Title |
A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics
|
---|---|
Published in |
Frontiers in Neurorobotics, May 2017
|
DOI | 10.3389/fnbot.2017.00024 |
Pubmed ID | |
Authors |
Philipp Beckerle, Gionata Salvietti, Ramazan Unal, Domenico Prattichizzo, Simone Rossi, Claudio Castellini, Sandra Hirche, Satoshi Endo, Heni Ben Amor, Matei Ciocarlie, Fulvio Mastrogiovanni, Brenna D. Argall, Matteo Bianchi |
Abstract |
Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions. |
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Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 20% |
Switzerland | 2 | 13% |
United States | 2 | 13% |
Central African Republic | 1 | 7% |
Spain | 1 | 7% |
Netherlands | 1 | 7% |
Georgia | 1 | 7% |
Italy | 1 | 7% |
Unknown | 3 | 20% |
Demographic breakdown
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Members of the public | 12 | 80% |
Scientists | 2 | 13% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 226 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 53 | 23% |
Student > Master | 46 | 20% |
Researcher | 24 | 11% |
Student > Doctoral Student | 18 | 8% |
Student > Bachelor | 17 | 8% |
Other | 23 | 10% |
Unknown | 45 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 103 | 46% |
Computer Science | 19 | 8% |
Psychology | 11 | 5% |
Nursing and Health Professions | 10 | 4% |
Medicine and Dentistry | 6 | 3% |
Other | 21 | 9% |
Unknown | 56 | 25% |