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Energy-aware Tiny Machine Learning for Sensor-based Hand-washing Recognition

Authors:

Emanuele Lattanzi,

Lorenzo CalistiAuthors Info & Claims

ICMLT '23: Proceedings of the 2023 8th International Conference on Machine Learning Technologies

Pages 15 - 22

https://doi.org/10.1145/3589883.3589886

Published: 27 June 2023 Publication History

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Abstract

Tiny wearable devices are nowadays one of the most popular and used devices in everyday life. At the same time, machine learning techniques have reached a level of maturity such that they can be used in the most varied fields. The union of these two technologies represents a valuable opportunity for the development of pervasive computing applications. On the other hand, pushing the machine learning inference on a wearable device can lead to nontrivial issues. In fact, devices with small size and low-energy availability, like those dedicated to wearable platforms, pose strict computational, memory, and power requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore the trade-off between energy consumption and classification accuracy of a machine learning-based hand-washing recognition task deployed on a real wearable device. Through extensive experimental results, obtained on a public human activity recognition dataset, we demonstrated that given an identical level of classification performance, a classic SVM classifier can save about 40% of energy with respect to a more complex LSTM network. Moreover, reducing the LSTM complexity, by lowering the number of its internal unit, can make the LSTM network energy cost-effective (with a savings of about 30%) at the cost of a reduction in accuracy of only 2%.

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Cited By

Zhang TXu DAlfarraj OYu KGuizani MRodrigues J(2024)Design of Tiny Contrastive Learning Network With Noise Tolerance for Unauthorized Device Identification in Internet of UAVsIEEE Internet of Things Journal10.1109/JIOT.2024.337652911:12(20912-20929)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JIOT.2024.3376529
Calisti LLattanzi E(2024)Real-Time Energy-Efficient Sensor Libraries for Wearable DevicesIEEE Access10.1109/ACCESS.2024.343004912(126006-126018)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3430049

Index Terms

Energy-aware Tiny Machine Learning for Sensor-based Hand-washing Recognition

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ICMLT '23: Proceedings of the 2023 8th International Conference on Machine Learning Technologies

March 2023

293 pages

ISBN:9781450398329

DOI:10.1145/3589883

Copyright © 2023 ACM.

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Publication History

Published: 27 June 2023

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ICMLT 2023: 2023 8th International Conference on Machine Learning Technologies

March 10 - 12, 2023

Stockholm, Sweden

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Cited By

Zhang TXu DAlfarraj OYu KGuizani MRodrigues J(2024)Design of Tiny Contrastive Learning Network With Noise Tolerance for Unauthorized Device Identification in Internet of UAVsIEEE Internet of Things Journal10.1109/JIOT.2024.337652911:12(20912-20929)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JIOT.2024.3376529
Calisti LLattanzi E(2024)Real-Time Energy-Efficient Sensor Libraries for Wearable DevicesIEEE Access10.1109/ACCESS.2024.343004912(126006-126018)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3430049

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