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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. Yet, to date, there are no data on the motivating benefit of SARs in a long-term interaction with post-stroke patients. 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. 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 24 post-stroke patients were randomly allocated to exercise with this platform—either with the robot or the computer configuration—over a 5–7 week period, 3 times per week, for a total of 306 sessions. Results The participants in both groups reported that this rehabilitation platform addressed their arm rehabilitation needs, and they expressed their desire to continue training with it even after the study ended. We found a trend for higher acceptance of the system by the participants in the robot group on all parameters; however, this difference was not significant. We found that system failures did not affect the long-term trust that users felt towards the system. Conclusions We demonstrated the usability of using this platform for a long-term rehabilitation with post-stroke patients in a clinical setting. We found high levels of acceptance of both platform configurations by patients following this interaction, with higher ratings given to the SAR configuration. We show that it is not the mere use of technology that increases the motivation of the person to practice, but rather it is the appreciation of the technology’s effectiveness and its perceived contribution to the rehabilitation process. In addition, we provide a list of guidelines that can be used when designing and implementing other technological tools for rehabilitation. 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.

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

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.

Impairment in force regulation and motor control impedes the independence of individuals with stroke by limiting their ability to perform daily activities. There is, at present, incomplete information about how individuals with stroke regulate the application of force and control their movement when reaching, grasping, and lifting objects of different weights, located at different heights. In this study, we assess force regulation and kinematics when reaching, grasping, and lifting a cup of two different weights (empty and full), located at three different heights, in a total of 46 participants: 30 sub-acute stroke participants, and 16 healthy individuals. We found that the height of the reached target affects both force calibration and kinematics, while its weight affects only the force calibration when post-stroke and healthy individuals perform a reach-to-grasp task. There was no difference between the two groups in the mean and peak force values. The individuals with stroke had slower, jerkier, less efficient, and more variable movements compared to the control group. This difference was more pronounced with increasing stroke severity. With increasing stroke severity, post-stroke individuals demonstrated altered anticipation and preparation for lifting, which was evident for either cortical lesion side.

With the aging of the population worldwide, humanoid robots are being used with an older population, e.g., stroke patients and people with dementia. There is a growing body of knowledge on how people interact with robots, but limited information on the difference between young and old adults in their preferences when interacting with humanoid robots and what factors influence these preferences.We developed a gamified robotic platform of a cognitive-motor task.We conducted two experiments with the following aims: to test how age, location of touch interaction (touching the robot’s tablet or hand), and embodied presence of a humanoid robot affect the motivation of different age-group users to continue performing a cognitive-motor task. A total of 60 participants (30 old adults and 30 young adults) took part in two experiments with the humanoid Pepper robot (Softbank robotics). Both old and young adults reported they enjoyed the interaction with the robot as they found it engaging and fun, and preferred the embodied robot over the non-embodied computer screen. This study highlights that in order for the experience of the user to be positive a personalization of the interaction according to the age, the needs of the user, the characteristics, and the pace of the task is needed.

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