Low-Power AI Model Optimization for Wearable Health Monitoring Applications

Abstract

Wearable health monitoring devices have emerged as crucial tools for continuous tracking of vital physiological parameters such as electrocardiogram (ECG), heart rate, and oxygen saturation. With the integration of artificial intelligence (AI), these devices can provide real-time analysis and early detection of health anomalies. However, the constrained computational resources and limited battery capacity of wearable devices pose significant challenges for deploying deep learning models. This paper proposes a comprehensive framework for low-power AI model optimization tailored for wearable health monitoring applications. The framework employs quantization, pruning, and adaptive sampling to minimize computational load while maintaining high diagnostic accuracy. Experimental evaluations on public health datasets (PhysioNet, MIMIC-III) demonstrate up to 45% reduction in energy consumption with less than 2% accuracy degradation. The results highlight the potential of optimized AI models to enable longer battery life and efficient, real-time inference on wearable platforms, thus advancing the field of mobile health (mHealth) technologies.

Introduction

Wearable health devices (e.g., smartwatches, fitness bands) have evolved into intelligent health monitors, tracking vital signs like ECG, PPG, SpO?, HRV, and respiration. With AI integration, they now offer predictive and diagnostic capabilities such as:

• Atrial fibrillation detection

• Stress level identification

• Early detection of chronic diseases

However, deploying AI models on wearable devices is challenging due to limited memory, processing power, and battery life.

2. Challenges in Deploying AI on Wearables

• Deep learning models (CNNs, LSTMs, Transformers) are resource-intensive.

• Running models locally leads to:

  • High latency

  • Battery drain

  • Thermal throttling

• Cloud-based inference introduces:

  • High latency (due to round-trip communication)

  • Connectivity dependence

  • Privacy risks (violations of HIPAA/GDPR)

3. Solution: On-Device AI Inference

• On-device AI enables:

  • Real-time analysis

  • Offline functionality

  • Enhanced privacy

• Needs specialized optimization to be viable on constrained hardware:

  • Low memory (e.g., <1GB RAM)

  • Low-power microcontrollers

4. Motivation

• TinyML and Edge AI offer promising techniques:

  • Quantization

  • Pruning

  • Knowledge Distillation (KD)

  • Neural Architecture Search (NAS)

• But wearable health monitoring demands higher accuracy and energy-efficiency than other domains (e.g., vision or speech), making conventional trade-offs less acceptable.

5. Research Gaps

• Most AI models focus on accuracy, not power efficiency.

• Optimizations are not tailored for biomedical time-series data (e.g., ECG, PPG).

• Lack of integrated optimization pipelines (e.g., combining quantization + pruning + KD).

• Few evaluations on real wearable hardware (microcontroller-based, not smartphones).

6. Contributions of This Work

This paper proposes a hybrid low-power AI optimization framework for wearables:

• Combines: Quantization + Structured Pruning + Adaptive Sampling

• Introduces: Mathematical models to balance accuracy vs. power

• Evaluated on: Public datasets (e.g., PhysioNet, MIMIC-III) and Cortex-M4F prototypes

• Results:

  • Up to 45% energy reduction

  • <2% drop in classification accuracy

7. Optimization Techniques Reviewed

• Trade-offs: Designers must balance accuracy (A), energy (E), latency (L), and size (S) using constrained optimization:

  minE(θ)subjecttoA(θ)≥Amin,L(θ)≤Lmax,S(θ)≤Smaxmin E(θ) subject to A(θ) ≥ A_min, L(θ) ≤ L_max, S(θ) ≤ S_max minE(θ)subjecttoA(θ)≥Am?in,L(θ)≤Lm?ax,S(θ)≤Sm?ax

8. Deployment Tools & Acceleration

• Software stacks:

  • TensorFlow Lite for Microcontrollers

  • ARM CMSIS-NN

  • ONNX Runtime Mobile

• Hardware accelerators:

  • Tiny NPUs, ARM Ethos, Edge TPU (rarely used in ultra-low-power wearables)

• Software-level optimization remains key for mainstream wearable devices.

9. Privacy & Federated Learning

• Federated Learning (FL) helps preserve privacy by keeping data on-device.

• Challenges:

  • Battery constraints

  • Intermittent connectivity

  • Client diversity

• Emerging focus on energy-aware FL protocols, compressed updates, and personalized models.

10. Open Research Questions

• Few real-device evaluations on constrained wearables.

• Optimized multi-modal fusion of sensors (e.g., ECG + PPG) is underexplored.

• No mature frameworks for joint optimization (NAS + KD + quantization + pruning).

• Federated Learning for wearables needs further development.

Conclusion

This paper presented a low-power AI optimization framework for wearable health monitoring devices, addressing the critical challenges of energy consumption, computational limitations, and privacy concerns. By integrating quantization, pruning, knowledge distillation, adaptive sampling, and federated learning, the framework achieved significant reductions in latency and energy consumption while maintaining clinically acceptable accuracy across benchmark datasets such as PhysioNet, MIMIC-III, and WESAD.

References

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Copyright

Copyright © 2025 Manu Kumar Misra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.