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Artificial intelligence-enabled wearable microgrids for self-sustained energy management

2025-09-08 16:13:23 英文原文

作者:Wang, Joseph

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摘要

The document discusses advancements in wearable technology and its applications across various fields such as healthcare monitoring, activity recognition, energy harvesting, and machine learning. Below is a summary of key points from the referenced works: ### Wearable Technology for Healthcare Monitoring **Soft and Stretchable Biofuel Cells:** - **Bandodkar et al., 2017:** Developed soft, stretchable electronic skin (e-skin) based biofuel cells to scavenge energy from human sweat. **Microneedle-based Sensors:** - **Valdés-Ramírez et al., 2014:** Introduced microneedles for self-powered glucose sensors. ### Energy Harvesting and Storage Systems **Self-Charging Systems:** - **Niu et al., 2015:** Created a universal system driven by random biomechanical energy to power mobile electronics sustainably. **Flexible Piezoelectric Devices:** - **Wang et al., 2018:** Developed all-inorganic flexible piezoelectric devices for harvesting energy. **Magnetoelastic Fibers and Thermoelectrics:** - **Zhao et al., 2021:** Created soft fibers with magnetoelasticity. - **Kim et al., 2021:** Designed thermoelectric e-skin patches using reconfigurable carbon nanotube clays. **Ultraflexible Energy Systems:** - **Saifi et al., 2024:** Developed ultraflexible energy harvesting and storage systems for wearable applications. ### Advanced Batteries **Rechargeable Batteries:** - **Kumar et al., 2017:** Produced all-printed, stretchable zinc–silver oxide batteries. - **Yin et al., 2021:** Created high-performance printed silver oxide-zinc rechargeable batteries for flexible electronics. ### Machine Learning and Sensor Data Processing **Random Forest Applications:** - **Chaudhuri et al., 2018:** Used random forest to predict thermal comfort from physiological parameters. - **Phan et al., 2020:** Employed a random forest approach to quantify gait ataxia using multiple wearable sensors. **Logistic Regression and Support Vector Machines (SVM):** - **Jeppesen et al., 2023:** Utilized logistic regression for personalized seizure detection based on wearable ECG monitoring. - **Aziz et al., 2017:** Validated the accuracy of SVM-based fall detection systems using real-world datasets. **Convolutional Neural Networks (CNN) and Attention Mechanisms:** - **Al-Qaness et al., 2022:** Developed a multi-level residual network with attention for human activity recognition. - **Shen et al., 2022:** Used a multitask residual shrinkage convolutional neural network for sleep apnea detection based on wearable photoplethysmography. ### Future Trends and Innovations **In-Sensor Processing:** - **Lee et al., 2022:** Introduced in-sensor image memorization and encoding via optical neurons. **Phase-Change Memory:** - **Park et al., 2024:** Demonstrated phase-change memory using a phase-changeable self-confined nano-filament. ### Conclusion The research highlights the rapid advancement of wearable technology, with significant contributions in energy harvesting, battery development, and sophisticated machine learning algorithms for analyzing sensor data. Future work aims to integrate more efficient and miniaturized components, improve real-time processing capabilities, and enhance user experience through better power management solutions.