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      Safety Assessment of Rehabilitation Robots: A Review Identifying Safety Skills and Current Knowledge Gaps

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          Abstract

          The assessment of rehabilitation robot safety is a vital aspect of the development process, which is often experienced as difficult. There are gaps in best practices and knowledge to ensure safe usage of rehabilitation robots. Currently, safety is commonly assessed by monitoring adverse events occurrence. The aim of this article is to explore how safety of rehabilitation robots can be assessed early in the development phase, before they are used with patients. We are suggesting a uniform approach for safety validation of robots closely interacting with humans, based on safety skills and validation protocols. Safety skills are an abstract representation of the ability of a robot to reduce a specific risk or deal with a specific hazard. They can be implemented in various ways, depending on the application requirements, which enables the use of a single safety skill across a wide range of applications and domains. Safety validation protocols have been developed that correspond to these skills and consider domain-specific conditions. This gives robot users and developers concise testing procedures to prove the mechanical safety of their robotic system, even when the applications are in domains with a lack of standards and best practices such as the healthcare domain. Based on knowledge about adverse events occurring in rehabilitation robot use, we identified multi-directional excessive forces on the soft tissue level and musculoskeletal level as most relevant hazards for rehabilitation robots and related them to four safety skills, providing a concrete starting point for safety assessment of rehabilitation robots. We further identified a number of gaps which need to be addressed in the future to pave the way for more comprehensive guidelines for rehabilitation robot safety assessments. Predominantly, besides new developments of safety by design features, there is a strong need for reliable measurement methods as well as acceptable limit values for human-robot interaction forces both on skin and joint level.

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          Most cited references96

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          Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation

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            Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial

            Summary Background Loss of arm function is a common problem after stroke. Robot-assisted training might improve arm function and activities of daily living. We compared the clinical effectiveness of robot-assisted training using the MIT-Manus robotic gym with an enhanced upper limb therapy (EULT) programme based on repetitive functional task practice and with usual care. Methods RATULS was a pragmatic, multicentre, randomised controlled trial done at four UK centres. Stroke patients aged at least 18 years with moderate or severe upper limb functional limitation, between 1 week and 5 years after their first stroke, were randomly assigned (1:1:1) to receive robot-assisted training, EULT, or usual care. Robot-assisted training and EULT were provided for 45 min, three times per week for 12 weeks. Randomisation was internet-based using permuted block sequences. Treatment allocation was masked from outcome assessors but not from participants or therapists. The primary outcome was upper limb function success (defined using the Action Research Arm Test [ARAT]) at 3 months. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN69371850. Findings Between April 14, 2014, and April 30, 2018, 770 participants were enrolled and randomly assigned to either robot-assisted training (n=257), EULT (n=259), or usual care (n=254). The primary outcome of ARAT success was achieved by 103 (44%) of 232 patients in the robot-assisted training group, 118 (50%) of 234 in the EULT group, and 85 (42%) of 203 in the usual care group. Compared with usual care, robot-assisted training (adjusted odds ratio [aOR] 1·17 [98·3% CI 0·70–1·96]) and EULT (aOR 1·51 [0·90–2·51]) did not improve upper limb function; the effects of robot-assisted training did not differ from EULT (aOR 0·78 [0·48–1·27]). More participants in the robot-assisted training group (39 [15%] of 257) and EULT group (33 [13%] of 259) had serious adverse events than in the usual care group (20 [8%] of 254), but none were attributable to the intervention. Interpretation Robot-assisted training and EULT did not improve upper limb function after stroke compared with usual care for patients with moderate or severe upper limb functional limitation. These results do not support the use of robot-assisted training as provided in this trial in routine clinical practice. Funding National Institute for Health Research Health Technology Assessment Programme.
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              Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke

              Electromechanical and robot‐assisted arm training devices are used in rehabilitation, and may help to improve arm function after stroke. To assess the effectiveness of electromechanical and robot‐assisted arm training for improving activities of daily living, arm function, and arm muscle strength in people after stroke. We also assessed the acceptability and safety of the therapy. We searched the Cochrane Stroke Group's Trials Register (last searched January 2018), the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library 2018, Issue 1), MEDLINE (1950 to January 2018), Embase (1980 to January 2018), CINAHL (1982 to January 2018), AMED (1985 to January 2018), SPORTDiscus (1949 to January 2018), PEDro (searched February 2018), Compendex (1972 to January 2018), and Inspec (1969 to January 2018). We also handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trialists, experts, and researchers in our field, as well as manufacturers of commercial devices. Randomised controlled trials comparing electromechanical and robot‐assisted arm training for recovery of arm function with other rehabilitation or placebo interventions, or no treatment, for people after stroke. Two review authors independently selected trials for inclusion, assessed trial quality and risk of bias, used the GRADE approach to assess the quality of the body of evidence, and extracted data. We contacted trialists for additional information. We analysed the results as standardised mean differences (SMDs) for continuous variables and risk differences (RDs) for dichotomous variables. We included 45 trials (involving 1619 participants) in this update of our review. Electromechanical and robot‐assisted arm training improved activities of daily living scores (SMD 0.31, 95% confidence interval (CI) 0.09 to 0.52, P = 0.0005; I² = 59%; 24 studies, 957 participants, high‐quality evidence), arm function (SMD 0.32, 95% CI 0.18 to 0.46, P < 0.0001, I² = 36%, 41 studies, 1452 participants, high‐quality evidence), and arm muscle strength (SMD 0.46, 95% CI 0.16 to 0.77, P = 0.003, I² = 76%, 23 studies, 826 participants, high‐quality evidence). Electromechanical and robot‐assisted arm training did not increase the risk of participant dropout (RD 0.00, 95% CI ‐0.02 to 0.02, P = 0.93, I² = 0%, 45 studies, 1619 participants, high‐quality evidence), and adverse events were rare. People who receive electromechanical and robot‐assisted arm training after stroke might improve their activities of daily living, arm function, and arm muscle strength. However, the results must be interpreted with caution although the quality of the evidence was high, because there were variations between the trials in: the intensity, duration, and amount of training; type of treatment; participant characteristics; and measurements used. Review question To assess the effects of electromechanical and robot‐assisted arm training for improving arm function in people who have had a stroke. Background More than two‐thirds of people who have had a stroke have difficulties with reduced arm function, which can restrict a person's ability to perform everyday activities, reduce productivity, limit social activities, and lead to economic burden. Electromechanical and robot‐assisted arm training uses specialised machines to assist rehabilitation in supporting shoulder, elbow, or hand movements. However, the role of electromechanical and robot‐assisted arm training for improving arm function after stroke is unclear. Study characteristics We identified 45 trials (involving 1619 participants) up to January 2018 and included them in our review. Twenty‐four different electromechanical devices were described in the trials, which compared electromechanical and robot‐assisted arm training with a variety of other interventions. Participants were between 21 to 80 years of age, the duration of the trials ranged from two to 12 weeks, the size of the trials was between eight and 127 participants, and the primary outcome (activities of daily living: the most important target variable measured) differed between the included trials. Key results Electromechanical and robot‐assisted arm training improved activities of daily living in people after stroke, and function and muscle strength of the affected arm. As adverse events, such as injuries and pain, were seldom described, these devices can be applied as a rehabilitation tool, but we still do not know when or how often they should be used. Quality of the evidence The quality of the evidence was high.
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                Author and article information

                Contributors
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                22 March 2021
                2021
                : 8
                : 602878
                Affiliations
                [ 1 ]Roessingh Research and Development, Enschede, Netherlands
                [ 2 ]Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
                [ 3 ]Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands
                [ 4 ]Fraunhofer Institute for Factory Operation and Automation, Magdeburg, Germany
                [ 5 ]Interactive Robotics Laboratory, CEA LIST, Palaiseau, France
                [ 6 ]National Research Council of Italy, Milan, Italy
                [ 7 ]Department of Robot Technology, Danish Technological Institute, Odense, Denmark
                Author notes

                Edited by: Sanja Dogramadzi, The University of Sheffield, United Kingdom

                Reviewed by: Brahim Brahmi, McGill University, Canada

                Danijela Ristic-Durrant, University of Bremen, Germany

                *Correspondence: Jule Bessler, j.bessler@ 123456rrd.nl

                This article was submitted to Biomedical Robotics, a section of the journal Frontiers in Robotics and AI

                Article
                602878
                10.3389/frobt.2021.602878
                8080797
                33937345
                95fa4d82-686f-41c3-9bfe-9666ad1b80cb
                Copyright © 2021 Bessler, Prange-Lasonder, Schaake, Saenz, Bidard, Fassi, Valori, Lassen and Buurke.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 September 2020
                : 28 January 2021
                Funding
                Funded by: Horizon 2020 10.13039/501100007601
                Categories
                Robotics and AI
                Review

                rehabilitation robots,physical human-robot interaction,safety assessment,hazards,regulation,standardization,mechanical safety,development phase

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