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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/352

    Title: 微機電感測器資料手套建構與手部復健姿態偵測實作;Hand Motion Detection with MEMS Sensor Data Glove for Rehabilitation
    Authors: 楊廷鴻;YANG, TING-HONG
    Contributors: 電機工程研究所
    Keywords: 資料手套;微機電感測器;姿態偵測;手部姿態模擬;Data glove;MEMS sensor;Attitude detection;Hand attitude simulation
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 關於資料手套的研究,近年來成為熱門的研究領域,雖然實際的資料手套相關產品目前並不普遍,但資料手套被大量的研究且結合影音娛樂、工業設計,以及醫療。在醫療方面,目前資料手套多為手部復健器材,作為復健治療執行的操作介面,因量測結果為大略近似值,鮮少應用於協助病情評估中。我們希望資料手套,可在使用復健器材時,偵測手部姿態,量測關節角度。協助醫療人員診斷患者手部能力,這對中風以及手部手術後的患者有相當的幫助。本論文將透過姿態資料手套,取得手部姿態。在手套上手指與手掌的背面位置安裝十六組重量輕且體積小的姿態感測器(Motion Sensor),其為含有加速計、角速度計以及磁力計的微機電感測器(Micro Electro Mechanical Systems Sensor),姿態感測器量測的數據被收集後,經過校正,利用航姿參考系統演算法融合數據,這類姿態偵測以地磁為參考,取得手套上的十六組感測器姿態。所有感測器姿態以Wi-Fi傳輸至電腦,在電腦上藉由Vpython執行模擬運算以及透過向量運算關節彎曲角度,呈現手部的姿態模擬,並可算得各個關節的彎曲角,成為診斷時的參考。協助醫療人員判斷患者手部能力。
    In recent years, data gloves have become one of the popular researching topics, although data glove products are not common now. Data gloves can provide easier control by direct hand actions. Many researchers use it in their research areas which include media, industrial design, and medical area. In the medical area, most of data gloves are used just as control interface of rehabilitation systems. The measuring results are rough approximations. It is rarely applied as a diagnosis supporting device in a rehabilitation process. We want to develop a data glove as a diagnosis device for rehabilitation. The new data glove can detect hand attitude and hand joint bend angles. Doctor and therapist can use the data glove to diagnose hands ability of patients. This is helpful for patients after stroke and hand surgery.In the thesis, we get the hand motion by the attitude detecting data glove. Sixteen motion sensors are set on the back of the glove. The motion sensor includes an accelerometer, a gyroscope, and a magnetometer. Those are MEMS (Micro Electro Mechanical Systems) sensors. Data of the inertial sensor and the magnetic sensor are fused to get the attitude by AHRS algorithm. The attitude detection refers the magnetic field of the earth. The attitudes of 16 sensors are gathered into a socket and the socket is sent to the computer by Wi-Fi. The hand attitude simulation and the joint angles are computed with Vpython on the computer. Finally, the hand attitude is presented on screen with the limitation for impossible hand action. The bend angles of joints can be fetched by the vector computing. The result can be a reference for diagnosis.
    Appears in Collections:[電機工程研究所] 學位論文

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