Bell’s palsy is a disease that occurs primarily between ages of 15 and 60, especially in middle-aged individuals. Although this disease gradually recovers within weeks to months, recurrence and permanent sequelae are possible. Its causes are diverse and unclear. Appropriate treatment is unknown, threatening lives of patients with this condition. In this study, we measured the degree of facial paralysis in a model of Bell’s palsy patients using OpenCV and the H.B grade measurement method and classified measured values according to H.B grade classification. This enabled prediction of the type and risk of diseases that might occur depending on the degree of facial paralysis. Additionally, we utilized more coordinate data to confirm movement of facial muscles by region to address limitations of the Nottingham system measurement method. We graded the level of this movement to enable intuitive confirmation and confirmed differences between existing Nottingham system and the H.B grade. This simple system could determine the level of paralysis in patients with Bell’s palsy and their corresponding risk level for related diseases. It enables information on causative disease of patients with Bell’s palsy to be quickly obtained, enabling prompt treatment and support.

In this study, a four-axis platform capable of rolling, pitch, and yaw rotation was created for rehabilitation and exercise. Based on this, a virtual coach and a virtual reality game system were developed. The virtual coach is a 3D person model created with the Unity program that allows the user to exercise in the correct posture with the virtual coach, and recognizes the correct posture with the Kinect. Additionally, a virtual reality game has been developed, and in conjunction with this, the actual platform also moves to increase the rehabilitation effect. The upper body and lower body movement and plantar pressure measurement were used to associate with the four-axis platform to maintain strength and balance the body.