Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients

Neurological disorders, such as Parkinson’s disease and Alzheimer’s disease, affect millions of individuals worldwide, leading to significant healthcare challenges. Early detection and ongoing monitoring of these illnesses are critical for effective treatment and better quality of life. In this paper, a system for monitoring and surveillance of neurological disorder patients using face recognition with a CNN four-layered architecture is proposed. The system analyzes facial expressions and movements to detect changes in symptoms, using video data captured by cameras placed in the patient’s environment. The system's performance is examined using a case study of Parkinson's disease patients, and its effectiveness in identifying changes in facial expressions and movements over time is demonstrated. The results show that the training accuracy at epoch 14 is 72.34%, validation loss is 1.0975, and validation accuracy is 60.54%. Hence, the system can provide a reliable and efficient method for monitoring disorder patients and improving their care.