UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

User Activity Recognition Method
based on Atmospheric Pressure
Sensing
Keisuke Komeda
Ritsumeikan Universitya
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577 JAPAN
kome@ubi.cs.ritsumei.ac.jp
Masahiro Mochizuki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577 JAPANmoma@ubi.cs.ritsumei.ac.jp
Nobuhiko Nishiko
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577 JAPANnishio@cs.ritsumei.ac.jp
a His current aﬃliation is Mixi, Inc.

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UbiComp ’14 , September 13 - 17 2014, Seattle, WA, USA
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http://dx.doi.org/10.1145/2638728.2641299

Abstract
Several studies have been conducted on context
recognition as well as hobby and preference extraction by
analyzing the data obtained from the sensors in asmartphone. As a smartphone component, a barometer is
expected to be useful for activity recognition because of
its low power consumption. In this work, we propose an
activity recognition method of classifying a user’s state
into indoor and outdoor states and using a barometer at
each state. In the proposed method, the floor of abuilding on which a user is located is estimated bydetermining atmospheric pressure variations sensed in theindoor state, and the user’s location is estimated bydetermining atmospheric pressure variations according to
the user movement along a track in the outdoor state. In
particular, this paper delineates the method of estimating
the current floor on which the user is located. We
confirmed that it is possible to closely estimate thecurrent floor of the building in the case of user movement
among eighteen floors.

Author Keywords
Activity Recognition, Atmospheric Pressure, Floor-level
Estimation, Location Estimation, Life-log, GPS

ACM Classification Keywords
I.5.1 [PATTERN RECOGNITION]: Models-Statistical.

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

General Terms
Experimentation, Management, Measurement,
Performance, Reliability

Introduction

Table 1: Power consumption ofeach component in operation

Type
GPS
Wi-Fi
Accelerometer
Barometer

%/h
5.28
3.41
3.64
1.79

Smartphones are equipped with a variety of hardware
components such as Wi-Fi, Global Positioning System(GPS), and sensors such as accelerometers andbarometers. In the coming years, a smartphone is
expected to serve as a mobile sensing platform forcollecting a user’s activity data in real time using thesecomponents. Research activities have been progressing in
the field of information extraction by analyzing the datacollected from the mobile sensing platform, andapplications such as personalized services are foreseen.
We have been operating a life-log sensing platformtargeting individuals through full-time sensing using thebuilt-in sensors of the mobile sensing platform forrecognizing personalized activity.
Barometers have recently been incorporated in mobile
sensing platforms. They draw the attention of researchers
because they have lower power consumption compared toother smartphone components including GPS, Wi-Fi, andaccelerometer, as shown in Table 1. The barometer canmeasure atmospheric pressure correlated to altitude andclimate. Many studies have been focusing on barometers,
and the most common approach is to determine
atmospheric pressure variations sensed because of a user’s
vertical movement between floors. This real-time sensed
pressure information can be used to recognize the status of
the user’s movement as well as the current altitude. It is,however, still diﬃcult to exactly identify the floor, which isthe basic unit of location recognition in a building.
Results of another research[2, 3] show that the moving
path of users can be estimated based on the variations inatmospheric pressure according to geographical altitude.

These research results can be applied to estimate the pathtaken by the user, but they cannot be used to obtainlocation information while the user is moving.
In this paper, we propose activity recognition methods
based on atmospheric pressure for indoor and outdoor
states respectively. We can achieve life-log sensing withlower power consumption by applying the proposed
methods to indoor and outdoor states. We apply one
method for estimating the current staying floor by
extracting information on user movement between floors
using atmospheric pressure changes in the indoor state.
We apply the other method for estimating the current
location by comparing the transition of atmosphericpressure at diﬀerent locations in the outdoor state. With
these estimation results, we can dynamically andeﬀectively switch active sensors. For instance, we will takean approach in which GPS is deactivated whileatmospheric pressure sensing, Wi-Fi positioning, andPedestrian Dead Reckoning (PDR) are activated inside abuilding until it is recognized that the user is back on the
ground floor from where he/she can go outside. Likewise,
when it is recognized that the user is outside and on a
train moving along a track, we will activate the barometer
together with the intermittent use of GPS only when the
train stops at each station.
The remainder of the paper is structured as follows. The
next section relates our work to other studies. A detailed
description of the proposed activity recognition method
follows. We present the results of our study, and finallyprovide the conclusions drawn.

Related Works and Requirements
Related works
It is important to consider the strong relationship between
atmospheric pressure and altitude because atmosphericWORKSHOP: HASCA

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Error correction

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Extract
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Figure 1: Flowchart of extractionof user movement between floors
using atmospheric pressure

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Namiki et al. [4] reported that the major cause of errors
in altitude estimation using atmospheric pressure ischanges in sea level pressure and sea surface temperature,and proposed an altitude estimation method incorporatinga countermeasure against the cause of errors. In their
proposed method, errors are corrected by using data fromnetwork-connected weather stations and meteorologicalobservatories in Japan. However, their method requires a
network-connected weather station close to the place
where the altitude estimation is performed, and thecorrection with data from the meteorological observatories
alone provides approximately three meters as the meanvalue for the estimated altitude error, which is notaccurate enough to estimate a user’s altitude on a
per-floor basis. It is highly likely to provide a wrong floor
level because the mean value is almost equivalent to thefloor height in an ordinary building.

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(a) Detection of the occurrence ofuser movement between floorspressure varies according to the changes in the altitude of
the user’s current location. There are several studies suchas [5] targeting activity recognition based on data from an
atmospheric pressure sensor equipped in recent models ofsmartphones.

  [s](b) Search of the start and end pointsof user movement between floors

Figure 2: Extraction of user
movement between floors

Researches by Watanabe et al. [7] and Ioroi et al. [1] havea common objective, which is activity recognition in thecase of user movement between floors while staying insidea building. They have been focusing on the recognition of
movement of users from one floor to another, based on
the variations in atmospheric pressure. In theseresearches, an accelerometer is used in addition to an
atmospheric pressure sensor for the recognition of user
movement between floors and the estimation of current
floor, and there is still room for improvement of thesemethods in terms of power consumption by fully takeadvantage of low power consumption of atmosphericpressure sensors. Moreover, their studies targeted at user

activity recognition during a user’s movement between
floors, not enough at the estimation of the current floor.
Wang et al. [6] proposes a 3D localization scheme that
integrates the use of the barometric sensor for altitude
estimation, Wi-Fi based 2D localization, and building
information. In their scheme, an adaptive self-calibrating
algorithm is adopted to recursively detect the floor index
by using two filter windows: ahead window and backwindow. Although their altitude estimation scheme hasmuch in common with ours at an algorithm level, theirscheme mainly targets 3D localization in an indoor
environment as opposed to our activity recognition
method that uses atmospheric pressure sensing for bothindoor and outdoor states in order to achieve life-log
sensing of user location information with lower power
consumption.
As for user activity recognition using atmosphericpressure, user location information is often required
besides information on user movements between floors.
Morishita et al. [3] have been reseaching on the trackingof wild animals, and using an approach to estimate themigratory pathway using a GPS receiver and a barometerattached to a wild animal, as well as atmospheric pressure
observation stations at fixed locations within a tracking
range. By comparing the pressure-altitude history of a
tracking target with a digital elevation model, an animal
moving-pathway is estimated between distant
GPS-positioned points, and lower power consumption isachieved than when using a GPS positioning method
alone. In the proposed method, location information forboth the start and end points and the measured
barometric altitude history are required, and the pathway
estimation process needs to be performed from theprevious GPS-positioned point after the migration has
been completed. The requirement of the fixed stations

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Figure 4: Correction foratmospheric pressure change due
to climate change

within the migration range increases the installation costs
when we apply this method to life-log sensing, because
the installation area will be larger depending on thenumber of people whose activity needs to be recognized.
As for the installation cost of fixed stations, Iwanami etal. [2] proposes an alternative method for estimating thepath in which a user moves by using only a smartphone
besides fixed stations. They attempted to estimate an
actual moving path by making an observed atmosphericpressure value at the start point as a reference value, andby observing the transition of atmospheric pressure values
relative to the reference point from both the start and end
points. These proposed methods, however, have been
proposed for the purpose of estimating the path in which
the user moves between the start and end points, and are
not suitable for the services using location information inreal time.
Requirements
The objective of this research is activity recognition using
atmospheric pressure sensor with lower power
consumption. The essential first step of activity
recognition based on atmospheric pressure in the indoor
state is the estimation of the floor level, because it
enables us to recognize the floor where a user currently
stays and we consider it will lead to more advanced indoor
activity recognition. Our approach is not based on
calculating altitude from an absolute atmospheric pressure
value; instead, it is based on reducing relevant values
required for recognition as much as possible by focusing
on the atmospheric pressure changes and variations
because of user movements between floors.
Nevertheless, we require certain values such as the verticalheight between floors, current temperature, andatmospheric pressure value at a reference location toaccurately estimate the floor level. Our approach obtains

the required values, but the number of values required canbe reduced with a certain heuristic such as using
knowledge that there are more opportunities ofrecognizing the revisits to a certain floor by referring to
the past life-log than knowing to which floor the user
moved among all floors in a high-rise building.
As for the activity recognition based on atmosphericpressure in the outdoor state, it is necessary to estimate
location information in real time. User location can be
estimated using atmospheric sensor alone, and our
method could emerge as one of the methods of obtaining
location information with lower power consumption.

Activity Recognition Method
We will introduce here our activity recognition method forextracting information related to the user’s movement
between the floors of a building and estimating thecurrent floor using atmospheric pressure variations inindoor states.
User movement information extraction and floor-level estimation using atmospheric pressure variation
Atmospheric pressure has correlation with altitude, andtherefore atmospheric pressure rapidly changes according
to user movements between the floors in indoor states. In
the proposed method, we first observe atmosphericpressure variation per unit time, and detect and extract
movement information when the variation goes above
threshold. Next, we estimate the floor level using
atmospheric pressure variations observed when the user
moved between the floors. The flowchart shown in Figure
1 illustrates the diﬀerent steps in the detection of user
movement across floors and the floor-level estimation,
each of which is explained below.
1. Atmospheric pressure data smoothing

WORKSHOP: HASCA

The noise level of an in-built barometer of a mobile
sensing platform is high, and therefore the smoothing
process of atmospheric pressure data is carried out at eachcollection. During this process, we prepare two types of
data: one is data for detection whose noise issubstantially eliminated to accurately detect atmosphericpressure changes caused by the user movements between
floors, and the other is data for extraction, which aresmoothed by suppressing the phase delay and blunting of
the waveform in order to extract the precise period of user
movement between floors.
2. Detection of the occurrence of user movement
between floors
It is necessary to extract user movement information per
se from atmospheric pressure data in order to eliminate
the eﬀect of atmospheric pressure fluctuations due tochanges in the climate and to perform floor-level
estimation only using atmospheric pressure variations
caused by the user movement between floors. As shown in
Figure 2(a), the occurrence of user movement between
floors is detected when atmospheric pressure variationconsecutively goes above threshold for a certain duration.
We eliminate false detection due to changes in the climate
and atmospheric pressure changes arising from noise by
setting atmospheric pressure variation per unit time andconsecutive observation period as the conditions ofdetection.
3. Extraction of user movement information
The detection of the occurrence of user movement
between floors occurs some time later from the occurrenceof atmospheric pressure change caused by actual floor
movement. In the proposed method, we observe
atmospheric pressure variation per unit time in the data
for extraction, search inflection points before and after thedetection of movement, and regard the points respectively

as the start and end points of user movement between
floors as shown in Figure 2(b). We finally extract a period
from the start point and the end point as user movement
information.
4. Correction of extracted movement information
As a result of investigating atmospheric pressure datawhen movement occurred, we found two main causes oferrors in extracting the amount of atmospheric pressure
variation at the period of user movement between floors.
The causes of errors and the respective correction
methods are described here.
Atmospheric pressure change due to changes inclimate during user movement between floors
The first cause is the change in atmospheric pressure due
to change in the climate. As shown Figure 4(a), an
atmospheric pressure change occurs when there are
drastic changes in sea level atmospheric pressure and sea
surface temperature even if there is no change in altitude.
We cannot extract atmospheric pressure variation solely
caused by the user’s movement between floors under thesecircumstances. To resolve this problem, we calculate theregression line using atmospheric pressure data for acertain period of time as shown in Figure 4(b), andeliminate the eﬀect of changes in the climate from thedata centered around the start point of user movement.
Noise occurrence overlapped with timing before andafter the user movement between floors
The second cause is the occurrence of noise in observed
atmospheric pressure data. Figure 3(a) indicates thesituation where the start and end points of user
movement between floors are extracted from the period
irrelevant to the likely atmospheric pressure value due tothe noise occurrence before and after the movement. To
remove the eﬀect of noise, we calculate a likely

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA&  

true

C/2|

ΔP < 0false

true

atmospheric pressure value for each floor by averaging
data from a certain duration before and after the
movement as illustrated in Figure 3(b).

false

C-1/2|

5. Finalization of extracted user movement
information
The extracted floor movement is finalized through theprocess of detection, extraction, and correction.
Timestamps of the start and end points of the user
movement between floors as well as the data of
atmospheric pressure variations due to the movement areheld in the extracted movement information.

true

ΔP <- ΔP - PC

ΔP <- ΔP - P C-1

C <- C + 1

C <- C - 1

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Figure 5: Flowchart of floor-level
estimation

Figure 6: Schematic of reference
value calculation

6. Estimation of staying floor
We estimate the total number of floors moved as theresult of movement between floors by comparing extracted
atmospheric pressure variation due to user movement tolikely atmospheric variation, which is a reference value,
between each floor in a building. Figure 5 shows aflowchart of the floor-level estimation process. We can
express the current floor as a relative value from an entry
point by accumulating the estimated floor level each timea movement between the floors is detected since theuser’s entry into a building. We proceed to explain how to
calculate a reference value in the following paragraph.

each floor as a reference value from the atmosphericpressure at each floor. Atmospheric pressure PF at eachfloor F can be calculated from the sea-level atmosphericpressure P0 , temperature at each floor TF , and altitude ateach floor ZF as shown in Eq. (1):PF = P0 (1 −(1)

As the sea-level atmospheric pressure and the station’s
temperature change from time to time, we can calculate amore accurate reference value using parameters close to
those measured in the actual environment. We obtainthese parameters from three data sources: (i)International Standard Atmosphere (ISA), (ii) observed
values at the mobile sensing platform, and (iii) data fromthe meteorological observatory. Table 2 summarizes theparameters obtained from each data source. We cancalculate the sea-level atmospheric pressure P0 andtemperature TF at each floor from obtained dataaccordingly using Eqs. (2)-(4). Note that L denotes thetemperature lapse rate, and it takes the value –0.0065
K/m in an environment where the altitude is less than orequal to 11 km.

Calculation of a reference value
In addition to the atmospheric pressure variation due touser movement between floors, we need to know
atmospheric pressure diﬀerence between floors while theuser stays in a building; in other words, a reference value isrequired as a correct answer for the floor-level estimation.
We adopt an approach to calculate a reference value
tailored to each floor because in some cases, the distance
between diﬀerent floors varies. As shown in Figure 6, we
can calculate the atmospheric pressure diﬀerence between

0.0065ZF)5.257
TF + 0.0065ZF + 273.15

P0 =

PF
0.0065ZF(1 − TF +0.0065ZF +273.15 )5.257(2)

T0 = T − LZ

(3)

TF = T0 + LZF(4)

Moreover, a temperature sensor built into a mobile
sensing platform generally measures the internal
temperature of a device and not the air temperature;
therefore, we consider that the temperature conforms to
ISA. We calculate the atmospheric pressure diﬀerenceWORKSHOP: HASCA

Table 2: Parameters obtained
from each data sourceDatasource(i)

Obtained parameters
Sea-levelatmospheric pressure P0 :1013.25 hPa, Sea
surface temperature
T0 : 288.15 K
Station’s
atmosphericpressureP
Sea-levelatmospheric pressure P0 ,Station’s
temperature T (updating
every hour on thehour)

between each floor, ∆PF , which is a reference value, by
taking a diﬀerence between atmospheric pressure value
calculated at each floor, that is, PF +1 − PF .

Evaluation
In this section, we evaluate the method used to estimate
the floor level on which the user is located.

Evaluation of floor-level estimation
Evaluation of correction eﬀect(ii)
Here, we evaluate the correction method. Information
regarding user movement between floors with/without
correction is extracted from the data collected in advance,(iii)
and we confirm the eﬀect of the correction by comparing
the diﬀerence from a reference value. We collected datawhen the user moved from one floor to another in abuilding using stairs, and the diﬀerence in the altitudes ofthe floors is 4 m. We prepared test environments with two
conditions: when the atmospheric pressure fluctuation
Table 3: Building environment
caused by change in climate is considerable, and when the
Building Each floor’s Ground fluctuation is negligible. We conducted experiments onheightheightthe user movement between floors four times in eachResearch 4.0 m for 154.2
environment. Figure 8 shows the results with/without
Buildfloor 1–2mdata correction. Figure 8(a) shows the result of a trial
4.3 m foringwith a large atmospheric fluctuation, and we confirmed
floor 3–7
(CC)that the diﬀerence with a reference value largely decreased
before and after the correction. Figure 8(b) expresses theOsaka
5.9 m for 0.4 m
result of a trial with a small atmospheric fluctuation, andFukoku floor 1
it can be observed that the diﬀerence before and after the
4.9 m for
Seimeicorrection is small because there is little room for thefloor 2–6
Buildcorrection.
4.1 m foring(FS)

floor 7–14
4.2 m forfloor 15–19

Although the maximum values after correction were larger
than those before correction in the experiments, thediﬀerences were smaller overall, and the fact does not
aﬀect the floor-level estimation process. For floor-level
estimation using the proposed method, we can tolerate anerror up to approximately 0.24 hPa in the test
environment.
Evaluation of errors in floor-level estimation on the extraction of floor movement
As for the evaluation of the floor-level estimation method,
we confirmed the accuracy of the floor movement
extraction and the floor-level estimation by comparing thediﬀerence between atmospheric pressure variation of an
extracted floor movement, an extracted value, and areference value at the correct floor visited. Moreover, we
performed an evaluation on data sources used for the
calculation of reference values by comparing actualreference values calculated with each data source.
Measurements were performed with three patterns of floor
movement, and the diﬀerence between extracted
atmospheric pressure variation and a reference value ispresented. Table 3 summarizes the environment variablesof the buildings where experiments were performed, andthe details of each trial pattern are given in Table 4.
Table 4: Details of each trial
Type of trial
By time period
By total # offloors moved
Large # offloors moved

BuildingCCCC# of floors moved+1 floor+1 – +6 floors# of trials
8 for each
6 for eachFS

+18 floors

3Floor-level estimation by time period
Figure 10 shows the result of trials to confirm that
estimating the floor level of the building where the user islocated is possible regardless of the period of time.
Floor-level estimation by the number of floors moved
We evaluated the feasibility of the floor-level estimation as
well as the eﬀect on the accuracy of the estimation of user

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   Floor-level estimation with a large number of floors
moved
We also evaluated the feasibility of the floor-level
estimation along with the eﬀect on the accuracy in thecase of user movement with the large number of floors
moved. The objective of this evaluation is to confirm theaccuracy of the floor-level estimation with each reference
value through the trials of user movement between floors
where the accumulation of the diﬀerence between
extracted values and reference values becomes large.

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Figure 9: The diﬀerence between
the extracted value and areference value (large number offloors moved)

Figure 9 shows the results for each of the three reference
value calculation methods employed.
The extracted values and reference values fall within anallowable error of nearly 0.24 hPa in every case, where themaximum number of floors moved is 18 floors, and we
confirmed that the floor-level is correctly estimated. We
also confirmed that the diﬀerence becomes large when the
total number of floors moved increases, especially in thecase of meteorological observatory-compliant reference
values. This is because the temperature observed by themeteorological observatory would be diﬀerent from room
temperature inside a building where air conditioning isused, and errors arising from atmospheric pressure
variations are accumulated as the number of floors moved
increases. In the case of floor-level estimation with thelarge number of floors moved, where errors obviously
appear, the estimation using observed-value-compliant
reference values produced the best results. It seems that
this is because air temperature in an indoor environmentis close to that of the ISA model, and atmosphericFigure 11 shows the estimation of error increase dependingon the changes in altitude caused by the user’s movement
between floors when using observed-value-compliant
reference values. The linear formula for the estimatedmaximum error is expressed with a gradient, which is theincrease in the median of errors according to the increasein the number of floors moved observed from the resultsof trials, and y-intercept, which is the diﬀerence between
the observed maximum error value and the median.
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pressure observed by a mobile device enables to apply the
sea-level atmospheric pressure, which is close to that of
the actual building environment. From these results, wepresume that the calculation of observed data-compliant
reference values is suitable for floor-level estimation in anindoor environment.



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movement between floors by changing the total number offloors moved. Figure 7 illustrates the result of theevaluation by each number of floors moved and by eachreference value.

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Figure 11: Estimated maximum error

Based on the experimental result shown in Figure 11, we
obtained a prospect for performing floor-level estimation
at a time up to 53 floors, which corresponds
approximately to 212 m height, in condition that theheight per floor of a building is 4 m, where allowable error
is nearly 0.24 hPa. The number of buildings whose height

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    (a) Reference value: ISA#" 

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    (b) Reference value: observed value             

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WORKSHOP: HASCA

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    (c) Reference value: meteorological observatory

Figure 7: The diﬀerence between extracted values and reference values (by total number of floors moved)
Table 5: Staying time at a stairlanding and the recognition rateof each movement between floors

Stayingtime
inseconds at astair landing
9
8
7
6
5
4

Recognition
rateofmovement
betweenfloors
100%
90%
80%
70%
40%
0%

exceeds 200 m is limited in Japan, and we think our
method is applicable to floor-level estimation for the
majority of buildings. Moreover, because the performanceof floor-level estimation is strongly aﬀected by theprecision of the barometer, more precise estimation is
expected to be possible using our method if theperformance of the sensor is improved.

as a separated ones if the staying time is more than nineseconds from the results in Table 5. If we can observe theoccurrence of stair landings in the time series of user
movement between floors, it will be useful as areinforcement for indoor location positioning under thecondition that the building structure is known in advance.

Evaluation of the granularity of user movement recognition
In the proposed method, user movement between floors is
recognized by observing the atmospheric pressure
variation per unit time, and whose change points frompositive to negative and vice versa are used for the
extraction of endpoints of user movement between floors
as well. As the waveforms of atmospheric pressure data
are smoothed, it can be recognized as one user movement
between floors even if it consists of multiple user
movements between floors from a fine-grained
perspective. We performed the user movement between
floors using stairs, and verified the time required torecognize each movement with the proposed method bychanging the staying time at the landings of stairs. It was
found that a movement between floors can be recognized

In this paper, we proposed an indoor-stay recognition
method using the estimation of user movement between
floors in an indoor state, along with a current location
estimation method by comparing the transition of
atmospheric pressure in moving along a track in an
outdoor state, and detailed the former method. We
focused on that the sudden changes in atmosphericpressure sensed in the indoor state because of the user’s
movement between the floors, and proposed a current
floor-level estimation method by observing atmosphericpressure values. We confirmed that the current floor-levelis correctly estimated even if the total number of floors is
as high as 18. Future work will include performancemeasurements of the floor-level estimation method inadverse environmental conditions.

Conclusion

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(a) Reference value: ISA

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(b) Reference value: observed value             

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UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

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(c) Reference value: meteorological observatory

Figure 10: The diﬀerence between extracted values and reference values (by time period)

Acknowledgements
Part of this research is supported by SCOPE(#132307011) (Strategic Information andCommunications R&D Promotion Program) from the
Ministry of Internal Aﬀairs and Communications(MIC),
Japan.

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