# A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot
###### Abstract  
In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.  
hand motor rehabilitation; sEMG; hand pose; social robot +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote 0: Chair of the  
ment of patients' performance. This way, the therapists can provide an optimized and enhanced service to more patients.  
HaReS is composed of a surface electromyography (sEMG) sensor and a hand-tracking device that allows to record and provide quantitative measurements and feedback of the rehabilitation sessions that a patient is performing. The exercises are set up by a therapist for each user. In addition, HaReS can take advantage of a social robot to show the exercises and to interact with patients. Thus, enhancing their adherence and engaging them. The system is modular, so it is able to work without the sensors or the robot. Apart from the robot, the rest of the system is fairly low cost. It is important to note that it is executed on a desktop or laptop computer, so all components are connected to it. The hand-tracking device, the sEMG sensor, and the social robot are all connected to the computer, which is in charge of running HaReS. In this sense, the robot is not executing HaReS, nor are the sensors connected to it. The role of the robot within the HaReS framework is to display the system in its built-in screen and interact with the patient using gestures, lights, and its speech capabilities.  
The main contributions of this paper are the following:  
* A hand rehabilitation system that integrates a series of exercises for the motor- and brain-injured;
* Use of low-cost sensors for hand tracking and muscle signal monitoring;
* An automatic and quantitative evaluation of the exercises to be analyzed by the therapists;
* Optional use of a social robot to improve patient adherence to rehabilitation  
The rest of the paper is structured as follows. First, some related work to this matter are presented in Section 2. Then, Section 3 describes the proposal in detail. Next, the evaluation we performed to validate the system is given in Section 4. Finally, Section 5 states the conclusion of this work and future research directions.