Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data. Physiol Meas 2016 Mar;37(3):360-79
Date
02/11/2016Pubmed ID
26862679Pubmed Central ID
PMC5078983DOI
10.1088/0967-3334/37/3/360Scopus ID
2-s2.0-84961146387 (requires institutional sign-in at Scopus site) 26 CitationsAbstract
Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities.
Author List
Kate RJ, Swartz AM, Welch WA, Strath SJAuthor
Whitney A. Morelli PhD Assistant Professor in the Physical Medicine and Rehabilitation department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AccelerometryAlgorithms
Decision Trees
Energy Metabolism
Exercise
Female
Humans
Machine Learning
Male
Middle Aged
Models, Biological
Support Vector Machine
Time Factors
