Design of arm movement for upper limb after stroke rehabilitation using enhanced VR-based

Abstract

Every year, 15 million people worldwide suffer from stroke attack. Nearly six million dead and another five million are left permanently disabled. According to National Stroke Association of Malaysia (NASAM), stroke is the third leading cause of death in Malaysia after cardiovascular disease and cancer. Stroke may lead to serious disability including loss of vision or speech, muscle disability and confusion. Muscle impairment can be treated by intense use and active movement of affected limbs to stimulate the weak muscle and slowly develop the motor function which enables sufferers to slowly regain the movement of the affected limbs. Conventional stroke therapy is costly at the same time less engaging, thus virtual reality (VR) system could be the main focus of enhancing stroke rehabilitation giving the stroke patient the possibility of action involvement sense at the same time offering many other benefits such as reducing therapy cost, providing more realistic assessment and adaptable to patient condition. Currently researchers are developing various virtual reality arm rehabilitation for post stroke patients, but less of the arm training task are design with measuring the muscle activity and most of the movement sequence are random. In this research, 18 fundamental arm movements are analyzed using EMG acquisition system involving deltoid anterior, deltoid lateral, biceps, triceps, flexor and extensor. The EMG signals were pre-processed to eliminate noise. Three statistical features which are mean, standard deviation and amount of movement (AOM) were then extracted from the EMG signals to analyze arm movements and muscle activation. Based on the results, AOM feature was chosen to represent muscle activity and four most activated muscles which are deltoid lateral, deltoid anterior, biceps and triceps were identified with each having AOM of 2.061, 1.113, 0.911 and 0.394 respectively. These results are then employed to design movement sequences in real (physical) environment involving 2D coronal plane, the amount of movement from all movement sequence were obtained and compared with the ideal criterion of rehabilitation (warming up, intensive and cooling down). The results were comparable to the proposed muscle activity pattern and the selected movement sequences were translated into virtual environment. Final experiment was conducted in virtual environments where subjects interacted with virtual objects using 5DT data glove and webcam, results show that movements made in VE trigger higher AOM compare to real environment but have comparable pattern. Final experiment to assess the consistency of the VR based system, SD of AOM for each movement are calculated with the highest SD of 0.501 for more intensive movements which is acceptable as movement style are not fixed between subjects. Generally, the experimental results show that it is possible to design optimum functional movements for arm rehabilitation after stroke. The system was tested using healthy subjects and revealed with potential rehabilitation system for stroke patient.