A wireless IoT system towards gait detection in stroke patients

Gait monitoring through the Internet of Things (IoT) is able to provide an overall assessment of daily living. All existing systems for predicting abnormality in gait mainly consider the gait related parameters. Their accuracy is limited because consequences due to injuries are significantly affected by different events in the gait. The objective of this study is to present a multisensory system that investigates walking patterns to predict a cautious gait in stroke patient. For this study, a smartphone built-in sensor and an IoT-shoe with a Wi-Fi communication module is used to discreetly monitor insole pressure and accelerations of the patient's motion. To the best of our knowledge, we are the first to use the gait spatiotemporal parameters implemented in smartphones to predict a cautious gait in a stroke patient. The proposed system can warn the user about their abnormal gait and possibly save them from forthcoming injuries from fear of falling.

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