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

Abstract

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.

Contextualizing remote fall risk: Video data capture and implementing ethical AI

“…Wearables: Video glasses and IMU. Any wearable camera and many attachment locations could be used within the context of gathering extrinsic data, but we suggest the use of wearable camera glasses as the technology is becoming more streamlined (i.e., subtle for daily use) and is typically ergonomically designed as well as being more user/patient friendly for passive sensing in comparison to a camera worn on the chest 18 . Wearable camera glasses also carry the potential of reduced injury in case of a fall event, especially when compared with a protruding chestmounted camera that is likely to cause further injury to the participant upon impact.…”

“…However, those approaches cannot be used within buildings, and situations beyond indoor environments may rely on outdated maps. Moreover, those approaches fail to capture the granular/minute influences on gait, such as navigating raised pathways or gait variations due to ad-hoc/random obstacles (not easily determined from outdated maps), which could generate abnormal gait characteristics 7 . Furthermore, although the use of diary-based apps can enable self-report of contextual factors 8 , it is limited due to subjective reporting 9 .…”

Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences

“…Digital biomarkers or performance outcomes arising from wearables could aid a better understanding of neurological conditions like Parkinson's Disease (PD), epilepsy, or stroke during routine functional tasks (like walking) to get a better insight into real-world challenges and incidents arising from home and/or community-based activities. Moreover, integrating multi-modal wearable data (e.g., environmental) could enhance the understanding of real-world challenges for those with a neurological disorder (Johnson and Picard, 2020 ) e.g., unstable gait leading to near falls or falls during free-living walking (Warmerdam et al, 2020 ; Moore et al, 2023 ).…”

A Wearable Multi-Modal Digital Upper Limb Assessment System for Automatic Musculoskeletal Risk Evaluation

“…Since RULA requires deployment in the field, therefore these non-automatic methods require tedious setup and a medical expert to continuously observe workers’ body movements. To empower devices to take on the burden from humans, body-mounted sensors can be used for computing RULA scores automatically, e.g., electromyograph (EMG) based methods [ 14 , 15 , 16 ] and inertial-sensor-based methods [ 17 , 18 , 19 ]. However, it is essential to note that EMG-based methods have higher costs and longer deployment times.…”

“…For the people working in environments where the setup is already installed, non-obstructive and intrinsic or body-mounted sensors, e.g., EMG, inertial sensors, gyroscope, and gravimeters, can be used for recording and analyzing motion data, and further for ergonomic risk assessments [ 17 , 18 , 19 , 28 ]. Padilla et al designed a web-based risk-factor assessment system using inertial sensors and displayed limb movements [ 29 ], but they did not provide any details for limb kinematics.…”