Machine Learning and Multimodal Sensing for Smart Wearable Assistive Robotics

Dear Colleagues,

Wearable assistive robotics technology has grown rapidly in recent years, allowing the development of devices which are capable of assisting people in performing activities of daily living (ADL), such as walking on flat surfaces and stairs, and sitting and standing. This progress, driven by advances in actuation, sensing technology, soft materials, and machine learning methods, has led to assistive robots that are portable, lightweight, and capable of making decisions.
Making use of multimodal sensing technologies and machine learning models provides the venue to develop smart assistive robots that can accurately understand the posture and activity of the human body. Furthermore, using multimodal information with novel machine learning models can allow the wearable robot to learn to adapt its performance (e.g., activity recognition and delivery of assistance) over time from interaction with the human body. Thus, this process has the potential to develop the next generation of wearable robots that can autonomously adapt and safely assist humans in ADL.
The aim of this Special Issue is to contribute to the state-of-the-art and introduce current developments on machine learning models and multimodal sensing for decision-making, adaptability, interaction, and control of wearable assistive robotics. We encourage potential authors to submit contributions of original research, new development, experimental works, and surveys in the field of wearable assistive robotics.

Dr. Uriel Martinez-Hernandez
Dr. Zhang Dingguo
Dr. Benjamin Metcalfe
Guest Editors

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