Towards unobtrusive detection and realistic attribute analysis of daily activity sequences using a finger-worn device

摘要

Detection and analysis of activities of daily living (ADLs) are important in activity tracking, security monitoring, and life support in elderly healthcare. Recently, many research projects have employed wearable devices to detect and analyze ADLs. However, most wearable devices obstruct natural movement of the body, and the analysis of activities lacks adequate consideration of various real attributes. To tackle these issues, we proposed a two-fold solution. First, regarding unobtrusive detection of ADLs, only one small device is worn on a finger to sense and collect activity information, and identifiable features are extracted from the finger-related signals to identify various activities. Second, to reflect realistic life situations, a weighted sequence alignment approach is proposed to analyze an activity sequence detected by the device, as well as attributes of each activity in the sequence. The system is validated using 10 daily activities and 3 activity sequences. Results show 96.8 % accuracy in recognizing activities and the effectiveness of sequence analysis.