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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84725

    Title: 智慧型中風病人復健輔具系統開發-總計畫暨子計畫一:應用電路開發;Intelligent Rehabilitation Aid System on Stroke Patients--Subproject I: Development of Biomedical Circuits
    Authors: 李柏磊;李龍豪;王昱棨
    Contributors: 電機工程學系
    Keywords: 穿顱電刺激器;邊緣計算;腦波人機介面;復健輔具;Transcranial electric stimulation;Edge computing;Brain computer interface;Rehabilitation assistive device
    Date: 2020-12-08
    Issue Date: 2020-12-09 10:47:48 (UTC+8)
    Publisher: 科技部
    Abstract: 隨著社會生活壓力的提升,腦中風病人的人數逐年攀升,成為台灣地區死亡人數第二位的疾病,相較於其它新血管疾病,腦中風的病人大約有三分之一的人會導致終身殘障,依照目前的中風復健方法,有90%接受復健療程的病患,仍然因為中風而失能,目前台灣約有二十萬因為中風而失能的人口,造成社會與家庭的經濟壓力,也讓中風成為最昂貴的疾病。因此,我們的目標在於開發一套結合穿顱電刺激與人工肌肉輔具的智慧型復健輔具系統。本計畫為智慧型復健輔具系統的總計畫與子計畫一,負責開發穿顱電刺激電路、腦電波量測電路、腦波人機介面邊緣計算電路、以及復健輔具系統的感測電路。我們從大腦復健與肢體復健兩個方面著手,藉由深度學習網路分析大腦的腦電波,評估受試者的大腦復健情形,並擬定穿顱腦電刺激策略,促進腦神經的神經活化與神經可塑性;另外在肢體復健方面,利用深度學習網路偵測大腦的意念,驅動HASEL人工肌肉達成肢體復健的目的。本計畫所完成的智慧型復健輔具系統,除了在復健輔具的應用之外,將來也可以使用於癱瘓病人的行動輔具應用。 ;With the raise of life pressure in modern society, the number of stroke patients increases year by year. It makes stroke have become the second leading cause of death in Taiwan. Comparing to cardiovascular diseases, stroke usually incurs more serious sequela, because one third of the stroke patients have consequence of disability after stroke attack in their following lives. According to previous medical reports, around 90% stroke patients who accepted traditional rehabilitation have only minor improvements in their mobile capabilities. Daily care of these disabled people costs huge economic burden to both society and families which makes stroke become the most expensive disease. Accordingly, we aim to develop a new intelligent rehabilitation aid system which combines transcranial electric stimulation (tES) and artificial muscle – based assistive device. This proposal includes the main project and the subproject I of the intelligent rehabilitation aid system, in charge of developing tES device, wireless electroencephalography (EEG) system, edge computing of brain computer interface, and sensing circuit for the rehabilitation assistive device. The design of our system will start from two aspects. First, we will evaluate the rehabilitation status of patient’s brain from whole-head EEG using deep learning network, so that the strategy of tES for improving neural activities and plasticity can be confirmed. For the aspect of rehabilitation assistive device, we design deep learning network based brain computer interface (BCI) to detect subject’s movement intentions, and the detected intentions will be used to help patient’s manipulate the HASEL artificial muscle to achieve their limb movements. The research outcome of our intelligent rehabilitation aid system can be used as prototype to design mobile assistive device for disabled patients in the future.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[電機工程學系] 研究計畫

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