Smart Sensors: Applications and Advances in Human Motion Analysis-Edition 2

Dear Colleagues,

New directions in the study of human motion include motion recognition and prediction and human–robot interaction perception using the sensor-based technologies that have emerged with recent technological advances (wearable sensors, advanced sensors, artificial intelligence and machine learning, electronic and smart sensing textiles, and so on). Advanced applications in both scenarios rely on the combination of smart sensors with algorithmic advances in artificial intelligence.

The adequate monitoring of human motion requires the consideration of various aspects, as follows: the use of unobtrusive, low-cost wearable sensors capable of tracking relevant motion in free-living conditions; machine-learning-based strategies for analyzing sensor data; and intuitive and collaborative sensor-based technologies for timely assistance and interaction with users in various environments. Accurate decision making in regard to human motion may bring about new achievements in robotic applications. The inclusion of motion prediction strategies in robotic assistive devices is necessary to provide patients with personalized motor assistance and mitigate risk in challenging environments. Moreover, collaborative robots, such as those used in Industry 4.0 programs and social robots, can benefit from the continuous provision of information on human motor performance and safety.

This Special Issue covers new strategies to recognize and predict human motion or human–robot interaction, both in clinical scenarios and industry, thanks to the application of smart sensors and the innovative use of standard wearable sensors. Research on sensors utilizing biofeedback strategies to augment human collaboration with robotic systems is also encouraged.

Topics of interest include, but are not limited to:

• Smart sensors for human motion analysis;
• Sensors for decision making and smart-based applications;
• Wearable sensor-based strategies for motion intention recognition;
• Machine learning algorithms for human motion recognition and prediction;
• Machine-learning-based sensor measurements for human motion estimation;
• Sensor applications in collaborative and assistive robots;
• Advanced strategies for improving human–robot interaction;
• Sensing for physical human–robot interaction;
• Applications of sensors for robotics.

Dr. Cristina P. Santos
Dr. Joana Figueiredo
Dr. Nuno Ribeiro
Guest Editors

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