Operationalizing a wireless wearable fall detection sensor for older adults

Abstract

Falls are the leading cause of disability and injury-related deaths among older adults, resulting in over 1.6 million annual emergency hospitalizations in the United States. Fall detection devices often rely on dramatized falls when developing algorithms. This study used tri-axial accelerometers worn by older adult research subjects in order to (1) collect false positive data (2) capture potential fall events and (3) evaluate the usability of the device among this target population. Twelve older adults wore activity monitors while participating in structured and unstructured activities. The study collected data on 120 patient days, yielding 492.5 hours of monitored time. Actigraphy data of annotated activities were used to define parameters for refining the algorithm. No falls occurred during the study, but valuable false positive data were collected. The study also obtained information on the usability of the devices and revealed user perspectives on commercializing the final product.