TinyML-Based Embedded Electronic Devices for Intelligent Remote Patient Monitoring
DOI:
https://doi.org/10.63665/IJAICE.0204.01Keywords:
Tiny Machine Learning (TinyML), Remote Patient Monitoring, Embedded Electronic Devices, Internet of Medical Things (IoMT), Edge Artificial Intelligence, Biomedical Sensors, ECG Monitoring, SpO₂ Monitoring, Lightweight Deep Learning, Low-Power Computing, Real-Time Health Monitoring, Predictive HealthcareAbstract
The increasing prevalence of chronic diseases, growing demand for continuous healthcare services, and limitations of conventional hospital-based monitoring systems have created a need for intelligent, low-power, and remotely accessible patient monitoring solutions. Recent advancements in embedded electronics, Internet of Medical Things (IoMT), and Tiny Machine Learning (TinyML) provide opportunities to develop compact healthcare devices capable of performing real-time physiological signal analysis directly on resource-constrained hardware. This research proposes a TinyML-based embedded electronic framework for remote patient monitoring, integrating wearable biomedical sensors, microcontroller-based processing units, lightweight machine learning algorithms, and wireless communication technologies. The proposed framework focuses on monitoring essential physiological parameters, including heart rate, body temperature, blood oxygen saturation (SpO₂), and electrocardiogram (ECG) signals, enabling continuous observation of patient health conditions outside conventional clinical environments. Lightweight machine learning models are designed to identify abnormal physiological patterns through localized inference, reducing dependency on continuous cloud processing and minimizing communication overhead. Model optimization techniques, including quantization, pruning, and efficient feature extraction, are considered to accommodate memory, computational, and energy constraints of embedded devices. Furthermore, the proposed architecture incorporates secure data transmission, threshold-based emergency notifications, and cloud-assisted healthcare visualization to facilitate communication between patients and healthcare professionals. The research emphasizes the evaluation of classification performance, inference latency, memory utilization, power consumption, communication reliability, and system scalability. By integrating intelligent edge processing with embedded biomedical electronics, the proposed framework aims to establish a practical foundation for affordable, energy-efficient, and privacy-conscious remote patient monitoring, particularly in resource-constrained and geographically distributed healthcare environments.
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