
透過非侵入式腦波感測與人工智慧辨識技術,系統可判讀患者運動意圖,控制外骨骼輔助下肢復健訓練,臺中榮總實測平均腦控辨識準確率達92.5%。
工研院在經濟部產業技術司支持下,攜手陽明交大、臺大及各大醫院開發「腦波控制外骨骼復健系統」,透過非侵入式腦波與AI技術判讀患者運動意圖以協助下肢復健,臺中榮總實測平均腦控辨識準確率達92.5%。
AI-generated summary
工研院在經濟部產業技術司支持下,開發腦波控制外骨骼復健系統,並與多所大學及醫院合作進行臨床驗證。
透過非侵入式腦波感測與人工智慧辨識技術,系統可判讀患者的運動意圖,控制外骨骼輔助下肢復健訓練。
為提升中風患者的復健品質與效率,工研院在經濟部產業技術司支持下,攜手陽明交通大學、臺灣大學開發「腦波控制外骨骼復健系統」,並與臺中榮民總醫院及高雄醫學大學附設中和紀念醫院合作進行臨床驗證。透過非侵入式腦波感測與人工智慧辨識技術,系統可判讀患者的運動意圖,控制外骨骼輔助下肢復健訓練。其中,臺中榮總中風患者實測的平均腦控辨識準確率達92.5%,展現腦波控制技術應用於神經復健的潛力。
工研院服務系統科技中心總監鄭伯壎表示,傳統外骨骼復健設備主要透過預設動作模式輔助患者訓練,工研院研發的腦波控制外骨骼復健系統,則進一步結合腦波辨識技術,讓患者能透過運動意圖參與復健過程。這項技術由工研院服務系統科技中心技術長暨陽明交通大學電控工程所教授柯立偉,將自主研發的非侵入式腦波感測裝置及辨識演算法導入系統,利用石墨烯乾式電極腦波帽擷取訊號,辨識使用者的運動意圖,進而控制外骨骼輔助動作。
為提升腦波訊號處理效率,團隊並與臺灣大學電機工程學系暨電子工程研究所教授楊家驤合作,將原本由軟體執行的腦波辨識演算法轉為硬體加速設計,透過可程式化晶片(FPGA)完成功能驗證。測試結果顯示,運算效能較傳統電腦處理器提升76倍,有助於縮短腦波訊號處理時間,提升外骨骼動作控制的即時性。
在臨床驗證方面,患者穿戴腦波感測設備及下肢外骨骼後,系統即可分析腦波訊號、辨識動作意圖,進而控制外骨骼輔助完成復健訓練。團隊在臺中榮民總醫院復健部主任程遠揚協助下,已完成中風住院患者的訓練與測試,平均腦控辨識準確率達92.5%。此外,團隊也與高雄醫學大學附設中和紀念醫院復健部醫師陳嘉炘合作進行臨床試驗,持續評估系統應用於神經復健的可行性。
在產業化方面,工研院與福寶科技、富伯生醫等國內業者合作,推動輕量化智慧復健設備及自主穿戴應用。目前團隊已採用40奈米製程完成腦波訊號處理晶片試製,後續將進行晶片功能測試及量產規劃,並與業者合作建置品質管理系統,依循衛生福利部食品藥物管理署相關法規及產品驗證流程,逐步推動技術產品化,促進臺灣智慧醫療與復健輔具產業發展。
AI outlook — possibilities, not facts
進行晶片功能測試及量產規劃並依規推動產品化
Very likely · Within months

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