Wearable Sensors and Systems for Rehabilitation

Dear Colleagues,

With recent technological advances, engineering has played a crucial role in improving the quality of lives of persons with disabilities as well as that of healthcare service providers, developing therapeutic interventions, assistive technologies, and objective monitoring methods to measure outcomes in the field of physical rehabilitation. However, there are many open challenges and opportunities to integrate engineering concepts into rehabilitation, not only reducing healthcare costs, but also increasing population wellbeing and wealth. This motivates researchers to conduct studies, design and develop novel rehabilitative and assistive technologies, and investigate methods to help people to monitor, improve, and recover cognitive and motor functions. Specifically, the challenge is to transfer the research results and new knowledge to all stakeholders concerned, such as users and their caregivers, physicians, physiotherapists, as well as occupational therapists, hospitals, clinics, and industry, raising a general awareness of the importance of rehabilitation engineering.

The purpose of this Special Issue (SI) is to demonstrate high-quality research and contributions as well as reviews that deal with the opportunities and challenges regarding the application of wearable and sensor technologies for the prevention, detection, monitoring, and management of a diverse range of health issues, including neurological, musculoskeletal, and cardiovascular problems in a physical rehabilitation context.

Topics of interest include but are not limited to:

• Wearable sensors;
• Patient activity monitoring;
• Reliability and validity of sensor-based measurements;
• Sensor-based feedback on motor performance;
• Sensor-based measurement of therapy adherence;
• Sensor-based telerehabilitation;
• Body sensors networks;
• Smart clothing/textiles technologies for rehabilitation purposes;
• Multimodal information fusion;
• Intelligent signal processing;
• Telerehabilitation and telemonitoring;
• Deep learning and machine learning techniques;
• Smart-phone applications for patient monitoring in rehabilitation context;
• Pervasive and unobtrusive patient monitoring solutions;
• Monitoring of physical condition of persons through lifespan;
• The integration of multiple sensor information.

Dr. Seungmin Rho
Dr. Naveen Chilamkurti
Prof. Neeraj Kumar
Guest Editors

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