Biomedical Electronics and Signal Laboratory
Department of Electrical Engineering
Chang Gung University
Advisor: Prof. Hsiao-Lung Chan 詹曉龍
TEL: 886-3-2118800-5145
Email: chanhl@mail.cgu.edu.tw
The research areas primarily focus on
wearable medical measurements, neural information processing, deep learning
applications in medicine, and virtual reality rehabilitation training. To date,
25 projects funded by the National Science Council, 9 projects from the
Ministry of Economic Affairs' academic industry specialized programs, 15
research projects from Chang Gung Memorial Hospital, and 4 industry-academia
collaboration projects have been led. There have been 82 journal articles
published and 6 patents filed, with a total of 1893 citations in Scopus and an
h-index of 23. Additionally, seven research awards from Chang Gung Memorial
Hospital and four research grants from the National Science Council have been
received.
Research topics
1. Deep Learning in Medicine
Utilizing physiological signals obtained from devices or virtual reality systems developed by our laboratory, or directly acquired from hospitals, such as EEG data from epilepsy, Parkinson's disease, and sleep apnea, advanced signal processing and deep learning-based AI algorithms are used to develop disease-related identification and assessment indicators.
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 in Rehabilitation: Multi-task, immerse visual feedback to evoke cerebral reorganization and motor recovery
Using wearable devices as a bridge, this involves designing virtual reality (VR) rehabilitation scenarios tailored for Parkinson's disease and stroke patients. These scenarios provide feedback, enhance the sense of immersion and the variety of rehabilitation activities, improve brain plasticity, and enhance motor function.
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. Virtual mirror feedback with robot-assisted training for post-stroke hemiplegia and 5G applications
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. Laser-light visual cueing for patients with Parkinson’s disease
In 2017, a two-year project funded by the National Science Council was conducted on "Development and Application of Intelligent Assistive Walking Shoes for Parkinson's Disease." In 2019, another two-year project funded by the National Science Council focused on "Development of Intelligent Assistive Devices for Independent Walking and Autonomous Rehabilitation Training at Home for Parkinson's Disease Patients" was executed. The research results have been reported by nine media outlets, including the Economic Daily News, Central News Agency, ETtoday Health Cloud, and China Broadcasting Corporation. Additionally, in 2022, the research was featured in special reports by Taiwan Television and Next TV.
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. Sleep apnea detections
Cooperated with Huijia Technology Co., Ltd. in developing a multi-parameter physiological signal monitoring system with recognition capabilities, specifically for sleep quality analysis. This collaboration resulted in the publication of one article in the SCI international academic journal Physiological Measurement and the filing of one patent.
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.