# A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot
## 2 Related Works
Several clinical studies have studied the efficacy of these types of assisted rehabilitation strategies and have concluded that they are effective in reducing the motor impairments of stroke victims, and that they improve the ability to perform activities of daily living [25; 41]. Most of these rehabilitation exercises require the presence of professional therapists that help and supervise the results of this process. However, the lack of medical resources, the difficulties in visiting these professionals daily, and the challenges to transfer this clinic technology to a home environment make the rehabilitation process much harder for patients when they are at home. Recent works have focused on at-home rehabilitation exercises in order to provide these tools outside of a clinic environment. In [42], the authors developed a multisensor system for rehabilitation and interaction with people with motor and cognitive disabilities. In the case of [43], PHAROS was proposed, an interactive robot system that recommends and monitors physical exercises at home, designed for the rehabilitation of chronic diseases. More recently, [44] proposed an augmented reality platform to engage and supervise rehabilitation sessions at home using low-cost sensors. This platform also stores a user's statistics and allows therapists to tailor the exercise programs according to their performance. The results presented in these research works suggest that these platforms improve the results and adhesion to rehabilitation exercises. However, there is a lack of this type of research specialized in hand motor rehabilitation at home that take into account the particularities and needs of its exercises and evaluations. Hand pose estimation methods are a really interesting topic, and thus, there are several different state-of-the-art approaches. For instance, there are pure vision-based methods [45; 46] that take as input images of hands and compute the hand pose. Nonetheless, these systems lack enough accuracy and are very computationally demanding, requiring a powerful graphics processing unit. There are also algorithms that process electromyography (EMG) signals [47] to predict static hand poses. These kind of methods are also unsuitable because they do not provide fine hand tracking, but rather, a range of fixed poses. Hand tracking can also be achieved by inertial measure unit sensors [48], but it is also limited to a range of specific hand movements. The methods reviewed so far rely on computational models to perform predictions. Nonetheless, there are also hardware-based methods to perform hand tracking. For instance, motion capture systems [49; 50] could be used, but they are expensive and intrusive, as they must be worn by the users. After carefully considering the different approaches to perform hand tracking, we decided to include the Leap Motion controller [51] in HaReS. This device is low cost and is not intrusive, as it is placed in the desk and the user does not need to wear it or any specific marker. In addition, its error ranges from 0.2 to 1.2 mm [52], reportedly being accurate and robust enough for a range of different applications, including robot manipulation [53] and dataset creation [54].