Maximizing stroke recovery with advanced technologies: A comprehensive assessment of robot-assisted, EMG-Controlled robotics, virtual reality, and mirror therapy interventions

Inconvenience caused by stroke brings physical disability found in many people which restricts their daily life activities. Globally, it was predicted that the prevalence of stroke will rise to 21.9 % by 2030. To improve the sensory and motor recovery of the stroke survivors, they required high attention for their controllability and adaptability. About 80 % of stroke victims have upper-limb motor deficits which affects their daily living activities. Due to technological advancement in rehabilitation engineering with new interventions, traditional physical therapy for stroke patients now translates into new rehabilitation strategies which help in fast recovery of motor tasks with intention motions. Electromyography (EMG) based stroke rehabilitation now being developed for enhancement and assessment of motion control in clinical settings. The integration of EMG-based intervention provides the feasibility to use the concept of myoelectric control with therapeutic settings. In this paper, the use of EMG-based robot aided therapy has been discussed and highlights the contribution of interventions for stroke rehabilitation. Furthermore, discussion also emphasis on virtual reality and mirror therapy and their latest interventions and approaches carried out by different investigators for the evaluations and assessment of stroke rehabilitation in lieu of considerations for improved functional motor task. The most widely used functional outcome measures for conducting clinical assessment of stroke patients in randomized control trials (RCTs) are Fugl-meyer assessment-upper extremity (FMA-UE), a score range from 0 to 66points, Fuglmeyer assessment for lower extremity (FMA-LE) has a score from 0 to 34 points, the Action Research Arm Test (ARAT), a scale from 0 to 57 points, the Functional independence measure (FIM) has a scale of 18-126 points, Modified Ashworth Scale (MAS), a scale from 0 to 5 points and Box and block test (BBT). Furthermore, in most of the RCTs the statistical significance was set at p < 0.05 (95 % confidence interval). Moreover, future directions suggested that for real time autonomous detection of motion intentions, the EMG-based machine learning must be involved with robots, virtual reality and mirror therapy-based interventions to achieve 95 % accuracy that could leads to the development of intelligent rehabilitation interventions for stroke survivors.