Determining the single best axis for exercise repetition recognition and counting on smartwatches

Bobak Jack Mortazavi*, Mohammad Pourhomayoun, Gabriel Alsheikh, Nabil Alshurafa, Sunghoon Ivan Lee, Majid Sarrafzadeh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

Due to the exploding costs of chronic diseasesstemming from physical inactivity, wearable sensor systems toenable remote, continuous monitoring of individuals has increasedin popularity. Many research and commercial systems exist inorder to track the activity levels of users from general dailymotion to detailed movements. This work examines this problemfrom the space of smartwatches, using the Samsung GalaxyGear, a commercial device containing an accelerometer and agyroscope, to be used in recognizing physical activity. This workalso shows the sensors and features necessary to enable suchsmartwatches to accurately count, in real-time, the repetitions offree-weight and body-weight exercises. The goal of this work isto try and select only the best single axis for each activity byextracting only the most informative activity-specific features, inorder to minimize computational load and power consumptionin repetition counting. The five activities are incorporated in aworkout routine, and knowing this information, a random forestclassifier is built with average area under the curve (AUC) of: 974, with average accuracy of 93%, in cross validation to identify eachrepetition of a given exercise using all available sensors and AUCof: 950 with accuracy of 89:9% using the single best axis foreach activity alone. Adding a gyroscope with the accelerometerincreased the average AUC from: 968 to: 974, increasing theaccuracy of specific movements as much as 2%. Results show that, while a combination of accelerometer and gyroscope provide thestrongest classification results, often times features extracted froma single, best axis are enough to accurately identify movementsfor a personal training routine, where that axis is often, but notalways, an accelerometer axis.

Original languageEnglish (US)
Title of host publicationProceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014
PublisherIEEE Computer Society
Pages33-38
Number of pages6
ISBN (Print)9781479949328
DOIs
StatePublished - Jan 1 2014
Event11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014 - Zurich, Switzerland
Duration: Jun 16 2014Jun 19 2014

Other

Other11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014
CountrySwitzerland
CityZurich
Period6/16/146/19/14

Keywords

  • Activity Recognition
  • Exercise Recognition
  • Repetition Counting
  • SmartWatch
  • Wireless Health

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Networks and Communications

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