Wearable EMG Sensor for Gait Rehabilitation using IoT

Abstract

Interventions for movement dysfunction, including radiography and rhythmic planning, have demonstrated a significant increase in gait mechanics. While the optimum criteria for gait training are still to be determined, previous research shown that the training duration facilitates neural restructuring, thereby promoting the design of wearable technology for gait recovery. This work provides evidence of the advanced tool used to acquire muscle activity. Muscle activity is recorded and analyzed on Node micro controller unit, then sent to remote service using internet of things concepts, where message queuing telemetry transport protocol is used in the cloud based telemetry of the received signals and is given to the thing speak. The live recordings, along with the frequency variation of the gait in moving and stable condition, are also obtained on the central monitor using the MATLAB. By creating a device that can be used at home, patients would be able to practice and sustain longer recovery services on a regular basis, thus encouraging neural reorganization. This can also help us to monitor patient treatment progress even if the physiotherapist is not able to come and recorded data can be sent directly to them.