Understanding human movements (walking, standing, reaching, grasping, etc) are crucial for motor neuroprosthetics and neurorehabilitation including functional electrical stimulation, prosthetic limbs, robotic exoskeletons, etc. The concerned approaches are categorized in several aspects. First, advanced sensing technologies may measure and collect abundant data (neural signals, dynamic data, and kinetic data) of human movements, which are the basis for understanding movements. In particular, wearable sensing is an active topic in recent years. The explosion of wearable sensors can be attributed to several factors, such as affordability and ergonomics provided by advances in miniaturized electronics, which make wearable sensors more pervasive. Second, signal processing methods have a dramatic improvement for movement analysis at present. Emerging machine learning and deep learning algorithms in AI field may extract more useful information from multiple-source signals related to human movement. This is very important for human-machine interfaces in neuroprosthetics and neurorehabilitation. For example, motion intention recognition attracts huge interests from researchers in signal processing field, which can well connect humans and machines. Third, various modeling methodologies such as muscle synergy modeling, musculoskeletal modeling, and motor skill modeling, can provide platforms to investigate movement mechanisms and thus enhance understanding movements. Fourth, human motor control focuses on the control of movement as well as the control of stability, so understanding motor control may biologically inspire engineers to develop better controllers for intelligent systems towards neuroprosthetics and neurorehabilitation.
We encourage potential researchers to submit their contributions on the following related topics to :
Advanced sensing technologies of human movements
Human-machine interfaces in neuroprosthetics and neurorehabilitation
Machine learning and deep learning algorithms for movement-related signal processing
Modeling methodologies such as muscle synergy modeling, musculoskeletal modeling, and motor skill modeling
Human motor control and human-robot interaction control
Understanding human movements (walking, standing, reaching, grasping, etc) are crucial for motor neuroprosthetics and neurorehabilitation including functional electrical stimulation, prosthetic limbs, robotic exoskeletons, etc. The concerned approaches are categorized in several aspects. First, advanced sensing technologies may measure and collect abundant data (neural signals, dynamic data, and kinetic data) of human movements, which are the basis for understanding movements. In particular, wearable sensing is an active topic in recent years. The explosion of wearable sensors can be attributed to several factors, such as affordability and ergonomics provided by advances in miniaturized electronics, which make wearable sensors more pervasive. Second, signal processing methods have a dramatic improvement for movement analysis at present. Emerging machine learning and deep learning algorithms in AI field may extract more useful information from multiple-source signals related to human movement. This is very important for human-machine interfaces in neuroprosthetics and neurorehabilitation. For example, motion intention recognition attracts huge interests from researchers in signal processing field, which can well connect humans and machines. Third, various modeling methodologies such as muscle synergy modeling, musculoskeletal modeling, and motor skill modeling, can provide platforms to investigate movement mechanisms and thus enhance understanding movements. Fourth, human motor control focuses on the control of movement as well as the control of stability, so understanding motor control may biologically inspire engineers to develop better controllers for intelligent systems towards neuroprosthetics and neurorehabilitation.
We encourage potential researchers to submit their contributions on the following related topics to :
Advanced sensing technologies of human movements
Human-machine interfaces in neuroprosthetics and neurorehabilitation
Machine learning and deep learning algorithms for movement-related signal processing
Modeling methodologies such as muscle synergy modeling, musculoskeletal modeling, and motor skill modeling
Human motor control and human-robot interaction control
