DOI: 10.5061/dryad.n2z34tn5q
title: An inertial and positioning dataset for the walking activity

description: We are publishing a walking activity dataset including inertial and
 positioning information from 19 volunteers, including reference distance
 measured using a trundle wheel. The dataset includes a total of 96.7 Km
 walked by the volunteers, split into 203 separate tracks. The trundle
 wheel is of two types: it is either an analogue trundle wheel, which
 provides the total amount of meters walked in a single track, or it is a
 sensorized trundle wheel, which measures every revolution of the wheel,
 therefore recording a continuous incremental distance.  
 Each track has data from the accelerometer and gyroscope embedded in the
 phones, location information from the Global Navigation Satellite System
 (GNSS), and the step count obtained by the device. The dataset
 can be used to implement walking distance estimation algorithms and to
 explore data quality in the context of walking activity and physical
 capacity tests, fitness, and pedestrian navigation.
description: The proposed dataset is a collection of walks where participants
 used their own smartphones to capture inertial and positioning
 information. The participants involved in the data collection come from
 two sites. The first site is the Oxford University Hospitals NHS
 Foundation Trust, United Kingdom, where 10 participants (7 affected by
 cardiovascular diseases and 3 healthy individuals) performed unsupervised
 6MWTs in an outdoor environment of their choice (ethical approval obtained
 by the UK National Health Service Health Research Authority protocol
 reference numbers: 17/WM/0355). All participants involved provided
 informed consent. The second site is at Malm ̈o University, in Sweden,
 where a group of 9 healthy researchers collected
 data. <br><br>This dataset can be used by researchers to
 develop distance estimation algorithms and how data quality impacts the
 estimation.  All walks were performed by
 holding a smartphone in one hand, with an app collecting inertial data,
 the GNSS signal, and the step counting. On the other free hand,
 participants held a trundle wheel to obtain the ground truth distance. Two
 different trundle wheels were used: an analogue trundle wheel that allowed
 the registration of a total single value of walked distance, and a
 sensorized trundle wheel which collected timestamps and distance at every
 1-meter revolution, resulting in continuous incremental distance
 information. The latter configuration is innovative and allows the use of
 temporal windows of the IMU data as input to machine learning algorithms
 to estimate walked distance. In the case of data collected by researchers,
 if the walks were done simultaneously and at a close distance from each
 other, only one person used the trundle wheel, and the reference distance
 was associated with all walks that were collected at the same
 time.<br><br>The walked paths are of variable length,
 duration, and shape. Participants were instructed to walk paths of
 increasing curvature, from straight to rounded. Irregular paths are
 particularly useful in determining limitations in the accuracy of walked
 distance algorithms. Two smartphone applications were developed for
 collecting the information of interest from the participants'
 devices, both available for Android and iOS operating systems. The first
 is a web-application that retrieves inertial data (acceleration, rotation
 rate, orientation) while connecting to the sensorized trundle wheel to
 record incremental reference distance [1]. The second app is the Timed
 Walk app [2], which guides the user in performing a walking test by
 signalling when to start and when to stop the walk while collecting both
 inertial and positioning data. All participants in the UK used the Timed
 Walk app. <br>The data collected during the
 walk is from the Inertial Measurement Unit (IMU) of the phone and, when
 available, the Global Navigation Satellite System (GNSS). In addition, the
 step count information is retrieved by the sensors embedded in each
 participant’s smartphone. With the dataset, we provide a descriptive table
 with the characteristics of each recording, including brand and model of
 the smartphone, duration, reference total distance, types of signals
 included and additionally scoring some relevant parameters related to the
 quality of the various signals. The path curvature is one of the most
 relevant parameters. Previous literature from our team, in fact, confirmed
 the negative impact of curved-shaped paths with the use of multiple
 distance estimation algorithms [3]. We visually inspected the walked paths
 and clustered them in three groups, a) straight path, i.e. no turns wider
 than 90 degrees, b) gently curved path, i.e. between one and five turns
 wider than 90 degrees, and c) curved path, i.e. more than five turns wider
 than 90 degrees. Other features relevant to the quality of collected
 signals are the total amount of time above a threshold (0.05s and 6s)
 where, respectively, inertial and GNSS data were missing due to technical
 issues or due to the app going in the background thus losing access to the
 sensors, sampling frequency of different data streams, average walking
 speed and the smartphone position. The start of each walk is set as 0 ms,
 thus not reporting time-related information. Walks locations collected in
 the UK are anonymized using the following approach: the first position is
 fixed to a central location of the city of Oxford (latitude: 51.7520,
 longitude: -1.2577) and all other positions are reassigned by applying a
 translation along the longitudinal and latitudinal axes which maintains
 the original distance and angle between samples. This way, the exact
 geographical location is lost, but the path shape and distances between
 samples are maintained. The difference between consecutive points “as the
 crow flies” and path curvature was numerically and visually inspected to
 obtain the same results as the original walks. Computations were made
 possible by using the Haversine Python library.
   Multiple datasets are available
 regarding walking activity recognition among other daily living tasks.
 However, few studies are published with datasets that focus on the
 distance for both indoor and outdoor environments and that provide
 relevant ground truth information for it. Yan et al. [4] introduced an
 inertial walking dataset within indoor scenarios using a smartphone placed
 in 4 positions (on the leg, in a bag, in the hand, and on the body) by six
 healthy participants. The reference measurement used in this study is a
 Visual Odometry System embedded in a smartphone that has to be worn at the
 chest level, using a strap to hold it. While interesting and detailed,
 this dataset lacks GNSS data, which is likely to be used in outdoor
 scenarios, and the reference used for localization also suffers from
 accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed
 estimation models for step length and provided an open-source dataset for
 a total of 22 km of only inertial walking data from 15 healthy adults.
 While relevant, their dataset focuses on steps rather than total distance
 and was acquired on a treadmill, which limits the validity in real-world
 scenarios.  Kang et al. [6] proposed a way to estimate travelled distance
 by using an Android app that uses outdoor walking patterns to match them
 in indoor contexts for each participant. They collect data outdoors by
 including both inertial and positioning information and they use average
 values of speed obtained by the GPS data as reference labels. Afterwards,
 they use deep learning models to estimate walked distance obtaining high
 performances. Their results share that 3% to 11% of the data for each
 participant was discarded due to low quality. Unfortunately, the name of
 the used app is not reported and the paper does not mention if the dataset
 can be made available. 
   This dataset is heterogeneous under
 multiple aspects. It includes a majority of healthy participants,
 therefore, it is not possible to generalize the outcomes from this dataset
 to all walking styles or physical conditions. The dataset is heterogeneous
 also from a technical perspective, given the difference in devices,
 acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS,
 sampling frequency in iPhone was particularly low). We suggest selecting
 the appropriate track based on desired characteristics to obtain reliable
 and consistent outcomes.   
 This dataset allows researchers to develop algorithms to
 compute walked distance and to explore data quality and reliability in the
 context of the walking activity. This dataset was initiated to investigate
 the digitalization of the 6MWT, however, the collected information can
 also be useful for other physical capacity tests that involve walking
 (distance- or duration-based), or for other purposes such as fitness, and
 pedestrian navigation.   The
 article related to this dataset will be published in the proceedings of
 the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23
 October. <br>This research is partially
 funded by the Swedish Knowledge Foundation and the Internet of Things and
 People research center through the Synergy project Intelligent and
 Trustworthy IoT Systems.
description: # An Inertial and Positioning Dataset for the walking activity
 [https://doi.org/10.5061/dryad.n2z34tn5q](https://doi.org/10.5061/dryad.n2z34tn5q) ### Change log **Oct version**: removed README from within zipped archive and fixed typo in README on Dryad ## Description of the data and file structure We publish a walking activity dataset including inertial and positioning information from **19** volunteers including reference distance measured using a trundle wheel. Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. Example code can be found at [the following link](https://github.com/SaraCaramaschi/walking_dataset_for_6MWT/) The data folder contains a metadata_tracks.csv file with attributes for each track. In addition, for every participant there is a folder (subject_X, X being a unique subject identifier), and for every track, a sub-folder (X_N, X being a unique subject identifier and N being the track identifier). **Every track folder contains the following CSV files:** Empty cells within a CSV file can be considered as missing data due to jittering, signal loss during data transmission, or recording errors. | events.csv | | | :---------- | :------------------------------------------------------------------------------- | | signalStart | time at which the test starts (but not walking!), always set as 0ms. | | testStart | ms since signalStart when the GNSS signal reaches enough quality (15m accuracy). | | testEnd | ms since signalStart when data collection stops. | | positions.csv | | | :-------------------------------- | :------------------------------------------------------------------------------------------ | | ms | milliseconds from signalStart. Zero ms corresponds to the value of testStart in events.csv. | | latitude, longitude, and altitude | geolocation coordinates. | | confInterval | confidence interval reported by the GNSS system heading. | | speed | sample-wise speed computed by the system. \[m/s] | | heading | value of heading for that timestamp. | | orientation.csv | | | :----------------- | :----------------------------- | | ms | milliseconds from signalStart. | | alpha, beta, gamma | device orientation. | | steps.csv | | | :----------------- | :----------------------------------------------------- | | ms | milliseconds from signalStart. | | steps | incremental number of steps taken. | | startDate, endDate | ms intervals when those steps were taken. | | floorsUp | number of ascending floors (not to consider) | | floorsDown | number of descending floors (not to consider) | | distance | distance estimated by the smartphone (not to consider) | | motion.csv | | | :------------------------------------------------------------ | :---------------------------------------------- | | ms | milliseconds from signalStart. | | accelX, accelY, accelZ, accelWithGX, accelWithGY, accelWithGZ | acceleration without and with G force. \[m/s^2] | | rotRateAlpha, rotRateBeta, rotRateGamma | rotation rate. | | interval | sampling frequency in ms. | | reference\_cont\_distance.csv | available if the reference distance is continuous | | :---------------------------- | :------------------------------------------------ | | ms | milliseconds from signalStart | | distance | continuous incremental value of distance \[m] | **Columns and description of the metadata_tracks.csv files and attributes:** Missing data for certain tracks (hasMotion: False or hasGNSS: False) is given by technical issues (subjects did not update smartphone app) or challenges to collect GNSS signal. | Column | Description | | :-------------------------- | :--------------------------------------------------------------------------------------------------- | | subject | unique subject ID. | | testID | test ID. | | testName | subjectID\_testID. | | isPatient | boolean value True or False. | | distanceReference | total distance walked \[m]. | | hasMotion | boolean value True or False if during the walk IMU was collected. | | hasGNSS | boolean value True or False if during the walk GNSS signal was received. | | device | brand, model and operating system of the smartphone. | | distanceByApp | distance measured by the Timed Walk App. | | totSteps | total steps taken during the walk. | | path curvature | 0,1, or 2 indicating a straight, gently curved (&lt;6 90 deg curves) or curved path (&gt;5 90 deg curves). | | total\_gaps\_time\_inertial | total time in seconds where IMU was not received for more than 0.05 seconds. | | total\_gaps\_time\_gnss | total time in seconds where GNSS signal was not received for more than 6 seconds. | | gt\_type | "final" or "continuous" according to which type of reference distance was collected. | | country | country where the subject registered the track (UK or SE) | | gnss\_anonimized | boolean True or False according to whether the geographical positions were anonymized. | | duration | duration of the walk \[s]. | | fs\_acc | average IMU sampling frequency. | | fs\_gnss | average GNSS signal sampling frequency. | | fs\_steps | average step counting sampling frequency. | | average\_walking\_speed | total reference distance divided by the test duration. | | smartphone\_position | Used smartphone position (Hand held). | | smartphone app | Used smartphone app (Timed Walk App or Malisa). |