Abstract
A proof-of-concept Wearable Mobility Monitoring System (WMMS) was developed to identify daily activities and provide environmental context, using integrated BlackBerry Smartphone low sensor and video data. Integrated accelerometer data were used to identify mobility changes-of-state (CoS) in real-time, trigger BlackBerry video capture at each CoS, and save activity outcomes on the Smartphone. System evaluation involved collecting WMMS output and (separate) camcorder video under realistic conditions for five able-bodied subjects. The subjects each performed a consecutive series of mobility tasks; including, walking, sitting, lying, stairs, ramps, elevator, bathroom activities, kitchen activities, dining activities and outdoor walking. Activity, timing and contextual information were obtained from the camcorder for comparison. Sensitivity results for sensor-based CoS identification were 97-100% for standing, sitting, lying and taking an elevator; 67-73% for walking-related CoS (stairs, ramps); 40-93% between walking and small movements (brushing teeth, etc.); and below 27% for daily living activities. False positives occurred in less than 12% of all activities, with less than 5% false positives for half the measures. Better classification results were achieved when using both acceleration features and Smartphone integrated video for all activities except sitting. The evaluation demonstrated that the WMMS algorithm and BlackBerry platform were effective for detecting mobility activities, even with low sampling rate sensors. The combined sensor and video analysis enhanced mobility task identification and contextual information.
Highlights
Understanding how people move within their daily lives is important for healthcare decision-making
The CoS results for daily living activities were poorer since the continuous series of small movements produced similar acceleration features
The number of false positives was less than 12% for all measures, with half the measures reporting less than 5% false positives
Summary
Understanding how people move within their daily lives is important for healthcare decision-making. Functional scales can be administered to help gain an understanding of a person’s mobility status[1,2,3,4]. These tests do not measure how a person moves when leaving the clinic. A quantitative method of characterizing mobility in the community would provide valid and useful information for healthcare providers and researchers. Wearable systems have been developed to evaluate mobility in any environment or location. These portable systems are worn on the body and collect mobility information within the home and community. While this study uses a smart phone as the system, the same limitations were encountered with no insight as to how those limitations may be overcome to progress this type of monitoring
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