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Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices
Jaehyun Park1, Ganapati Bhat2, Anish Nk2
1School of Electrical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|February 6, 2020
Summary
Researchers developed an energy-optimal method for wearable Internet of Things (IoT) devices to maximize recognized gestures within energy budgets. This approach significantly enhances the efficiency of energy-harvesting biomedical applications.
Area of Science:
- Biomedical Engineering
- Computer Science
- Energy Harvesting
Background:
- Wearable Internet of Things (IoT) devices offer diverse biomedical applications but face limitations due to battery capacity and energy consumption.
- Frequent charging requirements and user dissatisfaction are common issues with current wearable IoT devices.
Purpose of the Study:
- To develop an energy-optimal gesture recognition application for energy-harvesting wearable IoT devices.
- To maximize the number of recognized gestures under given energy budget and accuracy constraints.
Main Methods:
- Formulated an optimization problem to maximize recognized gestures.
- Derived an analytical energy model from power consumption measurements of a wearable IoT prototype.
- Proved the equivalence between maximizing recognized gestures and minimizing gesture recognition duration.
Main Results:
- Developed an optimization technique to maximize recognized gestures within energy constraints while meeting accuracy requirements.
- Validated the analytical energy model for wearable IoT applications.
- Demonstrated an increase of up to 2.4× in recognized gestures compared to manual optimization.
Conclusions:
- The proposed analytical model and optimization technique are effective for energy-constrained wearable IoT applications.
- The method significantly improves the performance of energy-harvesting devices for gesture recognition.
- This work paves the way for more efficient and potentially self-powered biomedical wearable devices.
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