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Batteryless sensing devices which rely on energy harvesting can enable more sustainable and long-lasting Internet of Things (IoT) based wearables. While it has become feasible to implement energy-harvesting based wearables for digital health applications, it remains challenging to integrate such devices and the data they collect into machine learning pipelines for tasks such as human activity recognition (HAR). A key obstacle is uncertainty in the data acquisition process. Given the discontinuous and uncertain availability of harvested energy, when should a sensor spend energy to sample and transmit data packets for processing? A common approach is to spend energy opportunistically by sending packets whenever sufficient energy is available. However, when considering a specific task, namely HAR with kinetic energy harvesting based sensors, this approach unfairly prioritizes data from activities where more energy can be harvested (e.g., running). In this work, we improve the opportunistic energy spending policy by pruning redundant packets to reallocate energy towards activities where less energy is harvested. Our approach results in an increase in the F1-score of ‘lower energy’ activities while having a minimal impact on the F1-score of ‘higher energy’ activities.
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This content will become publicly available on June 12, 2026
Workshop on AI integration with Renewable Energy harvesting sources
The potential of energy harvesting and batteryless sensing promises a future where IoT devices will become sustainable, long-lasting, and maintenance free. Given the significant challenges involved with building and programming such devices, it is reasonable to question whether energy harvesting based IoT can replace existing wearables (fitness trackers, smartwatches, or medical devices), while providing a reliable user experience. Hence, an important question arises: “What role can energy harvesting based sensing play in the age of AI and deep learning?” While energy harvesting based sensors can unlock new applications in wearables and personalized data analytics, the path towards integrating them into the modern deep learning landscape requires substantial intellectual innovation. This workshop aims to bridge multiple perspectives in wearable sensing and data analytics in the modern age of AI.
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- Award ID(s):
- 2428656
- PAR ID:
- 10599923
- Publisher / Repository:
- https://bsn.embs.org/2024/program/workshops/
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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