Understanding Older Users’ Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment

Algorithms processing data from wearable sensors promise to more accurately predict risks of falling – a significant concern for older adults. Substantial engineering work is dedicated to increasing the prediction accuracy of these algorithms; yet fewer efforts are dedicated to better engaging users through interactive visualizations in decision-making using these data. We present an investigation of the acceptance of a sensor-based fall risk assessment wearable device. A participatory design was employed to develop a mobile interface providing visualizations of sensor data and algorithmic assessments of fall risks. We then investigated the acceptance of this interface and its potential to motivate behavioural changes through a field deployment, which suggested that the interface and its belt-mounted wearable sensors are perceived as usable. We also found that providing contextual information for fall risk estimation combined with relevant practical fall prevention instructions may facilitate the acceptance of such technologies, potentially leading to behaviour change.