The recovery of hand function is one of the most challenging topics in stroke rehabilitation. Although the robot-assisted therapy has got some good results in the latest decades, the development of hand rehabilitation robotics is left behind. Existing reviews of hand rehabilitation robotics focus either on the mechanical design on designers’ view or on the training paradigms on the clinicians’ view, while these two parts are interconnected and both important for designers and clinicians. In this review, we explore the current literature surrounding hand rehabilitation robots, to help designers make better choices among varied components and thus promoting the application of hand rehabilitation robots. An overview of hand rehabilitation robotics is provided in this paper firstly, to give a general view of the relationship between subjects, rehabilitation theories, hand rehabilitation robots, and its evaluation. Secondly, the state of the art hand rehabilitation robotics is introduced in detail according to the classification of the hardware system and the training paradigm. As a result, the discussion gives available arguments behind the classification and comprehensive overview of hand rehabilitation robotics.
Stroke, caused by death of brain cells as a result of blockage of a blood vessel supplying the brain (ischemic stroke) or bleeding into or around the brain (hemorrhagic stroke), is a serious medical emergency . Stroke can result in death or substantial neural damage and is a principal contributor to long-term disabilities [1, 2]. According to the World Health Organization estimates, 15 million people suffer stroke worldwide each year . Although technology advances in health care, the incidence of stroke is expected to rise over the next decades . The expense on both caring and rehabilitation is enormous which reaches $34 billion per year in the US . More than half of stroke survivors experience some level of lasting hemiparesis or hemiplegia resulting from the damage to neural tissues. These patients are not able to perform daily activities independently and thus have to rely on human assistance for basic activities of daily living (ADL) like feeding, self-care, and mobility .
The human hands are very complex and versatile. Researches show that the relationship between the distal upper limb (i.e., hand) function and the ability to perform ADL is stronger than the other limbs [7–9]. The deficit in hand function would seriously impact the quality of patients’ life, which means more demand is needed on the hand motor recovery. However, although most patients get reasonable motor recovery of proximal upper extremity according to relevant research findings, recovery at distal upper extremity has been limited due to low effectivity . There are two main reasons for challenges facing the recovery of the hand. First, in movement, the hand has more than 20 degree of freedom (DOF) which makes it flexible, thus being difficult for therapist or training devices to meet the needs of satiety and varied movements . Second, in function, the area of cortex in correspondence with the hand is much larger than the other motor cortex, which means a considerable amount of flexibility in generating a variety of hand postures and in the control of the individual joints of the hand. However, to date, most researches have focused on the contrary, lacking of individuation in finger movements [12, 13]. Better rehabilitation therapies are desperately needed.
Robot-assisted therapy for poststroke rehabilitation is a new kind of physical therapy, through which patients practice their paretic limb by resorting to or resisting the force offered by the robots . For example, the MIT-Manus robot uses the massed training approach by practicing reaching movements to train the upper limbs ; the Mirror Image Movement Enabler (MIME) uses the bilateral training approach to train the paretic limb while reducing abnormal synergies . Robot-assisted therapy has been greatly developed over the past three decades with the advances in robotic technology such as the exoskeleton and bioengineering, which has become a significant supplement to traditional physical therapy [17, 18]. For example, compared with the therapist exhausted in training patients with manual labor, the hand exoskeleton designed by Wege et al. can move the fingers of patients dexterously and repeatedly [19, 20]. Besides, some robots can also be controlled by a patient’s own intention extracted from biosignals such as electromyography (EMG) and electroencephalograph (EEG) signals [21, 22]. These make it possible to form a closed-loop rehabilitation system with the robotic technology, which cannot be achieved by any conventional rehabilitation therapy .
Existing reviews of hand rehabilitation robotics on poststroke motor recovery are insufficient, for most studies research on the application of robot-assisted therapy on other limbs instead of the hand . Furthermore, current reviews focus on either the hardware design of the robots or the application of specific training paradigms [23, 24], while both of them are indispensable to an efficient hand rehabilitation robot. The hardware system makes the foundation of the robots’ function, while the training paradigm serves as the real functional parts in the motor recovery that decides the effect of rehabilitation training. These two parts are closely related to each other.
This paper focuses on the application of robot-assisted therapy on hand rehabilitation, giving an overview of hand rehabilitation robotics from the hardware systems to the training paradigms in current designs, for a comprehensive understanding is pretty meaningful to the development of an effective rehabilitation robotic system. The second section provides a general view of the robots in the entire rehabilitation robotic system. Then, the third section sums up and classifies hardware systems and the training paradigms in several crucial aspects on the author’s view. Last, the state of the art hand rehabilitation robotics is discussed and possible direction of future robotics in hand rehabilitation is predicted.[…]