Detection and Diagnosis of Paralysis Agitans

Humans' daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems, such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people's living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7%. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications.