Development of novel techniques to classify physical activity mode using accelerometers. Pober, D., M., Staudenmayer, J., Raphael, C., & Freedson, P., S. Medicine and science in sports and exercise, 38(9):1626-34, 9, 2006. ![Development of novel techniques to classify physical activity mode using accelerometers. [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website abstract bibtex PURPOSE: Use of accelerometers to assess physical activity (PA) is widespread in public health research, but their utility is often limited by the accuracy of data-processing techniques. We hypothesized that more sophisticated approaches to data processing could distinguish between activity types based on accelerometer data, providing a more accurate picture of PA. METHODS: Using data from MTI Actigraphs worn by six subjects during four activities (walking, walking uphill, vacuuming, working at a computer), quadratic discriminant analysis (QDA) was performed, and a hidden Markov model (HMM) was trained to recognize the activities. The ability of the new analytic techniques to accurately classify PA was assessed. RESULTS: The mean (SE) percentage of time points for which the QDA correctly identified activity mode was 70.9% (1.2%). Computer work was correctly recognized most frequently (mean (SE) percent correct = 100% (0.01%)), followed by vacuuming (67.5% (1.5%)), uphill walking (58.2% (3.5%)), and walking (53.6% (3.3%)). The mean (SE) percentage of time points for which the HMM correctly identified activity mode was 80.8% (0.9%). Vacuuming was correctly recognized most frequently (mean (SE) percent correct = 98.8% (0.05%)), followed by computer work (97.3% (0.7%)), walking (62.6% (2.3%)), and uphill walking (62.5% (2.3%)). In contrast to a traditional method of data processing that misidentified the intensity level of 100% of the time spent vacuuming and walking uphill, the QDA and HMM approaches correctly estimated the intensity of activity 99% of the time. CONCLUSION: The novel approach of estimating activity mode, rather than activity level, may allow for more accurate field-based estimates of physical activity using accelerometer data, and this approach warrants more study in a larger and more diverse population of subjects and activities.
@article{
title = {Development of novel techniques to classify physical activity mode using accelerometers.},
type = {article},
year = {2006},
identifiers = {[object Object]},
keywords = {Acceleration,Activities of Daily Living,Adult,Biological,Computer-Assisted,Exercise Test,Exercise Test: classification,Exercise Test: methods,Female,Humans,Male,Markov Chains,Models,Motor Activity,Numerical Analysis,Walking},
pages = {1626-34},
volume = {38},
websites = {http://www.ncbi.nlm.nih.gov/pubmed/16960524},
month = {9},
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last_modified = {2017-10-14T23:14:07.576Z},
tags = {accelerometry},
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abstract = {PURPOSE: Use of accelerometers to assess physical activity (PA) is widespread in public health research, but their utility is often limited by the accuracy of data-processing techniques. We hypothesized that more sophisticated approaches to data processing could distinguish between activity types based on accelerometer data, providing a more accurate picture of PA. METHODS: Using data from MTI Actigraphs worn by six subjects during four activities (walking, walking uphill, vacuuming, working at a computer), quadratic discriminant analysis (QDA) was performed, and a hidden Markov model (HMM) was trained to recognize the activities. The ability of the new analytic techniques to accurately classify PA was assessed. RESULTS: The mean (SE) percentage of time points for which the QDA correctly identified activity mode was 70.9% (1.2%). Computer work was correctly recognized most frequently (mean (SE) percent correct = 100% (0.01%)), followed by vacuuming (67.5% (1.5%)), uphill walking (58.2% (3.5%)), and walking (53.6% (3.3%)). The mean (SE) percentage of time points for which the HMM correctly identified activity mode was 80.8% (0.9%). Vacuuming was correctly recognized most frequently (mean (SE) percent correct = 98.8% (0.05%)), followed by computer work (97.3% (0.7%)), walking (62.6% (2.3%)), and uphill walking (62.5% (2.3%)). In contrast to a traditional method of data processing that misidentified the intensity level of 100% of the time spent vacuuming and walking uphill, the QDA and HMM approaches correctly estimated the intensity of activity 99% of the time. CONCLUSION: The novel approach of estimating activity mode, rather than activity level, may allow for more accurate field-based estimates of physical activity using accelerometer data, and this approach warrants more study in a larger and more diverse population of subjects and activities.},
bibtype = {article},
author = {Pober, David M and Staudenmayer, John and Raphael, Christopher and Freedson, Patty S},
journal = {Medicine and science in sports and exercise},
number = {9}
}
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