research-article

Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach

Authors:

Elisabetta Farella,

Gerhard Tröster,

Luca BeniniAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 11, Issue 3

Article No.: 68, Pages 1 - 30

https://doi.org/10.1145/2345770.2345781

Published: 01 September 2012 Publication History

Abstract

Wearable gesture recognition enables context aware applications and unobtrusive HCI. It is realized by applying machine learning techniques to data from on-body sensor nodes. We present an gesture recognition system minimizing power while maintaining a run-time application defined performance target through dynamic sensor selection.

Compared to the non managed approach optimized for recognition accuracy (95% accuracy), our technique can extend network lifetime by 4 times with accuracy >90% and by 9 times with accuracy >70%. We characterize the approach and outline its applicability to other scenarios.

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Chen RLuo HZhao FMeng XXie ZZhu Y(2024)A Lightweight Deep Human Activity Recognition Algorithm Using Multiknowledge DistillationIEEE Sensors Journal10.1109/JSEN.2024.344330824:19(31495-31511)Online publication date: 1-Oct-2024
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Index Terms

Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
      1. Microcomputers
2. Computing methodologies
  1. Artificial intelligence

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cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 11, Issue 3

September 2012

274 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2345770

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Copyright © 2012 ACM.

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Publication History

Published: 01 September 2012

Accepted: 01 March 2011

Revised: 01 March 2011

Received: 01 November 2009

Published in TECS Volume 11, Issue 3

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Cited By

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Foumani NTan CWebb GRezatofighi HSalehi M(2024)Series2vec: similarity-based self-supervised representation learning for time series classificationData Mining and Knowledge Discovery10.1007/s10618-024-01043-w38:4(2520-2544)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10618-024-01043-w
Zhang ZWang WAn AQin YYang F(2023)A human activity recognition method using wearable sensors based on convtransformer modelEvolving Systems10.1007/s12530-022-09480-y14:6(939-955)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s12530-022-09480-y
Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
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