A bag-of-words approach for assessing activities of daily living using wrist accelerometer data

Matin Kheirkhahan, Shikha Mehta, Madhurima Nath, Amal Wanigatunga, Duane B. Corbett, Todd M. Manini, Sanjay Ranka

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

Abstract

Most physical activity (PA) assessment studies use wearable accelerometers attached to the hip. However, there is significant and recent interest in understanding the usefulness of wrist based accelerometer data collection due to ease of use and higher compliance. This paper develops machine learning methods for identifying activity types and computing energy expenditures. Our approach converts the raw time series data into intermediate variables or features using standard statistical methods as well as bag-of-words (BoW) approach. We tested this approach to assess type of physical activities as well as estimate the required corresponding energy expenditure. The method was evaluated on 17 participants. Each of the participants wore an Actigraph GT3X+ accelerometer on the right wrist and performed 33 activities of daily living. Energy expenditure was measured in parallel by a portable indirect calorimetry system. Our results show that the BoW approach resulted in a more accurate model for PA identification (F1-score = 0.88 and 0.91 for sedentary and locomotion detection, respectively), compared with standard statistical summaries. The BoW approach preserved additional details about the accelerometer data, which resulted in distinguishing different activities that belonged to the same higher-level category (e.g., distinguishing leisure walk from stair ascent where both PAs belong to the locomotion class) and consequently yielding an accurate energy expenditure estimation model for PAs (rMSE = 0.93 and R2 = 0.69).

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages678-685
Number of pages8
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Conference

Conference2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Keywords

  • ActiGraph
  • Energy expenditure
  • Physical activity
  • Time series
  • Wearable

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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