指導教授
詹曉龍 長庚大學電機系
TEL:886-3-2118800-5145
Email: chanhl@mail.cgu.edu.tw
實驗室電話:03-2118800 分機 5716 位置:工07F06
研究議題以穿戴式醫學量測、神經資訊處理、深度學習醫學應用及虛擬實境復健訓練應用為主。至今已主持25件國科會計畫、兩期共六年經濟部學界科專分項計畫、一期三年經濟部學界科專分項計畫、15件長庚醫院研究計畫及三個工業技術研究院委託學界技術開發計畫。目前發表82篇期刊論文與6項專利。以2025年3月SCOPUS資料列計，總引用次數1857次，h-index 23。
1. 深度學習在醫學的應用
以本實驗室研發的裝置或虛擬實境系統所得之生理訊號或直接由醫院取得的癲癇、巴金森氏症腦波與睡眠呼吸障礙生理訊號，以進階訊號處理及深度學習為主的人工智慧(AI)演算法，開發疾病相關辨識與評估指標。
Deep neural networks for the detection of temporal-lobe epileptiform discharges from scalp electroencephalograms, Biomedical Signal Processing and Control, 84:104698, 2023.
https://www.sciencedirect.com/science/article/abs/pii/S1746809423001313
Convolutional neural network for individual identification using phase space reconstruction of electrocardiogram, Sensors, 23(6):3164, 2023.
https://www.mdpi.com/1424-8220/23/6/3164
Deep neural network for the detections of fall and physical activities using foot pressures and inertial sensing, Sensors 23(1):495, 2023.
https://www.mdpi.com/1424-8220/23/1/495
Enhancing plantar pressure distribution reconstruction with conditional generative adversarial networks from multi-region foot pressure sensing, Biomedical Signal Processing and Control, 100:107187, 2025.
https://www.sciencedirect.com/science/article/pii/S174680942401245X
2. AR/VR: 應用擴增實境與虛擬實境於復健訓練，呈現沉浸式與多樣性的任務型態，透過視覺回饋達到導引與誘發效應。
以穿戴式裝置為橋樑，設計適用於巴金森氏症及中風患者之虛擬實境(VR)復健場景，提供回饋，提升沉浸感及復健多樣性，改善大腦可塑性，改善動作功能。
Event-related brain potentials reveal enhancing and compensatory mechanisms during dual neurocognitive and cycling tasks, BMC Sports Science, Medicine and Rehabilitation, 15:133, 2023.
https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-023-00749-6
Resistance-induced brain activity changes during cycle ergometer exercises, BMC Sports Science, Medicine and Rehabilitation, 13:27, 2021.
https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-021-00252-w
Evaluation of anticipatory postural adjustment before quantified weight shifting—System development and reliability test, Applied Science, 11(2):758, 2021.
https://www.mdpi.com/2076-3417/11/2/758
3. 虛擬鏡像回饋與機器人輔助訓練應用於中風後偏癱及5G技術
Myoelectric, myo-oxygenation, and myotonometry changes during robot-assisted bilateral arm exercises with varying resistances, Sensors, 24(4):1061, 2024.
https://www.mdpi.com/1424-8220/24/4/1061
Myoelectric analysis of upper-extremity muscles during robot-assisted bilateral wrist flexion-extension in subjects with poststroke hemiplegia, Clinical Biomechanics, 87:105412, 2021.
https://www.sciencedirect.com/science/article/abs/pii/S026800332100142X
4. 雷射光視覺提示應用於帕金森氏症患者
2017年執行兩年期科技部輔具科技專案計畫「巴金森氏病之提示智慧助行鞋研發與應用研究」、2019年執行兩年期科技部長照與輔具科技專案計畫「巴金森氏病患者居家獨立行走與自主復健訓練智慧輔具開發」，以及2022年國科會專題計畫「巴金森氏病患者之互動式擴增實境步態訓練及壓力影響機制研究」。研究成果亦經經濟日報、中央社、ETtoday健康雲、中廣等九家媒體報導，以及於2022及2023年分別接受台視新聞及壹電視專題報導。
魔法鞋助行走 帕金森氏患者智慧輔具【發現科學】
https://www.youtube.com/watch?v=H2NjVm5kd_M
《新聞思想啟》 第66集 運動產業 新藍海 (影片時間30:12 ~ 33:48)
https://www.youtube.com/watch?v=NzRFqB8m0GA&t=2034s
Laser-light cueing shoes with integrated foot pressure and inertial sensing for investigating the impact of visual cueing on gait characteristics in Parkinson’s disease individuals, Frontiers in Bioengineering and Biotechnology, 12:1334403, 2024.
Swing limb detection using a convolutional neural network and a sequential hypothesis test based on foot pressure data during gait initialization in individuals with Parkinson's disease, Physiological Measurement, 45:125004, 2024.
https://iopscience.iop.org/article/10.1088/1361-6579/ad9af5
5. 光纖床墊睡眠呼吸障礙偵測
2015年起至今輔導滙嘉公司開發「具辨識功能之多參數生理訊號監測系統--睡眠品質分析」，指導發表SCI國際學術期刊Physiological Measurement一篇及專利一件。2024年執行滙嘉公司委託「光纖薄墊呼吸辨識之AI呼吸中止樣態模型開發產學合作計畫」。
Sleep apnea assessment using declination duration-based global metrics from unobtrusive fiber optic sensors, Physiological Measurement, 40:075005, 2019.
https://iopscience.iop.org/article/10.1088/1361-6579/ab21b5/meta
The Respiratory Fluctuation Index: a global metric of nasal airflow or thoracoabdominal wall movement time series to diagnose obstructive sleep apnea, Biomedical Signal Processing and Control, 49:250-262, 2019.
https://www.sciencedirect.com/science/article/abs/pii/S1746809418303148
Instantaneous respiratory estimation from thoracic impedance by empirical mode decomposition, Sensors, 15:16372-16387, 2015.
https://www.mdpi.com/1424-8220/15/7/16372