This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.

1In recent years, the field of Human-Computer Interaction (HCI) has been advanced with many technologies, however, most are limited to healthy users. In this paper, we leveraged the technology of free-hand interaction to rehabilitate patients with stroke. We modified the game of Fruit Ninja to use Leap Motion controller's hand tracking data for stroke patients with arm and hand weakness to practice their finger individuation. In a pilot study, we recruited 14 patients with chronic stroke to play the game using natural interaction. Their Fruit Ninja (FN) scores show high correlation with the standard clinical assessment scores such as Fugl-Meyer (FMA) and Box-and-Blocks Test (BBT) scores. This finding suggests that our freehand Fruit Ninja's score is a good indicator of the patient's hand function and therefore will be informative if used in their rehabilitation.

Background and objective Advances in technology are providing new forms of human-computer interaction. The current study examined one form of human-computer interaction, augmented reality (AR), whereby subjects train in the real world workspace with virtual objects projected by the computer. Motor performances were compared with those obtained while subjects used a traditional human-computer interaction, i.e., a personal computer (PC) with a mouse. Methods Patients used goal-directed arm movements to play AR and PC versions of the Fruit Ninja video game. The two versions required the same arm movements to control the game but had different cognitive demands. With AR, the game was projected onto the desktop, where subjects viewed the game plus their arm movements simultaneously, in the same visual coordinate space. In the PC version, subjects used the same arm movements but viewed the game by looking up at a computer monitor. Results Among 18 patients with chronic hemiparesis after stroke, the AR game was associated with 21% higher game scores (p=0.0001), 19% faster reaching times (p=0.0001), and 15% less movement variability (p=0.0068), as compared to the PC game. Correlations between game score and arm motor status were stronger with the AR version. Conclusions Motor performances during the AR game were superior to those during the PC game. This result is due in part to the greater cognitive demands imposed by the PC game, a feature problematic for some patients but preferred for others. Mode of human-computer interface influences rehabilitation therapy demands and can be individualized for patients.

The heterogeneity of stroke prompts the need for predictors of individual treatment response to rehabilitation therapies. We previously studied healthy subjects with EEG and identified a frontoparietal circuit in which activity predicted training-related gains in visuomotor tracking. Here we asked whether activity in this same frontoparietal circuit also predicts training-related gains in visuomotor tracking in patients with chronic hemiparetic stroke. Subjects (n = 12) underwent dense-array EEG recording at rest, then received 8 sessions of visuomotor tracking training delivered via home-based telehealth methods. Subjects showed significant training-related gains in the primary behavioral endpoint, Success Rate score on a standardized test of visuomotor tracking, increasing an average of 24.2 ± 21.9% (p = 0.003). Activity in the circuit of interest, measured as coherence (20–30 Hz) between leads overlying ipsilesional frontal (motor cortex) and parietal lobe, significantly predicted training-related gains in visuomotor tracking change, measured as change in Success Rate score (r = 0.61, p = 0.037), supporting the main study hypothesis. Results were specific to the hypothesized ipsilesional motor-parietal circuit, as coherence within other circuits did not predict training-related gains. Analyses were repeated after removing the four subjects with injury to motor or parietal areas; this increased the strength of the association between activity in the circuit of interest and training-related gains. The current study found that (1) Eight sessions of training can significantly improve performance on a visuomotor task in patients with chronic stroke, (2) this improvement can be realized using home-based telehealth methods, (3) an EEG-based measure of frontoparietal circuit function predicts training-related behavioral gains arising from that circuit, as hypothesized and with specificity, and (4) incorporating measures of both neural function and neural injury improves prediction of stroke rehabilitation therapy effects.

Introducing computer games to the rehabilitation market led to development of numerous Virtual Reality (VR) training applications. Although VR has provided tremendous benefit to the patients and caregivers, it has inherent limitations, some of which might be solved by replacing it with Augmented Reality (AR). The task of pick-and-place, which is part of many activities of daily living (ADL's), is one of the major affected functions stroke patients mainly expect to recover. We developed an exercise consisting of moving an object between various points, following a flash light that indicates the next target. The results show superior performance of subjects in spatial AR versus non-immersive VR setting. This could be due to the extraneous hand-eye coordination which exists in VR whereas it is eliminated in spatial AR.