Automatic Detection of Gait Perturbations With Everyday Wearable Technology

Objective: Older adults face a heightened fall risk, which can severely impact their health. Individual responses to unexpected gait perturbations (e.g., slips) are potential predictors of this risk. This study examines automatic detection of treadmill-generated gait perturbations using acceleration and angular velocity from everyday wearables. Detection is achieved using a deep convolutional long short-term memory (DeepConvLSTM) algorithm. Results: An F1 score of at least 0.68 and recall of 0.86 was retrieved for all data, i.e., data from hearing aids, smartphones at various positions and professional sensors at lumbar and sternum. Performance did not significantly change when combining data from different sensor positions or using only acceleration data. Conclusion: Results suggest that hearing aids and smartphones can monitor gait perturbations with similar performance as professional equipment, highlighting the potential of everyday wearables for continuous fall risk monitoring.