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From brain to movement: Wearables-based motion intention prediction across the human nervous system

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Tang, C 
Xu, Z 
Occhipinti, E 
Yi, W 
Xu, M 


Fueled by the recent proliferation of energy-efficient and energy-autonomous or self-powered nanotechnology-based wearable smart systems, human motion intention prediction (MIP) plays a critical role in a wide range of applications, such as rehabili- tation and assistive robotics, to enable more natural, biologically inspired, and seamless integrated motion assistance task execution, including for elders and physically impaired patients. With the increasing complexity of human-machine interactions and the need for personalized assistance, there is a growing demand for real-time and accurate MIP sys- tems. This review aims to provide a comprehensive understanding of the interdisciplinary field of MIP, under the logic of its physiological foundations, by discussing state-of-the- art sensing technologies, including brain-computer interfaces (BCI), electromyography (EMG), and motion sensors, alongside the relevant data processing techniques and decod- ing algorithms. We emphasize the importance of fostering collaboration among scholars from different domains to capture the intricate dependencies between the set of stimuli and responses of the central nervous system and the activation of the complex set of muscles and joints that produce human motion. By offering insights into the recent advancements and future prospects of the field, this review seeks to stimulate further research and innova- tion in the rapidly evolving area of human motion intention prediction, for a future where technologies understand and respond to complex human intentions patterns, anticipating their needs.



Motion intention prediction (MIP), Wearable sensors, Brain-computer interface (BCI), Electromyography (EMG), Explainable AI, Multimodal motion analysis

Journal Title

Nano Energy

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Volume Title


Elsevier BV