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An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health Applications

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Umit Y. OgrasAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 18, Issue 5s

Article No.: 49, Pages 1 - 22

https://doi.org/10.1145/3358175

Published: 07 October 2019 Publication History

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Abstract

Human activity recognition (HAR) has recently received significant attention due to its wide range of applications in health and activity monitoring. The nature of these applications requires mobile or wearable devices with limited battery capacity. User surveys show that charging requirement is one of the leading reasons for abandoning these devices. Hence, practical solutions must offer ultra-low power capabilities that enable operation on harvested energy. To address this need, we present the first fully integrated custom hardware accelerator (HAR engine) that consumes 22.4 μJ per operation using a commercial 65 nm technology. We present a complete solution that integrates all steps of HAR, i.e., reading the raw sensor data, generating features, and activity classification using a deep neural network (DNN). It achieves 95% accuracy in recognizing 8 common human activities while providing three orders of magnitude higher energy efficiency compared to existing solutions.

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https://doi.org/10.3390/s24030999
Kaur HRani VKumar M(2024)Human activity recognition: A comprehensive reviewExpert Systems10.1111/exsy.1368041:11Online publication date: 27-Jul-2024
https://doi.org/10.1111/exsy.13680
Saddaf Khan NQadir SAnjum GUddin N(2024)StresSense: Real-Time detection of stress-displaying behaviorsInternational Journal of Medical Informatics10.1016/j.ijmedinf.2024.105401185(105401)Online publication date: May-2024
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Index Terms

An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health Applications
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware
2. Hardware

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 18, Issue 5s

Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019

October 2019

1423 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3365919

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 07 October 2019

Accepted: 01 July 2019

Revised: 01 June 2019

Received: 01 April 2019

Published in TECS Volume 18, Issue 5s

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Cited By

Manokhin MChollet PDesgreys P(2024)Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare ApplicationsSensors10.3390/s2403099924:3(999)Online publication date: 3-Feb-2024
https://doi.org/10.3390/s24030999
Kaur HRani VKumar M(2024)Human activity recognition: A comprehensive reviewExpert Systems10.1111/exsy.1368041:11Online publication date: 27-Jul-2024
https://doi.org/10.1111/exsy.13680
Saddaf Khan NQadir SAnjum GUddin N(2024)StresSense: Real-Time detection of stress-displaying behaviorsInternational Journal of Medical Informatics10.1016/j.ijmedinf.2024.105401185(105401)Online publication date: May-2024
https://doi.org/10.1016/j.ijmedinf.2024.105401
Yamin NBhat G(2023)Uncertainty-aware Energy Harvest Prediction and Management for IoT DevicesACM Transactions on Design Automation of Electronic Systems10.1145/360637228:5(1-33)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3606372
Yi MLee WHwang S(2023)A Human Activity Recognition Method Based on Lightweight Feature Extraction Combined With Pruned and Quantized CNN for Wearable DeviceIEEE Transactions on Consumer Electronics10.1109/TCE.2023.326650669:3(657-670)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3266506
Dawton BIshida SArakawa Y(2023)C-HAR: Compressive Measurement-Based Human Activity Recognition2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150315(601-607)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150315
Lattanzi ECalisti LCapellacci P(2023)Lightweight accurate trigger to reduce power consumption in sensor-based continuous human activity recognitionPervasive and Mobile Computing10.1016/j.pmcj.2023.10184896(101848)Online publication date: Dec-2023
https://doi.org/10.1016/j.pmcj.2023.101848
Ankalaki SThippeswamy M(2023)A novel optimized parametric hyperbolic tangent swish activation function for 1D-CNN: application of sensor-based human activity recognition and anomaly detectionMultimedia Tools and Applications10.1007/s11042-023-15766-383:22(61789-61819)Online publication date: 26-May-2023
https://doi.org/10.1007/s11042-023-15766-3
Bhat GHussein DYamin N(2023)Robust Machine Learning for Low-Power Wearable Devices: Challenges and OpportunitiesEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-40677-5_3(45-71)Online publication date: 7-Oct-2023
https://doi.org/10.1007/978-3-031-40677-5_3
Deb RAn SBhat GShill HOgras U(2022)A Systematic Survey of Research Trends in Technology Usage for Parkinson’s DiseaseSensors10.3390/s2215549122:15(5491)Online publication date: 23-Jul-2022
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