Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Moore, Jason, Stuart, Sam, McMeekin, Peter, Walker, Richard, Celik, Yunus, Pointon, Matthew and Godfrey, Alan (2023) Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data. Sensors, 23 (2). p. 891. ISSN 1424-3210

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Official URL: https://doi.org/10.3390/s23020891


Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Item Type: Article
Additional Information: Funding information: This research is co-funded by a grant from the National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) North-East and North Cumbria (NENC). This research is also co-funded as part of a PhD studentship (J.M.) by the Faculty of Engineering and Environment at Northumbria University. Additionally, work was supported in part, by the Parkinson’s Foundation (PF-FBS-1898, PF-CRA-2073) (PI: S.S.).
Uncontrolled Keywords: gait, wearables, free-living, computer vision, environment
Subjects: C600 Sports Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Health and Life Sciences > Nursing, Midwifery and Health
Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: John Coen
Date Deposited: 10 Jan 2023 11:56
Last Modified: 17 Jan 2023 09:30
URI: https://nrl.northumbria.ac.uk/id/eprint/51117

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