research-article

Kinematic-based sedentary and light-intensity activity detection for wearable medical applications

Authors:

Sami Yli-Piipari,

Timothy W. HnatAuthors Info & Claims

MMA '14: Proceedings of the 1st Workshop on Mobile Medical Applications

Pages 28 - 33

https://doi.org/10.1145/2676431.2676433

Published: 03 November 2014 Publication History

Abstract

A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.

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Cited By

Aldenaini NAlqahtani FOrji RSampalli S(2020)Trends in Persuasive Technologies for Physical Activity and Sedentary Behavior: A Systematic ReviewFrontiers in Artificial Intelligence10.3389/frai.2020.000073Online publication date: 28-Apr-2020
https://doi.org/10.3389/frai.2020.00007
Chen XLiu CJi JPucuo CZhang W(2018)Information Collection of Motion Based on Wireless Sensor Network2018 5th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI.2018.8599481(496-500)Online publication date: Nov-2018
https://doi.org/10.1109/ICSAI.2018.8599481
Shen HYang LHe ZHu W(2018)Design and implementation of human motion information collection system2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA.2018.8397814(755-758)Online publication date: May-2018
https://doi.org/10.1109/ICIEA.2018.8397814
Show More Cited By

Index Terms

Kinematic-based sedentary and light-intensity activity detection for wearable medical applications
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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cover image ACM Conferences

MMA '14: Proceedings of the 1st Workshop on Mobile Medical Applications

November 2014

46 pages

ISBN:9781450331906

DOI:10.1145/2676431

Conference Chair:
Ayan Banerjee
Arizona State University
,
General Chair:
Sandeep K. S. Gupta
Arizona State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2014

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SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems

November 3 - 6, 2014

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Cited By

Aldenaini NAlqahtani FOrji RSampalli S(2020)Trends in Persuasive Technologies for Physical Activity and Sedentary Behavior: A Systematic ReviewFrontiers in Artificial Intelligence10.3389/frai.2020.000073Online publication date: 28-Apr-2020
https://doi.org/10.3389/frai.2020.00007
Chen XLiu CJi JPucuo CZhang W(2018)Information Collection of Motion Based on Wireless Sensor Network2018 5th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI.2018.8599481(496-500)Online publication date: Nov-2018
https://doi.org/10.1109/ICSAI.2018.8599481
Shen HYang LHe ZHu W(2018)Design and implementation of human motion information collection system2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA.2018.8397814(755-758)Online publication date: May-2018
https://doi.org/10.1109/ICIEA.2018.8397814
Su BTang QJiang JSheng MYahya AWang GCai YThalmann D(2016)A novel method for short-time human activity recognition based on improved template matching techniqueProceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 110.1145/3013971.3014004(233-242)Online publication date: 3-Dec-2016
https://dl.acm.org/doi/10.1145/3013971.3014004
Hyunchoong Kim Jonghoon Shin Soohwan Kim Yohan Ko Kyoungwoo Lee Hojung Cha Seong-il Hahm TaeJun Kwon (2016)Collaborative classification for daily activity recognition with a smartwatch2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844810(003707-003712)Online publication date: Oct-2016
https://doi.org/10.1109/SMC.2016.7844810

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