Design and Development of a Virtual Reality Based Cycling Platform System with the Applications into the Detection and Training for Patients with Parkinson's Disease

It is reported from the previous studies in literature that patients with Parkinson's Disease (PD) usually suffer from gait, balance, and cognitive disturbances as well as reduced postural responses. These deficiencies due to motor and cognitive functional abnormality also substantially increase the risk of fall and the mobile difficulty, especially during complex or dual-task walking. In fact, traditional gait training may not be able to effectively, fully resolve complex gait activities. On the other hand, in recent years there are some researches having indicated that Virtual Reality (VR) has been widely used in postural control and balance training, diagnosis and assessment for PD patients. This is because VR can effectively incorporate a feedback learning process, and thus can provide a variety of attractive and challenging training in complex virtual environments. Therefore, this project aims to develop a 3D VR-based dual-task cycling training system. It allows early detection of PD for subjects that undergo the VR-based dual-task cycling exercise. Also, for those who suffer from PD, the exercise training system may help them effectively improve the motor disturbance of lower limb as well as achieve the enhancement of cognitive function. In this project, we expect to accomplish the following objectives in the upcoming three years: 1) to establish an 3D VR exercise scene; the VR system can reflect in the exercise intensity corresponding to the virtual scene to the subject, and also quantitatively assess the performance of motor task execution, 2) to incorporate a series of cognitive tasks (cognitive tasks, such as route memory, memory map, the direction of judgment ... etc.) and quantitative assessments of performance in executing the tasks mentioned above into the VR system design, and 3) to finalize the system prototyping of the interactive VR-based dual-task detecting and training bicycle system.

Project ID:PB10408-5722
External Project ID:MOST104-2221-E182-017