@inproceedings{76802823888848e48a1b10ce8ba55908,
title = "A bag-of-words approach for assessing activities of daily living using wrist accelerometer data",
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).",
keywords = "ActiGraph, Energy expenditure, Physical activity, Time series, Wearable",
author = "Matin Kheirkhahan and Shikha Mehta and Madhurima Nath and Wanigatunga, {Amal A.} and Corbett, {Duane B.} and Manini, {Todd M.} and Sanjay Ranka",
note = "Funding Information: ACKNOWLEDGMENTS The Metabolic Costs of Daily Activity in Older Adults Study is funded by the National Institutes of Health (NIH)/National Institute on Aging (NIA) (R01AG042525). The research is partially supported by the Data Science and Applied Technology Core of the Claude D. Pepper Older Americans Independence Centers at the University of Florida (P30AG028740). Dr. Amal Wani-gatunga is supported by NIH/NIA grants (T32AG000247 and P30AG021334). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",
year = "2017",
month = dec,
day = "15",
doi = "10.1109/BIBM.2017.8217735",
language = "English (US)",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "678--685",
editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
}