Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data
Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study participants. Current methods for removing delivery data i...
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doaj-d5e9e83c64d84412bef03b98650f02eb2021-04-13T23:01:37ZengMDPI AGSensors1424-82202021-04-01212726272610.3390/s21082726Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry DataRyan Moore0Kristin R. Archer1Leena Choi2Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Orthopaedic Surgery, Center for Musculoskeletal Research, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USAAccelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study participants. Current methods for removing delivery data involve arduous manual review of dense datasets. We aimed to develop models for the classification of days in accelerometry data as activity from human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery. We developed statistical and machine learning models for the classification of accelerometry data in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. Model performances were assessed and compared using Monte Carlo cross-validation. We found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively. The best performing models and related data processing techniques are made publicly available in the R package, Physical Activity.https://www.mdpi.com/1424-8220/21/8/2726accelerometrystatistical learningmachine learningpredictive modelingneural networksphysical activity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ryan Moore Kristin R. Archer Leena Choi |
spellingShingle |
Ryan Moore Kristin R. Archer Leena Choi Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data Sensors accelerometry statistical learning machine learning predictive modeling neural networks physical activity |
author_facet |
Ryan Moore Kristin R. Archer Leena Choi |
author_sort |
Ryan Moore |
title |
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data |
title_short |
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data |
title_full |
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data |
title_fullStr |
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data |
title_full_unstemmed |
Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data |
title_sort |
statistical and machine learning models for classification of human wear and delivery days in accelerometry data |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-04-01 |
description |
Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study participants. Current methods for removing delivery data involve arduous manual review of dense datasets. We aimed to develop models for the classification of days in accelerometry data as activity from human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery. We developed statistical and machine learning models for the classification of accelerometry data in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. Model performances were assessed and compared using Monte Carlo cross-validation. We found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively. The best performing models and related data processing techniques are made publicly available in the R package, Physical Activity. |
topic |
accelerometry statistical learning machine learning predictive modeling neural networks physical activity |
url |
https://www.mdpi.com/1424-8220/21/8/2726 |
work_keys_str_mv |
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