In this paper, we report the results of a radar and accelerometer based pilot observational study of gait patterns of elderly participants enrolled in an exercise program aimed at improving strength, balance, and agility. We employ a radar system and a wearable accelerometer device to capture biomechanical movements of the participants as they walk back and forth in front of a radar. We extract gait parameters by analyzing the Doppler signatures obtained from the radar measurements and time-series data from the accelerometer device worn on the wrist while walking. Additionally, we record physical activity levels of participants over a two-week period using the wrist-worn accelerometer device and determine duration of moderate-to-vigorous physical activity (MVPA). The gait parameters and MVPA duration, extracted from two separate sets of measurements made prior to and at the conclusion of the exercise program, are used to assess potential changes in the gait and mobility of the participants. Using percentage change in parameter values as a metric, the results generally demonstrate a positive impact of the exercise program on gait and physical activity levels. At the same time, an appreciable categorical agreement is observed between the two sensing modalities.
Stroke is a leading cause of long-term disability in survivors, imposing functional limitations such as mobility impairments, speaking, and understanding, as well as paralysis. The outcomes of stroke on function and mobility may vary from complete paralysis of one side of the body to one-sided weakness of the body. This forces individuals to use multiple types of assistive technologies (AT) for mobility and balance. The use of AT combined with variations in functional recovery post-stroke create hard to detect complex mobility modes and patterns. Existing clinic- and community-based post-stroke rehab interventions rely on measurements of physical activity, rehab, and health outcomes using validated clinical tools, such as questionnaires and self-reports. These tools, however, suffer from participant bias, recall bias, and social acceptability bias. To address some of the limitations of self-report, research in use of body sensors for detecting and quantifying mobility in individuals with stroke has gained increased interest. In this paper, we consider a body-plus-assistive-device based network and identify dominant sensors for classification of complex mobility modes, such as walking with a cane or a walker, or other mobility activity, influenced by functional limitations and AT usage.
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