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Simple classification of walking activities using commodity smart phones

Published: 23 November 2009 Publication History

Abstract

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.

References

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Ainsworth, B., Haskell, W., Whitt, M., Irwin, M., Swartz, A., Strath, S., et al. (2000). Compendium of physical activities: classification of energy costs of human physical activities. Medicine & Science in Sports & Exercise, 32 (9), 498--516.
[2]
Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. Proceedings of Pervasive 2004: the Second International Conference on Pervasive Computing (pp. 1--17). Vienna, Austria: Springer.
[3]
Franke, T. (2008). Context Logger. Retrieved March 4, 2009, from http://contextlogger.blogspot.com/
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Lester, J., Hurvitz, P., Chaudhri, R., Hartung, C., & Borriello, G. (2008). MobileSense - sensing modes of transportation in studies of the built environment. UrbanSense08, (pp. 46--50). Raleigh, North Carolina.
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Saponas, T., Lester, J., Froehlich, J., Fogarty, J., & Landay, J. (2008). iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones. UW-CSE-08-04-02 Tech Report.
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Ravi, N., Dandekar, N., Preetham, M., & Littman, M. (2005). Activity recognition from accelerometer data. IAAI-05 (pp. 1541--1546). America: AAAI.
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Yi, J., Choi, Y., Jacko, J., & Sears, A. (2005). Context awareness via a single device-attached accelerometer during mobile computing. MobileHCI'05 (pp. 303--306). ACM.

Cited By

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  • (2020)A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile DevicesHandbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's10.1007/978-3-030-40305-8_33(685-713)Online publication date: 9-Feb-2020
  • (2016)Step and activity detection based on the orientation and scale attributes of the SURF algorithm2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN.2016.7743645(1-8)Online publication date: Oct-2016
  • (2015)Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation StudiesJMIR mHealth and uHealth10.2196/mhealth.35053:2(e36)Online publication date: 15-Apr-2015

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cover image ACM Other conferences
OZCHI '09: Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7
November 2009
445 pages
ISBN:9781605588544
DOI:10.1145/1738826
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 November 2009

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Author Tags

  1. accelerometer
  2. activity classification
  3. context-aware
  4. mobile computing
  5. sensor technology
  6. ubiquitous computing
  7. user experience
  8. user interface

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  • Research-article

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OZCHI '09

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OZCHI '09 Paper Acceptance Rate 32 of 60 submissions, 53%;
Overall Acceptance Rate 362 of 729 submissions, 50%

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Cited By

View all
  • (2020)A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile DevicesHandbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's10.1007/978-3-030-40305-8_33(685-713)Online publication date: 9-Feb-2020
  • (2016)Step and activity detection based on the orientation and scale attributes of the SURF algorithm2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN.2016.7743645(1-8)Online publication date: Oct-2016
  • (2015)Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation StudiesJMIR mHealth and uHealth10.2196/mhealth.35053:2(e36)Online publication date: 15-Apr-2015

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