Design and development of IoT enabled bio-signal collection system for wheeler rehabilitation fitness assesment

Pua, Zhen Ying (2017) Design and development of IoT enabled bio-signal collection system for wheeler rehabilitation fitness assesment. Project Report. UTeM, Melaka,Malaysia. (Submitted)

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Abstract

Learning how to propel with wheelchair is the most crucial part of the rehabilitation process especially for those who suffered from lower limb spinal cord injury (SCI) due to partial recovery from SCI are only within 18 months and the chances of recovery deteriorates as time goes by [4].In a long-term,SCI patients rely on wheelchair towards their independence life through rehabilitation program.Therefore, it is essential to identify patient's strength or weakness during rehabilitation in order to suggest suitable rehabilitation programme to patient.However,it is difficult for physiotherapist to monitor patients' training as patients propel around rehabilitation center at their own. Moreover,there are up to 20 patients for monitoring at each training session.This project comprises the design and development of a system to allow physiotherapist to setup and monitor the rehabilitation progress of patients.An user interface (UI),cloud server with database to log the data of the sensors installed on wheelchair and display the real-time fitness assessment data are designed and developed in this project.Besides,this system also notify physiotherapist of patient's abnormal heart rate during training.Due to the reason that mobile IoT applications streams small packet of data and away from a convenient power supply.Bluetooth Low Energy (BLE) is used to transmit sensors data.The patient's BLE enabled smartphone,running Apps developed with Ionic framework,then streams sensors data to cloud server.With this system applied,patient's rehabilitation progress will be enhanced when physiotherapist can seamlessly access to the real-time data of the patient and subsequently provides quantitative and qualitative feedback through the developed system.The management of rehabilitation center will then be able to properly allocate center resources according to the patient attention priority from the feedback system.The developed system gave an instant data update for every second with low power consumption of 306mW - 357mW.

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