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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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