A Design and Implementation of A Home Rehabilitation Wearable System

• Main Authors: HSIAO, CHUNG-CHI, 蕭仲琦
• Other Authors: CHI, KUANG-HUI
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/hc885d

碩士 === 國立雲林科技大學 === 電機工程系 === 105 === Rehabilitation is typically a long process, during which patients often need to endure pains and sufferings. Such hardship makes people inconvenient to undergo rehabilitation, so only a small population of patients go to hospitals for regular treatment. For people in suburban or rural areas, frequent commutes between hospitals and homes are even less likely due to time, physical or transportation issues. These difficulties leave rehabilitation less accessible to many households. Concerning body parts for rehabilitation, shoulder joints are ones of the most important junctions of the human body. Any damage, weakness, or stiffness in shoulders will lead to reduced movements affecting daily lives considerably. In view that 87% of frozen shoulder (adhesive capsulitis of the shoulder) can recover completely through regular rehabilitation, this thesis aims at the promotion of shoulder rehabilitation because of its evident benefits. To this end, we propose a rehabilitation system for residential use that can free the user of the burden of repeatedly going to medical institutions back and fro, so as to facilitate early recovery. Our developed system consists of hardware and software, two major components complementary to each other. The hardware component involves an Android smartphone and a self-developed wearable device over Arduino that are attached close to the wrist and elbow joints respectively. Both devices collect in synergy the data of arm physical movements while rehabilitation is in progress. The Android APP will guide the user to do certain exercises by voice and determine instantly whether or not the current round of rehabilitation is correct. The two wearable devices are tasked to acquire data concerning arm movements sensed with the embedded accelerator and gyroscope. The device near the elbow transfers its sensed readings to the Android handset over the Bluetooth interface. The Android APP combines its own sensed data with the receipts for subsequent preprocessing of data alignment and normalization. Preprocessed outputs are then fed to another procedure implementing a back-propagation neural network which has been trained to resolve if the fed data corresponds to a correct motion. Next the APP transmits the resolved outcomes over the Internet to a backend database so that medical personnel are enabled to keep track of statistical results illustrating how individuals carried out rehabilitation. Currently we consider two types of exercise: pendulum and arm-raising exercises. Experiments were conducted by letting three subjects of different habitus, for each exercise, practice 10 correct and 10 incorrect rehabilitation movements respectively. Experimental results indicate that our development can identify the pendulum exercise at success rate of 91.67% and the arm raising exercise at 100%. The misjudgment rate is comparatively low, reflecting the value and usefulness of our design and implementation.