Explainable AI for Sensor Signal Interpretation to Revolutionize Human Health Monitoring: A Review

The complexity of sensor signal patterns in healthcare, coupled with the variability of physiological data present significant challenges in developing reliable diagnostic and monitoring. While machine learning (ML) has greatly advanced sensor-based health analysis, its decision-making processes often lack transparency, raising concerns about reliability and clinical adoption. This review explores the role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of ML models for sensor signal analysis, particularly in applications such as pressure sensors, wearable inertial sensors, imaging sensors (MRI, CT, X-ray), ECG, EEG, and wearable health tracking. A systematic literature review was conducted across multiple databases to identify studies applying XAI techniques to sensor-based health monitoring. The review categorizes nine trending XAI methods used to interpret ML-driven analyses of biosignals, evaluating their advantages and limitations in different healthcare scenarios. The findings emphasize the importance of transparency in ML-driven sensor analysis, which is critical for building trust and real-time clinical decision-making and wearable healthcare applications. Despite its potential, XAI for sensor signals faces challenges related to model scalability, real-time processing, and clinician interpretability. The review identifies key research gaps in integrating XAI into sensor-based healthcare systems, emphasizing the need for robust validation methods and user-friendly explanations. Ultimately, XAI offers a promising path toward revolutionizing sensor-driven health monitoring, though further advancements are necessary to fully integrate explainability into real-world clinical and assistive applications.