The Novel Rehabilitation Technique Using the Lower-limb Exoskeleton Robot for Hemiplegic Patients Based on Normal Side Movements

Abstract

With the continuing growth in the global population, it is expected to reach 10 billion by 2060. It will be an aged society, with 17.6% of the population above the age of 65 years. Stroke is the number one cause of death among geriatric illnesses; 64 % of stroke survivors suffer from a loss of kinesthesis, while 15–30% of them experience permanent impairment, such as hemiplegia. The independent walking ability of hemiplegic patients is critical, both personally and socio-economically. As such, gait rehabilitation is the most important element in the rehabilitation of hemiplegic patients. Since the gait rehabilitation of hemiplegic patients is a labor-intensive process requiring 2–3 physiotherapists, various gait rehabilitation robots have been developed to complement the process. The gait rehabilitation robots that have already been developed for hemiplegic patients are based on passive movement. The robots provide exercises consisting of moving the leg of the normal side and the leg of the affected side in the same pattern. However, such exercises make it difficult for patients to proactively participate in gait training. The exercise capacity of the normal leg of hemiplegic patients is the same as that of normal persons, and the movement of the normal leg reflects the patient’s willingness to move. Identifying and incorporating the movements of the normal leg into walking exercises can encourage patients to participate in rehabilitation exercises more proactively. In this thesis, the rehabilitation technique induced the involvement of robots in gait rehabilitation training to a minimum, a departure from past studies in which robots were heavily involved in gait rehabilitation training. The identification of walking movements of the normal leg in the rehabilitation exercise were proposed as well as the development and adjustment of walking training patterns of the affected leg. In addition, an adaptive virtual tunnel control method was suggested to assist patients in proactively participating in rehabilitation exercises. In particular, the proposed adaptive virtual control method allows for adjusting the gain real-time, based on the status of the patient: it reduces the robot’s assistance in sections where the patient carries out the exercise well, and vice versa. Clinical experiments were conducted with 13 hemiplegic patients using the exoskeleton walking rehabilitation robot, HEXAR WA-H(Hanyang Exoskeleton Assistive Robot, Walking Assist for Hemiplegia), which applied walking training and control techniques. The results showed that most of the patients who took part in the walking training experienced 10–40% improvement on average in Fugl-Meyer, Berg Balance Scale, Timed Up and Go, 6-min walk evaluations. Moreover, the qualitative survey results indicated that patients proactively participated in the walking training. Hence, the methods proposed by the current study are found to be effective in the rehabilitation training conducted with robots.