, and the implementation of ABT in nonclinical settings for rehabilitation purposes. The search 

strategy covered three main areas, namely wearable technology, rehabilitation, and setting. Potentially 

relevant studies were categorized as systems that either evaluate methods or outcome parameters. 

Methodological qualities of the studies were assessed by two customized checklists, depending on their 

categorization, and rated independently by three blinded reviewers. Twelve studies involving ABT met 

the eligibility criteria, of which three studies were identified as having implemented ABT for rehabilitation 

purposes in nonclinical settings. From the twelve studies, seven studies achieved high methodological 

quality scores. These studies were not only capable of assessing the type, quantity, and quality measures 

of functional activities, but could also distinguish healthy from nonhealthy subjects and/or address 

disease severity levels. Although many studies support ABT’s potential for telerehabilitation, few actually 

utilized it to assess mobility-related functional activities outside laboratory settings. To generate more 

appropriate outcome measures, there is a clear need to translate research findings and novel methods 

into practice.
36

Reference Results

Modeling

Mining multivariate time series with mixed sampling rates

Author(s): Cachucho, Ricardo;1

 Meeng, Marvin;1 Vespier, Ugo;1 Nijssen, Siegfried;1 Knobbe, Arno1

Year: 2014

Source: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous 

Computing. UbiComp '14, pp. 413–423

Publisher: Association for Computing Machinery

Author affiliation: 1

Leiden Institute of Advanced Computer Science (LIACS), Leiden University, 2300 RA Leiden, Netherlands; 

http://www.liacs.nl/

Fitting sensors to humans and physical structures is becoming more and more common. These 

developments provide many opportunities for ubiquitous computing, as well as challenges for analyzing 

the resulting sensor data. From these challenges, an underappreciated problem arises: modeling 

multivariate time series with mixed sampling rates. Although mentioned in several application papers 

using sensor systems, this problem has been left almost unexplored, often hidden in a preprocessing step 

or solved manually as a one-pass procedure (feature extraction/construction). This leaves an opportunity 

to formalize and develop methods that address mixed sampling rates in an automatic fashion. The authors 

approach the problem of dealing with multiple sampling rates from an aggregation perspective. They 

propose Accordion, a new embedded method that constructs and selects aggregate features iteratively, 

in a memory-conscious fashion. Their algorithms work on both classification and regression problems. 

The authors describe three experiments on real-world time series datasets, with satisfying results.

Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning

Author(s): Hilsenbeck, Sebastian;1

 Bobkov, Dmytro;1 Schroth, Georg;1 Huitl, Robert;1 Steinbach, Eckehard1

Year: 2014

Source: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous 

Computing. UbiComp '14, pp. 147–158

Publisher: Association for Computing Machinery

Author affiliation: 1

Chair of Media Technology Department of Electrical and Computer Engineering, Technische Universität 

München, 80333 Munich, Germany; http://www.lmt.ei.tum.de/en/home.html

The authors propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian 

indoor positioning using mobile devices. The authors employ their fusion technique to combine relative 

motion information based on step detection with WiFi signal-strength measurements. The method is 

based on the well-known particle filter methodology. In contrast to previous work, the authors provide a 

probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based 

representation of the indoor environment. They generate this graph by adaptive quantization of the 

indoor space, removing irrelevant degrees of freedom from the estimation problem. The authors evaluate 

the proposed method in two realistic indoor environments using real data collected from smartphones. 

In total, the dataset spans about 20 km in distance walked and includes 13 users and four different mobile 

device types. The results demonstrate that the filter requires an order of magnitude less particles than 

state-of-the-art approaches, while maintaining an accuracy of a few meters. The proposed low-complexity 

solution not only enables indoor positioning on less-powerful mobile devices, but also saves muchneeded resources for location-based end-user applications, which run on top of a localization service.
37

Hands-free gesture control with a capacitive textile neckband 

Author(s): Hirsch, Marco;1

 Cheng, Jingyuan;1 Reiss, Attila;2 Sundholm, Mathias;1 Lukowicz, Paul;1 Amft, Oliver2

Year: 2014

Source: Proceedings of the 2014 ACM International Symposium on Wearable Computers. ISWC '14, pp. 

55–58

Publisher: Association for Computing Machinery

Author affiliation: 1

German Research Center for Artificial Intelligence (DFKI), D-67663 Kaiserslautern, Germany; 

http://www.dfki.de/web?set_language=en&cl=en

2

ACTLab, Chair of Sensor Technology, Faculty of Computer Science and Mathematics, University of 

Passau, D-94032 Passau, Germany; http://www.fim.uni-passau.de/en/sensor-technology/

The authors present a novel sensing modality for hands-free gesture-controlled user interfaces, based on 

active capacitive sensing. Four capacitive electrodes are integrated into a textile neckband, allowing 

continuous unobtrusive head movement monitoring. The authors explore the capability of the proposed 

system for recognizing head gestures and postures. A study involving 12 subjects was carried out, 

recording data from 15 head gestures and 19 different postures. The authors present a quantitative 

evaluation based on this dataset, achieving an overall accuracy of 79.1 percent for head gesture recognition 

and 40.4 percent for distinguishing between head postures (69.9 percent when merging the most 

adjacent positions), respectively. These results indicate that this approach is promising for hands-free 

control interfaces. An example application scenario of this technology is the control of an electric 

wheelchair for people with motor impairments, where recognized gestures or postures can be mapped 

to control commands.

Semantic management of moving objects: A vision toward smart mobility

Author(s): Ilarri, Sergio;1

 Stojanović, Dragan;2 Ray, Cyril3

Year: 2015

Source: Expert Systems with Applications, vol. 42, no. 3, pp. 1418–1435

Publisher: Elsevier

Author affiliation: 1

Department of Computer Science and Systems Engineering, University of Zaragoza, 50018 Zaragoza, 

Spain; http://diis.unizar.es/

2

Faculty of Electronic Engineering, University of Niš, 18000 Niš, Serbia; http://www.elfak.ni.ac.rs/en

3

Institut de Recherche de l’École Navale (IRENav), F-29240 Brest Cedex 9, France; 

https://www.ecole-navale.fr/-IRENav-.html

The authors present their vision for the semantic management of moving objects. They argue that 

exploiting semantic techniques in mobility data management can bring valuable benefits to many 

domains characterized by the mobility of users and moving objects in general, such as traffic management, 

urban dynamics analysis, ambient assisted living, emergency management, mHealth, etc. The authors 

present the state of the art in the domain of management of semantic locations and trajectories and 

outline research challenges that need to be investigated to enable a full-fledged and intelligent semantic 

management of moving objects and location-based services that support smarter mobility. The authors 

propose a distributed framework for the semantic enrichment and management of mobility data and 

analyze the potential deployment and exploitation of such a framework.
38

Reference Results

Miscellaneous

Atheer Labs

Author(s): Atheer Labs1

Year: 2014

Author affiliation: 1

Atheer, Inc., Mountain View, CA 94040, United States; http://atheerair.com/

The applications for Atheer’s Augmented Interactive RealityTM platform are limitless. Atheer sees a future 

where the physical and the digital converge to create a better, safer, and more open world. The researchers 

envision a new paradigm for computing and communications that enables everyone—from doctors and 

designers, to machinists, musicians, shipbuilders, and scientists—to work smarter and realize their full 

potential. The enterprise is just the beginning: it is only a matter of time before augmented interactive 

reality extends into the home and becomes embedded in people’s everyday lives.

2015 Predictions: What’s next for wearable technology

Author(s): APX Labs1

Year: 2014

Author affiliation: 1

APX Labs, Herndon, VA 20171, United States; http://www.apx-labs.com

The year 2104 was one of tremendous growth for the wearable technology industry, and 2015 was poised 

to be even bigger. Deloitte estimated the wearable tech industry would realize nearly $3 billion in sales by 

the end of 2014, and many experts predicted that 2015 would be the year that wearable technologies 

“crossed the chasm” to achieve mainstream adoption. Without a doubt, 2015 brought big changes in the 

wearable technology space as new players entered the market and existing players left or shifted 

strategies. To help consumers and businesses understand what they can expect to see happen in the 

market, APX Labs crafted several predictions for the wearable technology industry in 2015 and hopes 

these predictions provide valuable insight for readers.

Wearable technologies: Funding competition

Author(s): Innovate UK1

Year: 2014

Author affiliation:

1

Innovate UK, Swindon, Wiltshire SN2 1UE, United Kingdom; 

https://www.gov.uk/government/organisations/innovate-uk

Innovate UK is offering six businesses up to £35,000 each to encourage innovation in wearable technology 

across the themes of sport and well-being, entertainment, hospitality, health and safety, and accessibility 

and design.

Wearable technology

Author(s): Xsens1

Year: 2014

Author affiliation:

1

Xsens Technologies B.V., 7500 AN Enschede, Netherlands; https://www.xsens.com/

Wearable technology and wearable research are enabling the next wave of innovation in cloud-connected 

wearable sports, fitness, eHealth, virtual reality, healthcare, and gaming sensor accessories for smartphones. 

Real-time 3-D body motion tracking data enable the development of apps that can recognize and classify 

complex motions such as sports techniques by digitizing the user’s exact movements for immediate 

feedback and live sharing.
39

APPENDIX B: LIST OF INTERVIEWEES

Shana Baker, Federal Highway Administration. Interviewed February 5, 2015.

Rochelle Carpenter, Nashville Area Metropolitan Planning Organization. Interviewed August 

13, 2015.

Ed Christopher, Federal Highway Administration. Interviewed February 5, 2015.

Janet Fulton, Centers for Disease Control and Prevention. Interviewed March 23, 2015.

Sherri Hunt, Environmental Protection Agency. Interviewed March 10, 2015.

Stephen Intille, Northeastern University. Interviewed July 8, 2015.

Santosh Kumar, University of Memphis. Interviewed September 1, 2015.

Kevin Patrick, University of California, San Diego. Interviewed July 17, 2015.

Prabasaj Paul, Centers for Disease Control and Prevention. Interviewed March 23, 2015.

Victoria Peters, Federal Highway Administration. Interviewed February 5, 2015.

William Riley, National Institutes of Health. Interviewed September 22, 2015.

Francis Tsow, Arizona State University. Interviewed July 7, 2015.
40

REFERENCES

1. Castell, N., Viana, M., Cruz Minguillón, M., Guerreiro, C., & Querol, X. (2013). Real-world 

application of new sensor technologies for air quality monitoring. European Topic Centre 

on Air Pollution and Climate Change Mitigation Technical Paper. Bilthoven, The Netherlands. 

Retrieved January 20, 2016, from http://acm.eionet.europa.eu/docs/ETCACM_TP_2013_16_

new_AQ_SensorTechn.pdf.
41

About the EAR Program

Federal legislation establishes an Exploratory Advanced Research (EAR) Program for 

transportation to address longer term, higher risk, breakthrough research with the 

potential for dramatic long-term improvements to transportation systems, improvements 

in planning, building, renewing, and operating safe, congestion-free, and environmentally 

sound transportation facilities. The Federal Highway Administration’s (FHWA’s) EAR 

Program secures broad scientific participation and extensive coverage of advanced 

ideas and new technologies through stakeholder engagement, topic identification, and 

sponsored research. The uncertainties in the research approach and outcomes challenge 

organizations and researchers to be innovative problem-solvers, which can lead to new 

research techniques, instruments, and processes that can be applied to future high-risk 

and applied research projects.

For more information, please visit the EAR Program Web site at http://www.fhwa.dot.

gov/advancedresearch/.
Office of Corporate Research, Technology, 

and Innovation Management

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-16-034

HRTM-30/3-16(WEB)E