Mapping explainability and energy efficiency in TinyML-based real-time health monitoring using wearable and Internet of medical things devices: A scoping review

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Original abstract

Wearable and Internet of Medical Things devices increasingly support continuous health monitoring, but cloud-dependent analytics remain constrained by latency, connectivity, privacy, and battery requiirements. Tiny machine learning shifts inference toward resource-constrained microcontrollers and edge processors; however, its clinical value depends not only on predictive accuracy but also on energy efficiency, real-time responsiveness, and understandable decision logic. This scoping review mapped the evidence on explainability and energy efficiency in TinyML-based health monitoring. Following the Joanna Briggs Institute approach and PRISMA guidance for scoping reviews, Scopus was searched in the title, abstract, and keyword fields for studies published from 2020 to 2025. The search combined TinyML and embedded or edge artificial intelligence terms with wearable or Internet of Medical Things concepts, health-monitoring applications, and explainability or efficiency terms. The supplied export contained 293 records. After screening, 39 reports underwent eligibility assessment and 36 studies were included. Publication activity accelerated sharply, with 18 studies published in 2025. The evidence covered cardiac monitoring, human activity and fall detection, neurological and affective assessment, respiratory monitoring, signal-quality control, glucose sensing, gait analysis, and smart textiles. Convolutional neural networks and hybrid deep models were common, while deployment platforms ranged from microcontrollers to field-programmable gate arrays, application-specific integrated circuits, and neuromorphic hardware. Quantization, pruning, binary or ternary computation, feature reduction, event-driven processing, and local transmission control were frequently used to reduce resource demand. In contrast, only a small minority of studies explicitly evaluated explainability through model-based feature selection, feature importance, or class activation maps. The field is therefore energy-aware but not yet consistently explanation-aware. Future research should adopt standardized hardware reporting, clinician-centered explanation evaluation, external and longitudinal validation, and multiobjective optimization that jointly considers clinical accuracy, energy, latency, memory, robustness, and interpretability

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• Official / arXiv page

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