Smart Patient Monitoring and Motion Tracking System - Dr. Kannimuthu S; Saurav Adhithya A M; Sri Hari G; Sriramakrishnan S; Udhaya Priyan K (International Journal of Radio Frequency Innovations, 2025)
The integration of smart technologies in healthcare has revolutionized patient monitoring and diagnostics. This paper presents a Smart Patient Monitoring and Motion Tracking System designed for hospitals, leveraging EEG (Electroencephalogram) signals to track patient movements and monitor neurological health. The proposed system combines motion tracking with real time EEG signal analysis to enhance patient safety, especially for individuals prone to seizures, neurological disorders, or other mobility-related risks. The system employs non-invasive EEG devices to capture brain activity, which is processed using advanced signal processing techniques and machine learning algorithms. Key EEG patterns associated with motion intention, sleep cycles, or abnormal neural activity are analyzed in real time. Simultaneously, motion tracking sensors integrated into wearable devices provide complementary data on patient physical movements. This dual-mode monitoring enables the system to detect critical events such as falls, convulsions, or prolonged immobility, triggering alerts for immediate medical intervention. A central feature of the system is its ability to classify and interpret EEG signals to predict potential neurological events such as epileptic seizures. Deep learning models, trained on large EEG datasets, ensure high accuracy in detecting anomalies. The combined data is transmitted to a secure cloud platform, where healthcare professionals can access and analyze patient health metrics remotely. The system also incorporates a user-friendly interface that visualizes EEG and motion data, offering real-time updates and customizable alerts. This reduces the workload on hospital staff and minimizes the risk of human error.
Keywords: Brain- computer interface (BCI); EEG (Electroencephalogram); Motion Tracking System; intensive care units (ICUs); recurrent neural networks (RNNs)