IIntelligent system for managing physiological load based on IoT devices and data processing methods

With the growing popularity of wearable IoT devices for cardiovascular monitoring, their use faces the problem of measurement accuracy, especially during physical activity. This paper focuses on developing a methodology for detecting and eliminating anomalies in heart rate (HR) data collected from IoT devices to assess myocardial workload. As part of the work, an experiment was conducted in which HR data collected from wearable IoT devices (smart watches) were compared with the readings of certified medical equipment (Holter monitor). An algorithm for time series analysis is proposed, including the stages of data preprocessing, anomaly detection and correction them. Isolation forest algorithm was used to detect anomalies. The results of the study demonstrated that the proposed approach can reduce measurement error and achieve acceptable accuracy in the range of HR 90–120 beats per minute, which is critical for cardiac rehabilitation tasks. Based on the cleaned data, a model for classifying physical activity levels was developed, including recommendations for optimizing the patient's activity. The proposed methodology combines elements of system analysis, control and information processing, which makes it universal for application in intelligent health monitoring systems. The obtained results emphasize the prospects of IoT devices as a basis for building remote cardiac rehabilitation systems that can improve the quality of life of patients and reduce the burden on healthcare.

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