Reasoning with multi-modal sensor streams for m-health applications.
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WIJEKOON, A. 2018. Reasoning with multi-modal sensor streams for m-health applications. In Minor, M. (ed.) Workshop proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 234-238. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=234
Musculoskeletal Disorders have a long term impact on individuals as well as on the community. They require self-management, typically in the form of maintaining an active lifestyle that adheres to prescribed exercises regimes. In the recent past m-health applications gained popularity by gamification of physical activity monitoring and has had a positive impact on general health and well-being. However maintaining a regular exercise routine with correct execution needs more sophistication in human movement recognition compared to monitoring ambulatory activities. In this research we propose a digital intervention which can intercept, recognize and evaluate exercises in real-time with a view to supporting exercise self-management plans. We plan to compile a heterogeneous multi-sensor dataset for exercises, then we will improve upon state of the art machine learning models implement reasoning methods to recognise exercises and evaluate performance quality.