Abstract

There is a significant body of literature demonstrating that accelerometers placed at various locations on the body can provide the data necessary to recognize walking. Most of the literature, however, either does not consider accelerometers placed at the wrist, or suggests that the wrist is not the ideal location. The wrist, however, is probably the most socially-acceptable location for a monitoring device. This study evaluates the possibility of using wrist accelerometers to recognize walking in the elderly during everyday life to evaluate the amount of time spent walking and, moreover, potentially recognize changes in stability that might lead to falls. Thirty elderly individuals aged 65 years and older were asked to wear a wrist accelerometer for four hours each while simultaneously being videorecorded as they went about their normal daily activities. Accelerometer data were then analyzed using both frequency- and time-domain analyses. Particular attention was given to methods capable of being calculated on the wrist device so that future work will not require streaming large amounts of data from the device to the central server. Frequencybased analysis to characterize walking in the test set yielded results of 98% area under the receiver operating characteristic curve (AUC). Using a time-series algorithm limited to features calculable on the wrist device, moreover, achieved an AUC of 90%. A small, socially-acceptable, wrist-based device, therefore, can successfully be used to differentiate walking from other activities of daily living in older adults. These findings may enable improved gait monitoring and efforts in falls prevention.

Keywords: Activities of daily living, elderly, fall prevention, fall risk assessment, falls, gait monitoring, home monitoring, mobile computing, telemonitoring, walking, walking frequencies, wearable technology, wrist-based accelerometers, wrist-worn.