An inertial and positioning dataset for the walking activity

Caramaschi, Sara1 ; Olsson, Carl Magnus1 ; Orchard, Elizabeth2 ; Molloy, Jackson2 ; Salvi, Dario1

Research facility: Malmö University

Published Aug 29, 2024 on Dryad. https://doi.org/10.5061/dryad.n2z34tn5q

Data files

Aug 29, 2024 version files 169.53 MB

data.zip

169.52 MB
README.md

8.13 KB

Abstract

We are publishing a walking activity dataset including inertial and positioning information from 18 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.

README: An Inertial and Positioning Dataset for the walking activity

https://doi.org/10.5061/dryad.n2z34tn5q

Description of the data and file structure

We publish a walking activity dataset including inertial and positioning information from 18 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

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 (<6 90 deg curves) or curved path (>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).

Methods

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.

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.

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.

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.

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.