Fall risk assessment dataset: older-adult participants undergoing the time up and go test

Wisanu Jutharee; Chatchai Paengkumhag; Warissara Limpornchitwilai; Wen Tao Mo; Jonathan H Chan; Tanagorn Jennawasin; Boonserm Kaewkamnerdpong

doi:10.1016/j.dib.2023.109653

Fall risk assessment dataset: older-adult participants undergoing the time up and go test

Data Brief. 2023 Oct 6:51:109653. doi: 10.1016/j.dib.2023.109653. eCollection 2023 Dec.

Authors

Wisanu Jutharee¹, Chatchai Paengkumhag², Warissara Limpornchitwilai², Wen Tao Mo^{3

4}, Jonathan H Chan³, Tanagorn Jennawasin⁵, Boonserm Kaewkamnerdpong²

Affiliations

¹ Institute of Field Robotics and Automation, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
² Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
³ Innovative Cognitive Computing (IC2), School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
⁴ Engineering Science, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Canada.
⁵ Department of Control System and Instrumentation Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.

Abstract

This article presents a dataset comprising signal data collected from Inertial Measurement Unit (IMU) sensors during the administration of the Time Up and Go (TUG) test for assessing fall risk in older adults. The dataset is divided into two main sections. The first section contains personal, behavioral, and health-related data from 34 participants. The second section contains signal data from tri-axial acceleration and tri-axial gyroscope sensors embedded in an IMU sensor, which was affixed to the participants' waist area to capture signal data while they walked. The chosen assessment method for fall risk analysis is the TUG test, requiring participants to walk a 3-meter distance back and forth. To prepare the dataset for subsequent analysis, the raw signal data underwent processing to extract only the walking periods during the TUG test. Additionally, a low-pass filter technique was employed to reduce noise interference. This dataset holds the potential for the development of effective models for fall risk detection based on insights garnered from questionnaires administered to specialists who observed the experiments. The dataset also contains anonymized participant information that can be explored to investigate fall risk, along with other health-related conditions or behaviors that could influence the risk of falling. This information is invaluable for devising tailored treatment or rehabilitation plans for individual older adults. The complete dataset is accessible through the Mendeley repository."

Keywords: Aging; IMU sensors; Signal data; Walking analysis.