AI-Based Coma Patient Movement Monitoring

Coma patients require continuous observation because even small body movements may provide useful information about their condition. However, continuously monitoring patients manually can be difficult for healthcare workers, especially when several patients need attention at the same time. To address this problem, this paper presents an AI-based coma patient movement monitoring and alert system. The system uses computer vision and deep learning techniques to monitor and analyze patient movements. A camera is used to continuously capture the patient's movements, and Media Pipe Pose is used to identify important body landmarks from the video frames. The extracted landmark information is processed by a Long Short-Term Memory (LSTM) model to recognize movement patterns over time. When a significant or abnormal movement is detected, the system generates an alert so that the caregiver can check the patient immediately. A monitoring dashboard is also provided to display the patient's current movement status and maintain movement records for further observation. The proposed system provides a non-contact method for continuous patient monitoring and can help reduce the effort required for manual observation. It can also support healthcare professionals by providing timely movement information and alerts, particularly in situations where continuous direct supervision is not possible. The interface can be accessed by an authorized doctor through the provided public IP address, allowing the doctor to view the patient's movement remotely without being physically present near the patient. This can be useful when the doctor is located in another room or at a different location and needs to check the patient's current movement status. The system also includes an automatic email alert mechanism. When movement is detected, the system automatically captures a screenshot of the event and sends it to the doctor's registered email address along with an alert message. The screenshot provides visual information about the detected event, allowing the doctor to understand what occurred and decide whether further attention is required. This feature helps reduce the possibility of important movements being missed during continuous monitoring. The proposed system therefore combines real-time movement detection, AI-based analysis, remote access, and email-based notification into a single monitoring platform. The system is designed to assist healthcare professionals rather than replace medical judgment.