Labeled raw accelerometry data captured during walking, stair climbing and driving

Abstract

The database contains raw accelerometry data collected during outdoor walking, stair climbing, and driving for 32 healthy adults. Accelerometry data were collected simultaneously at four body locations: left wrist, left hip, left ankle, and right ankle, at a sampling frequency of 100 Hz. The 3-axial ActiGraph GT3X+ devices were used to collect the data. The data include labels of activity type performed (walking, descending stairs, ascending stairs, driving, clapping) for each time point of data collection. Basic demographic information of participants is also provided. All data are anonymized.

Background

Wearable accelerometers provide an objective measure of human physical activity. Modern, 3-axial accelerometers are small electromechanical devices that collect acceleration of a body along three orthogonal axes. The collected measurements are often stored in a form of three-dimensional time-series and expressed in g units (standard acceleration due to gravity; defined as 9.80665m/s^2).

Data were collected with wearable accelerometers as a part of the study on Identification of Walking, Stair Climbing, and Driving Using Wearable Accelerometers, sponsored by the Indiana University CTSI grant and conducted at the Department of Biostatistics, Fairbanks School of Public Health at Indiana University. The study was led by Dr. Jaroslaw Harezlak, assisted by Drs. William Fadel and Jacek Urbanek. The study was approved by the Institutional Review Board of Indiana University; all participants provided written informed consent. 

Methods

Study participants

There were 32 healthy participants in the study - 13 men and 19 women - who were of ages ranging between 23 and 52 years. There were 31 right-handed participants; one individual identified themselves as ambidextrous.

Devices setup and placement

Participants wore four 3-axial ActiGraph GT3X+ wearable accelerometer devices, placed at left ankle, right ankle, left hip, and left wrist, respectively. ActiLife software was used to synchronize the devices to the same external clock. In theory, the synchronization procedure should assure parallel measurement among devices; however, a subsecond-level desynchronization of the devices could have happened over time (see Sect 3.8 in Karas et al. 2019). No serious desynchronization has been observed in this data. For each device, the data collection frequency was set to 100 Hz (100 observations per second).

Each device was attached to a participant's body using velcro bands. The sensors at the ankles were placed on the outside side of the ankles. The sensor at the wrist was placed similarly to a regular watch placed on the top side of the wrist. The sensor at the left hip was attached to the belt of the participant on the left hip side; when a belt was not available, the device was either attached to the corresponding belt loop or clipped to the waistband.

Walking and driving trials

The study protocol included a walking pathway (approx. 0.66 miles) followed by a driving trail (approx. 12.8 miles). Data were downloaded immediately following each participant’s session.

The walking component consisted of 5 periods of walking on level ground, 6 periods of descending stairs, and 6 periods of ascending the stairs; the part lasted between 9.0 and 13.5 minutes. Participants were asked to walk at their usual pace along a predefined course to imitate a free-living activity. One participant briefly forgot the instructions and had an additional period of walking on the level ground before turning around to ascend the stairs.

Right after the walking part of the experiment, participants were accompanied to their vehicle, and they then drove on a predefined route for between 18 and 30 min, depending on traffic. The route included both highway and city driving.

To ensure accuracy of identifying the start and stop times of different activities, participants were asked to clap three times at the beginning and end of each activity. The clapping movement generated three spikes of magnitude in the raw accelerometry data signal, allowing to mark the beginning and end of each activity and to accurately assign activity labels for each section of the protocol in a data preprocessing stage. Data corresponding to a few seconds before/after the first/last activity are included and labeled as "non-study activity".

Data Description

This project includes raw accelerometry data files, a data files dictionary, and participant demographic information. All data are anonymized. Specifically, the project files include:

1. raw_accelerometry_data: a directory with 32 data files in CSV format. Each file corresponds to raw accelerometry data measurements of 1 study participant. File names follow the convention: "subj_id.csv". Each file contains 14 variables:

• activity: Type of activity (1=walking; 2=descending stairs; 3=ascending stairs; 4=driving; 77=clapping; 99=non-study activity)
• time_s: Time from device initiation (seconds [s])
• lw_x: Left wrist x-axis measurement (gravitation acceleration [g])
• lw_y: Left wrist y-axis measurement (gravitation acceleration [g])
• lw_z: Left wrist z-axis measurement (gravitation acceleration [g])
• lh_x: Left hip x-axis measurement (gravitation acceleration [g])
• lh_y: Left hip y-axis measurement (gravitation acceleration [g])
• lh_z: Left hip z-axis measurement (gravitation acceleration [g])
• la_x: Left ankle x-axis measurement (gravitation acceleration [g])
• la_y: Left ankle y-axis measurement (gravitation acceleration [g])
• la_z: Left ankle z-axis measurement (gravitation acceleration [g])
• ra_x: Right ankle x-axis measurement (gravitation acceleration [g])
• ra_y: Right ankle y-axis measurement (gravitation acceleration [g])
• ra_z: Right ankle z-axis measurement (gravitation acceleration [g])

2. raw_accelerometry_data_dict.csv: a CSV file containing the description of 14 variables that each file in the raw_accelerometry_data directory consists of.

3. participant_demog.csv: a CSV file with participants demographic information. The file contains 7 variables:

• subj_id: Participant ID (a character scalar). The value in this column can be matched with a file name (without ".csv" extension) of a file in raw_accelerometry_data directory.
• gender: Participant gender (a character scalar; one of: "male", "female").
• age: Participant age (an integer scalar).
• height_in: Participant height (an integer scalar; expressed in inches).
• weight_lbs: Participant weight (an integer scalar; expressed in pounds).
• race: Participant race (a character scalar; one of: "asian", "black", "caucasian").
• right_handed: Participant handedness (an integer scalar; 1 if right-handed, 0 otherwise).

Usage Notes

Recent advances in technology and the decreasing cost of wearable devices led to a rapid increase in the popularity of wearable technology in health research. Wearable PA monitors have vast potential for health studies including an estimation of PA fragmentation into active and sedentary states, quantification of time spent at different PA intensity levels, and precise identification of activity types at the subsecond level. We discuss the challenges and opportunities of working with accelerometry data in health research in an accompanying paper [3]. 

Acknowledgements

This data collection was made possible, in part, with support from the Indiana Clinical and Translational Sciences Institute Design and Biostatistics Pilot Grant funded in part by grant UL1TR001108 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. Jaroslaw Harezlak has received funding from the National Institute of Mental Health research grant R01MH108467.

Conflicts of Interest

The authors do not have a financial, commercial, legal, or professional relationship with other organizations, or with the people working with them, that could influence this research. 

References

1. Fadel, W. F., Urbanek, J. K., Albertson, S. R., Li, X., Chomistek, A. K., & Harezlak, J. (2019). Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data. Statistics in Biosciences, 11(2), 334–354. doi: https://doi.org/10.1007/s12561-019-09241-7
2. Straczkiewicz, M., Urbanek, J., Fadel, W., Crainiceanu, C., & Harezlak, J. (2017). Automatic Car Driving Detection Using Raw Accelerometry Data. Innovation in Aging, 1(suppl_1), 1239–1239. doi: 10.1093/geroni/igx004.4499
3. Karas, M., Bai, J., Strączkiewicz, M., Harezlak, J., Glynn, N. W., Harris, T., … Urbanek, J. K. (2019). Accelerometry Data in Health Research: Challenges and Opportunities. Review and Examples. Statistics in Biosciences, 11, 210–237. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874221/