skip to main content
10.1145/2948963.2948964acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiwoarConference Proceedingsconference-collections
research-article

SmartMove: a smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep

Published: 23 June 2016 Publication History

Abstract

In this paper, we describe a self adapting algorithm for smart watches to define individual transitions between motion intensities. The algorithm enables for a distinction between high-amplitude motions (e.g. walking, running, or simply moving extremities) low-amplitude motions (e.g. human microvibrations, and heart rate) as well as a general doffed-state. A prototypical implementation for detecting all three motion types was tested with a wrist-worn acceleration sensor. Since the aforementioned motion types are user-specific, SmartMove incorporates a training module based on a novel actigraphy-based sleep detection algorithm, in order to learn the specific motion types. In addition, our proposed sleep algorithm enables for reduced power consumption since it samples at a very low rate. Furthermore, the algorithm can identify suitable timeframes for an inertial sensor-based detection of vital-signs (e.g. seismocardiography or ballistocardiography).

References

[1]
Anliker, U., Ward, J. A., Lukowicz, P., Tröster, G., Dolveck, F., Baer, M., ... & Belardinelli, A. (2004). AMON: a wearable multiparameter medical monitoring and alert system. In Transactions on Information Technology in Biomedicine, 8(4), (pp. 415--427). IEEE.
[2]
Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In Pervasive computing (pp. 1--17). Springer.
[3]
Bieber, G., Haescher, M., & Vahl, M. (2013, May). Sensor requirements for activity recognition on smart watches. In Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA), (p. 67). ACM.
[4]
Bulling, A., Blanke, U., & Schiele, B. (2014). A tutorial on human activity recognition using body-worn inertial sensors. In Computing Surveys (CSUR), 46(3), (p. 33). ACM.
[5]
Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., & Gillin, J. C. (1992). Automatic sleep/wake identification from wrist activity. In Sleep, 15(5), (pp. 461--469).
[6]
Gallasch, E., & Kenner, T. (1997). Characterisation of arm microvibration recorded on an accelerometer. In European journal of applied physiology and occupational physiology, 75(3), (pp. 226--232).
[7]
Haescher, M., Matthies, D. J., Trimpop, J., & Urban, B. (2015, June). A study on measuring heart-and respiration-rate via wrist-worn accelerometer-based seismocardiography (SCG) in comparison to commonly applied technologies. In Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction (iWOAR), (p. 2). ACM.
[8]
Haescher, M., Matthies, D. J., Trimpop, J., & Urban, B. (2016, May). SeismoTracker: Upgrade any Smart Wearable to enable a Sensing of Heart Rate, Respiration Rate, and Microvibrations. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, (pp. 2209--2216). ACM.
[9]
Haescher, M., Trimpop, J., Matthies, D. J., Bieber, G., Urban, B., & Kirste, T. (2015). aHead: Considering the Head Position in a Multi-Sensory Setup of Wearables to Recognize Everyday Activities with Intelligent Sensor Fusions. In Human-Computer Interaction: Interaction Technologies (pp. 741--752). Springer.
[10]
Hernandez, J., McDuff, D., & Picard, R. W. (2015, May). BioWatch: estimation of heart and breathing rates from wrist motions. In Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), (pp. 169--176). IEEE.
[11]
Jean-Louis, G., Kripke, D. F., Cole, R. J., Assmus, J. D., & Langer, R. D. (2001). Sleep detection with an accelerometer actigraph: comparisons with polysomnography. In Physiology & Behavior, 72(1), (pp. 21--28). Elsevier.
[12]
Mullaney, D. J., Kripke, D. F., & Messin, S. (1979). Wrist-actigraphic estimation of sleep time. In Sleep, 3(1), (pp. 83--92).
[13]
Rohracher, H. (1964). Microvibration, permanent muscle-activity and constancy of body-temperature. In Perceptual and motor skills, 19(1), (pp. 198--198).
[14]
Sadeh, A., Sharkey, K. M., & Carskadon, M. A. (1994). Activity-based sleep---wake identification: an empirical test of methodological issues. In Sleep, 17(3), (pp. 201--207).
[15]
Ward, J. A., Lukowicz, P., Troster, G., & Starner, T. E. (2006). Activity recognition of assembly tasks using body-worn microphones and accelerometers. In Transactions on Pattern Analysis and Machine Intelligence, 28(10), (pp. 1553--1567). IEEE.
[16]
Webster, J. B. (1982, December). An activity-based sleep monitor system for ambulatory use. In Sleep, 5(4), (pp. 389--399).

Cited By

View all
  • (2022)Smart watches: A review of evolution in bio-medical sectorMaterials Today: Proceedings10.1016/j.matpr.2021.07.46050(1053-1066)Online publication date: 2022
  • (2019)A Wearable Sleep Position Tracking System Based on Dynamic State Transition FrameworkIEEE Access10.1109/ACCESS.2019.29426087(135742-135756)Online publication date: 2019
  • (2018)Outlining a Novel Framework for Monitoring User's Vital Signs and Activity Data in Caregiving FacilitiesProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266224(1-2)Online publication date: 20-Sep-2018
  • Show More Cited By

Index Terms

  1. SmartMove: a smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      iWOAR '16: Proceedings of the 3rd International Workshop on Sensor-based Activity Recognition and Interaction
      June 2016
      63 pages
      ISBN:9781450342452
      DOI:10.1145/2948963
      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 the author(s) 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].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 June 2016

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. activity monitoring
      2. activity recognition
      3. ballistocardiography
      4. microvibration
      5. motion
      6. seismocardiography
      7. self adapting
      8. sleep
      9. smartwatch
      10. wearables

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      iWOAR '16

      Acceptance Rates

      iWOAR '16 Paper Acceptance Rate 9 of 15 submissions, 60%;
      Overall Acceptance Rate 46 of 73 submissions, 63%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)10
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 10 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)Smart watches: A review of evolution in bio-medical sectorMaterials Today: Proceedings10.1016/j.matpr.2021.07.46050(1053-1066)Online publication date: 2022
      • (2019)A Wearable Sleep Position Tracking System Based on Dynamic State Transition FrameworkIEEE Access10.1109/ACCESS.2019.29426087(135742-135756)Online publication date: 2019
      • (2018)Outlining a Novel Framework for Monitoring User's Vital Signs and Activity Data in Caregiving FacilitiesProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266224(1-2)Online publication date: 20-Sep-2018
      • (2018)Mobile Assisted LivingProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266210(1-10)Online publication date: 20-Sep-2018
      • (2018)Sleep behavior assessment via smartwatch and stigmergic receptive fieldsPersonal and Ubiquitous Computing10.1007/s00779-017-1038-922:2(227-243)Online publication date: 1-Apr-2018
      • (2017)Automatic machine status prediction in the era of Industry 4.0Journal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2017.10.00781:C(44-53)Online publication date: 1-Nov-2017

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media