Automated Detection of Older Adults’ Naturally-Occurring Compensatory Balance Reactions: Translation From Laboratory to Free-Living Conditions

Abstract

Objective: Older adults’ falls are a critical public health problem. The majority of free-living fall risk assessment methods have investigated fall predictive power of step-related digital biomarkers extracted from wearable inertial measurement unit (IMU) data. Alternatively, the examination of characteristics and frequency of naturally-occurring compensatory balance reactions (CBRs) may provide valuable information on older adults’ propensity for falls. To address this, models to automatically detect naturally-occurring CBRs are needed. However, compared to steps, CBRs are rare events. Therefore, prolonged collection of criterion standard data (along with IMU data) is required to validate model’s performance in free-living conditions. Methods: By investigating 11 fallers’ and older non-fallers’ free-living criterion standard data, 8 naturally-occurring CBRs, i.e., 7 trips (self-reported using a wrist-mounted voice-recorder) and 1 hit/bump (verified using egocentric vision data) were localized in the corresponding trunk-mounted IMU data. Random forest models were trained on independent/unseen datasets curated from multiple sources, including in-lab data captured using a perturbation treadmill. Subsequently, the models’ translation/generalization to older adults’ out-of-lab data were assessed. Results: A subset of models differentiated between naturally-occurring CBRs and free-living activities with high sensitivity (100%) and specificity (≥99%). Conclusions: The findings suggest that accurate detection of naturally-occurring CBRs is feasible. Clinical/Translational Impact- As a multi-institutional validation study to detect older adults’ naturally-occurring CBRs, suitability for larger-scale free-living studies to investigate falls etiology, and/or assess the effectiveness of perturbation training programs is discussed.

Keywords

• Compensatory balance reactions
• free-living digital biomarker
• falls
• machine learning
• fall risk assessment