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The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning
2019
IEEE Transactions on Biomedical Engineering
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This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders.
doi:10.1109/tbme.2019.2900863
pmid:30794506
fatcat:7qb7vt7e3jdz7gmsgbn7w4mos4