Special Issue "Sensor Fusion in Assistive and Rehabilitation Robotics"
Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 27699
Interests: rehabilitation engineering; virtual reality; human–machine interaction; sensor fusion
Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, 8008 Zurich, Switzerland
Interests: human motion synthesis; biomechanics; virtual reality; human–machine interaction; rehabilitation robotics
As the world’s population gradually grows older, more and more adults are experiencing sensory–motor disabilities due to stroke, traumatic brain injury, spinal cord injury and other diseases. People with such disabilities can greatly benefit from the help of robotic technologies: assistive robots can help people perform everyday activities (e.g., eating) despite a loss of motor function, while rehabilitation robots can help people perform intensive exercise to regain lost motor functions. However, while the effectiveness of assistive and rehabilitation robots has been demonstrated in several clinical trials (which have found, e.g., that rehabilitation robots are approximately as effective as human therapists), the technology has not yet been broadly adopted by hospitals and end-users.
This limited adoption of assistive and rehabilitation robots is not due to inappropriate mechanical design—state-of-the-art robots have many degrees of freedom and are mechanically robust to suboptimal operating conditions, allowing them to theoretically deliver support in a variety of real-world environments. Instead, the main limitation is currently the software: it is unclear how assistive and rehabilitation robots can appropriately react to users’ needs, intentions and mental states in order to provide the optimal amount of support. Thus, the limitations of state-of-the-art robots need to be addressed using novel sensor fusion algorithms that can tell the robot how to react in response to potentially unreliable multimodal information from the user and the environment.
The aim of this Special Issue is to showcase advanced sensor fusion algorithms for assistive and rehabilitation robotics, ideally with end-user evaluations that demonstrate how these algorithms perform in realistic operating conditions. These advances in sensor fusion will lead to a new generation of intelligent, collaborative robots that enhance users’ quality of life by either replacing lost sensory–motor functions or helping regain these lost functions. Topics of interest include but are not limited to:
- Affective computing (stress and workload recognition) for assistive and rehabilitation robots
- Brain–computer interfaces for assistive and rehabilitation robots
- Intention detection and gesture/gait recognition for intelligent orthoses and prostheses
- Localization and mapping for robotic wheelchairs and other mobile assistive robots
- Shared control algorithms for assistive and rehabilitation robots
- Error augmentation and resistance training in rehabilitation robotics
- Telerehabilitation and robot-aided telemonitoring
- Human factor analyses of existing intelligent robots
Prof. Domen Novak
Prof. Robert Riener
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- assistive robotics
- rehabilitation robotics
- sensor fusion
- robot control
- shared control
- intention detection
- affective computing
- simultaneous localization and mapping
- human factors
- brain–computer interfaces