Deep Learning-Driven Anomaly Detection in Wearable HealthTechnology for Real-Time Patient Monitoring and Predictive Diagnostics

This paper presents the possibilities to use Long Short-Term Memory (LSTM) networks in anomaly detection of wearable health technology, which is proposed to monitor the patient in real-time and predictive diagnostics. Wearable can continuously monitor important data regarding their health, including heart rate, motion patterns, and body temperature, and be analyzed with LSTM models because these models can identify long-term dependencies in time-series data. The LSTM network is then trained using data of normal patients enabling it to learn the general patterns of health metrics across time. Any difference in these patterns is raised as anomaly and as a result, the early stage of any possible health problem like arrhythmias, unexpectedly slow heartbeats, or unnatural patterns of movement is indicated. Through the application of LSTM in real-time monitoring, medical practitioners will be able to get instant notifications on abnormal occurrences to enhance speed and accuracy of diagnosis. The approach reveals a great potential of improving wearable health technology to provide continuous patient care and active medical intervention.