Special Issue "Multi-Sensor-Based Intelligent Systems for Physical Rehabilitation"
Deadline for manuscript submissions: closed (15 June 2019).
Department of Computer Science and Information Engineering, National Chung Cheng University, Min-Hsiung Chia-Yi 621, Taiwan
Interests: Internet of Things; body sensor networks; e-healthcare; mobile cloud computing; cloud-assisted multimedia network; embedded systems, etc.
Interests: parallel and distributed computing; embedded and ubiquitous/pervasive computing
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Department of Computer Science & Information Engineering, National Central University, Taoyuan 32001, Taiwan
Interests: Internet of Things; cloud computing; big data analysis; embedded systems; deep learning, etc.
Recently, many serious trends are emerging, including population aging, low birth rates and a lack of medical resources, which affect governments’ ability to manage healthcare. The intersection of these trends poses issues and challenges to physical rehabilitation. Meanwhile, rapid cognitive and intelligence systems and infrastructures have been developed to provide people with smarter and more efficient physical rehabilitation services. However, it is difficult to achieve greater intelligence depending on the existing techniques and models. Constructing a theoretical framework for advanced improvements to physical rehabilitation has become a critical issue. This Special Issue will provide a platform to collect novel research results and experiments on physical rehabilitation topics related to multi-sensor-based intelligent systems and other related sensor fields. This Special Issue features all recent advances, highlighting the trends with regard to advanced theory, systems and applications on physical rehabilitation issues.
Prof. Dr. Chin-Feng Lai
Prof. Dr. Laurence T. Yang
Prof. Dr. Yu-Sheng Su
Manuscript Submission Information
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- Wearable sensors
- Deep learning and machine learning techniques
- Multimodal information fusion
- Intelligent signal processing
- Physical rehabilitation
- Intelligent medical systems
- Sensorized medical devices