Social robots in rehabilitation: A question of trust

Kellmeyer, Philipp; Mueller, Oliver; Polak, Ronit Feingold; Levy-Tzedek, Shelly

doi:10.1126/scirobotics.aat1587

Cited by 42 publications

(35 citation statements)

References 10 publications

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“…The automated algorithm we present here may further be combined with socially assistive robotics (SAR), which can administer the exercise set, and provide feedback on the user's performance [48][49][50][51]. A potential line of future investigation would be how the compensation-specific information that the SAR may deliver to users affects their acceptance and level of trust in using such a device in the process of rehabilitation [52,53]. Lastly, it would be instructive to collect and use data from the unimpaired arm when individuals with stroke perform functional RTG movements, as compensatory strategies may also involve the unimpaired side of the body.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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During the process of rehabilitation after stroke, it is important that patients know how well they perform their exercise, so they can improve their performance in future repetitions. Standard clinical rating conducted by human observation is the prevailing way today to monitor motor recovery of the patient. Therefore, patients cannot know whether they are performing a movement properly while exercising by themselves. Adhering to the exercise regime makes the rehabilitation process more effective and efficient, and thus a system that can give the patients feedback on their performance is of great value. Here, we build a machine-learning-based automated model that give patients accurate information on the compensatory (undesirable) movements that they make. To construct the model, we recorded movements from 30 stroke patients, who each performed 18 movements, used to identify the presence of six types of compensatory movements in stroke patients' movement trajectories. We used the random-forest algorithm for training this multi-label classification model. We achieved 85% average precision across the six movement compensations. This is the first study to automatically identify movement compensations based on stroke patients' data. This model can be adapted for use in in-clinic and at-home exercise programs for patients after stroke.

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Section: Summary and Future Directionsmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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“…Personalization can contribute to the development of the long-term trust relationship between humans and robots, an aspect that has been recognized in the domain of SARs [51,[66][67][68]. As well, personalization has been found to encourage user acceptance among adults [51,69].…”

Section: Implementation Challenges: the Importance Of Trustmentioning

confidence: 99%

Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access

Fosch‐Villaronga

Čartolovni

Pierce

2020

Paladyn, Journal of Behavioral Robotics

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Pediatric access to exoskeletons lags far behind that of adults. In this article, we promote inclusiveness in exoskeleton robotics by identifying and addressing challenges and barriers to pediatric access to this potentially life-changing technology. We first present available exoskeleton solutions for upper and lower limbs and note the variability in the absence of these. Next, we query the possible reasons for this variability in access, explicitly focusing on children, who constitute a categorically vulnerable population, and also stand to benefit significantly from the use of this technology at this critical point in their physical and emotional growth. We propose the use of a life-based design approach as a way to address some of the design challenges and offer insights toward a resolution regarding market viability and implementation challenges. We conclude that the development of pediatric exoskeletons that allow for and ensure access to health-enhancing technology is a crucial aspect of the responsible provision of health care to all members of society. For children, the stakes are particularly high, given that this technology, when used at a critical phase of a child’s development, not only holds out the possibility of improving the quality of life but also can improve the long-term health prospects.

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“…Stroke patients often depend on reliable and effective relationships with their therapists. They are often older and multi-morbid and suffer from psychological distress because of their disability (56). Due to the limitations and frustration a stroke patient may experience because of their impairments, a system that is designed for stroke patients should be as trustworthy as possible.…”

Section: Personalization In Rehabilitationmentioning

confidence: 99%

A Robot Goes to Rehab: A Novel Gamified System for Stroke Rehabilitation using a Socially Assistive Robot: Methodology & Feasibility Testing

Polak¹,

Levy-Tzedek²

2020

Preprint

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Background: Socially assistive robots (SARs) have been proposed as a tool to help individuals who have had a stroke to perform their exercise during their rehabilitation process. Methods: Here, we describe a robot-based gamified exercise platform, which we developed for long-term post-stroke rehabilitation. The platform uses the humanoid robot Pepper, and also has a computer-based configuration (with no robot). It includes seven gamified sets of exercises, which are based on functional tasks from the everyday life of the patients, such as reaching to a cup, or turning a key in a lock. The platform gives the patients instructions, as well as feedback on their performance, and can track their performance over time. We performed a long-term patient-usability study, where 14 stroke patients exercised with this platform (in either the robot or the computer configuration) over a 5-week period, 3 times per week, for a total of 210 sessions. Results: The stroke patients reported that this rehabilitation platform addressed their arm rehabilitation needs, and they expressed their desire to continue training with the platform even after the study ended. Conclusions: These results are especially encouraging during the COVID-19 pandemic, when the requirement to reduce physical contact and keep a social distance accentuates the need for alternative rehabilitative tools, such as SARs, to enable patients to have an uninterrupted (even if modified) rehabilitation regime. Trial Registration: This trial is registered in the NIH ClinicalTrials.gov database. Registration number NCT03651063, registration date 21.08.2018. https://clinicaltrials.gov/ct2/show/NCT03651063

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Social robots in rehabilitation: A question of trust

Abstract: Creating and maintaining trust in human-robot interactions should be priorities when designing social robots for rehabilitation.

Cited by 42 publications

References 10 publications

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access

A Robot Goes to Rehab: A Novel Gamified System for Stroke Rehabilitation using a Socially Assistive Robot: Methodology & Feasibility Testing

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Social robots in rehabilitation: A question of trust

Abstract: Creating and maintaining trust in human-robot interactions should be priorities when designing social robots for rehabilitation.

Cited by 42 publications

References 10 publications

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access

A Robot Goes to Rehab: A Novel Gamified System for Stroke Rehabilitation using a Socially Assistive Robot: Methodology &amp; Feasibility Testing

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Resources

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A Robot Goes to Rehab: A Novel Gamified System for Stroke Rehabilitation using a Socially Assistive Robot: Methodology & Feasibility Testing