Real Time Anomaly Detection From Wearable Sensor Data

Computer Engineering

This project aims to develop a real-time anomaly prediction system utilizing wearable sensor data through machine learning techniques. Building upon our previous work in Parkinson's disease prediction from gait analysis using Hidden Markov Models (HMM), the current project focuses on extending the applicability of wearable sensor technology for early detection of anomalies in various contexts. The proposed system leverages advanced machine learning algorithms to analyze and interpret data collected from wearable sensors, enabling the identification of abnormal patterns indicative of potential health issues or irregular activities. The research contributes to the emerging field of health monitoring and anomaly detection using wearable devices, with the potential to revolutionize preventive healthcare by providing timely alerts and interventions. The proposed system builds upon the limitations of the existing approaches by developing a real-time anomaly prediction framework using wearable sensor data. The integration of advanced signal processing techniques and anomaly detection algorithms enhances the system's ability to identify abnormal patterns, facilitating early detection of potential health issues or irregular activities. The proposed system aims to provide a comprehensive solution for proactive health monitoring and timely intervention, addressing the shortcomings of the current state-of-the-art methods.

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