PHYSICAL ACTIVITY DISCRIMINATION IMPROVEMENT USING ACCELEROMETERS AND WIRELESS SENSOR NETWORK LOCALIZATION

摘要

Automating documentation of physical activity data (e.g., duration and speed of walking or propelling a wheelchair) into theelectronic medical record (EMR) offers promise for improving efficiency of documentation and understanding of bestpractices in the rehabilitation and home health settings. Commercially available devices which could be used to automatedocumentation of physical activities are either cumbersome to wear or lack the specificity required to differentiate activities.We have designed a novel system to differentiate and quantify physical activities, using inexpensive accelerometer-basedbiomechanical data technology and wireless sensor networks, a technology combination that has not been used in arehabilitation setting to date. As a first step, a feasibility study was performed where 14 healthy young adults (mean age =22.6 ± 2.5 years, mean height = 173 ± 10.0 cm, mean mass = 70.7 ± 11.3 kg) carried out eight different activities whilewearing a biaxial accelerometer sensor. Activities were performed at each participant’s self-selected pace during a singletesting session in a controlled environment. Linear discriminant analysis was performed by extracting spectral parametersfrom the subjects’ accelerometer patterns. It is shown that physical activity classification alone results in an average accuracyof 49.5%, but when combined with rule-based constraints using a wireless sensor network with localization capabilities in anin silico simulated room, accuracy improves to 99.3%. When fully implemented, our technology package is expected toimprove goal setting, treatment interventions and patient outcomes by enhancing clinicians’ understanding of patients’physical performance within a day and across the rehabilitation program.