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AIR: recognizing activity through IR-based distance sensing on feet

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Jianfei ShenAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 97 - 100

https://doi.org/10.1145/2968219.2971447

Published: 12 September 2016 Publication History

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Abstract

In this paper, we describe the results of a controlled experiment measuring everyday movement activity through a novel recognition prototype named AIR. AIR measures distance from the feet using infrared (IR) sensors. We tested this approach for recognizing six prevalent activities: standing stationary, walking, running, walking in place, going upstairs, and going downstairs and compared results to other commonly used approaches. Our results show that AIR obtains much higher accuracy in recognizing activity than approaches that rely primarily on accelerometers. Moreover, AIR has good generalization ability when applying recognition model to new users.

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Cited By

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Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Dewan AGunturi VNaik VDutta K(2021)NEAT Activity Detection using Smartwatch at Low Sampling Frequency2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)10.1109/SWC50871.2021.00014(25-32)Online publication date: Oct-2021
https://doi.org/10.1109/SWC50871.2021.00014
Hu LHu CJiang XHuo Z(2021)Adaptive coefficient-based kernelized network for personalized activity recognitionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-021-01455-w13:1(269-291)Online publication date: 26-Oct-2021
https://doi.org/10.1007/s13042-021-01455-w
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Index Terms

AIR: recognizing activity through IR-based distance sensing on feet
1. Human-centered computing

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Published In

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

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Poster

Funding Sources

National Natural Science Foundation of China
Beijing Municipal Science & Technology Commission
Science and Technology Planning Project of Guangdong Province, China
Research Foundation of Ministry of Education and China Mobile

Conference

UbiComp '16

Sponsor:

UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all

Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Dewan AGunturi VNaik VDutta K(2021)NEAT Activity Detection using Smartwatch at Low Sampling Frequency2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)10.1109/SWC50871.2021.00014(25-32)Online publication date: Oct-2021
https://doi.org/10.1109/SWC50871.2021.00014
Hu LHu CJiang XHuo Z(2021)Adaptive coefficient-based kernelized network for personalized activity recognitionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-021-01455-w13:1(269-291)Online publication date: 26-Oct-2021
https://doi.org/10.1007/s13042-021-01455-w
de Pinho André RRaposo AFuks H(2019)A Platform for Assessing Physical Education Activity EngagementIntelligent Human Systems Integration 201910.1007/978-3-030-11051-2_42(271-276)Online publication date: 6-Jan-2019
https://doi.org/10.1007/978-3-030-11051-2_42
Wang WHarari GWang RMüller SMirjafari SMasaba KCampbell A(2018)Sensing Behavioral Change over TimeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649512:3(1-21)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3264951
De Pinho André RDiniz PFuks H(2017)Bottom-up InvestigationProceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3134230.3134240(1-6)Online publication date: 21-Sep-2017
https://dl.acm.org/doi/10.1145/3134230.3134240
Liu YZhao WLiu QYu LWang D(2017)HL-HAR: Hierarchical Learning Based Human Activity Recognition in Wearable ComputingCloud Computing and Security10.1007/978-3-319-68542-7_59(684-693)Online publication date: 2-Nov-2017
https://doi.org/10.1007/978-3-319-68542-7_59

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