Extended Operational Life for Wearable Health Devices: A Hybrid TinyML and Server-Side ML Approach

Wearable devices equipped with sensors like ECG, PPG, and heart rate monitors are pivotal in health monitoring, yet they face significant challenges due to limited battery life, particularly when using complex machine learning (ML) models for continuous monitoring. This paper explores an innovative approach to enhance energy efficiency in wearable health monitoring applications by integrating TinyML with advanced ML models. We propose using energy-efficient TinyML models for initial binary classification to detect abnormalities in ECG signals. Upon detecting an anomaly, the data is transmitted to a server where more complex ML models, perform detailed multi-label classification to identify specific conditions. Our methodology leverages event-driven software frameworks and adaptive data collection strategies to optimize power consumption, extending the operational life of wearable devices without sacrificing accuracy. The proposed system is evaluated on several metrics, including accuracy, F1-score, energy consumption, and model latency. Our findings demonstrate that the integration of TinyML can significantly extend battery life while maintaining high levels of accuracy and reliability in health monitoring, presenting a promising solution for long-term wearable device deployment.