PURPOSE: Pattern recognition approaches to accelerometer data processing have emerged as viable alternatives to cut-point methods. However, few studies have explored the validity of pattern recognition approaches in preschoolers, and none have compared supervised learning algorithms trained on hip and wrist data. Purpose of this study was to develop, test, and compare activity class recognition algorithms trained on hip, wrist, and combined hip and wrist accelerometer data in preschoolers. METHODS: Eleven children 3-6 yr of age (mean age, 4.8 ± 0.9 yr) completed 12 developmentally appropriate physical activity (PA) trials while wearing an ActiGraph GT3X+ accelerometer on the right hip and nondominant wrist. PA trials were categorized as sedentary, light activity games, moderate-to-vigorous games, walking, and running. Random forest (RF) and support vector machine (SVM) classifiers were trained using time and frequency domain features from the vector magnitude of the raw signal. Features were extracted from 15-s nonoverlapping windows. Classifier performance was evaluated using leave-one-out cross-validation. RESULTS: Cross-validation accuracy for the hip, wrist, and combined hip and wrist RF models was 0.80 (95% confidence interval (CI), 0.79-0.82), 0.78 (95% CI, 0.77-0.80), and 0.82 (95% CI, 0.80-0.83), respectively. Accuracy for hip, wrist, and combined hip and wrist SVM models was 0.81 (95% CI, 0.80-0.83), 0.80 (95% CI, 0.79-0.80), and 0.85 (95% CI, 0.84-0.86), respectively. Recognition accuracy was consistently excellent for sedentary (>90%); moderate for light activity games, moderate-to-vigorous games, and running (69%-79%); and modest for walking (61%-71%). CONCLUSIONS: Machine learning algorithms such as RF and SVM are useful for predicting PA class from accelerometer data collected in preschool children. Although classifiers trained on hip or wrist data provided acceptable recognition accuracy, the combination of hip and wrist accelerometer delivered better performance.
PURPOSE: Pattern recognition approaches to accelerometer data processing have emerged as viable alternatives to cut-point methods. However, few studies have explored the validity of pattern recognition approaches in preschoolers, and none have compared supervised learning algorithms trained on hip and wrist data. Purpose of this study was to develop, test, and compare activity class recognition algorithms trained on hip, wrist, and combined hip and wrist accelerometer data in preschoolers. METHODS: Eleven children 3-6 yr of age (mean age, 4.8 ± 0.9 yr) completed 12 developmentally appropriate physical activity (PA) trials while wearing an ActiGraph GT3X+ accelerometer on the right hip and nondominant wrist. PA trials were categorized as sedentary, light activity games, moderate-to-vigorous games, walking, and running. Random forest (RF) and support vector machine (SVM) classifiers were trained using time and frequency domain features from the vector magnitude of the raw signal. Features were extracted from 15-s nonoverlapping windows. Classifier performance was evaluated using leave-one-out cross-validation. RESULTS: Cross-validation accuracy for the hip, wrist, and combined hip and wrist RF models was 0.80 (95% confidence interval (CI), 0.79-0.82), 0.78 (95% CI, 0.77-0.80), and 0.82 (95% CI, 0.80-0.83), respectively. Accuracy for hip, wrist, and combined hip and wrist SVM models was 0.81 (95% CI, 0.80-0.83), 0.80 (95% CI, 0.79-0.80), and 0.85 (95% CI, 0.84-0.86), respectively. Recognition accuracy was consistently excellent for sedentary (>90%); moderate for light activity games, moderate-to-vigorous games, and running (69%-79%); and modest for walking (61%-71%). CONCLUSIONS: Machine learning algorithms such as RF and SVM are useful for predicting PA class from accelerometer data collected in preschool children. Although classifiers trained on hip or wrist data provided acceptable recognition accuracy, the combination of hip and wrist accelerometer delivered better performance.
Authors: Matthew Ahmadi; Margaret O'Neil; Maria Fragala-Pinkham; Nancy Lennon; Stewart Trost Journal: J Neuroeng Rehabil Date: 2018-11-15 Impact factor: 4.262
Authors: Soyang Kwon; Patricia Zavos; Katherine Nickele; Albert Sugianto; Mark V Albert Journal: Int J Environ Res Public Health Date: 2019-07-21 Impact factor: 3.390
Authors: Matthew N Ahmadi; Margaret E O'Neil; Emmah Baque; Roslyn N Boyd; Stewart G Trost Journal: Sensors (Basel) Date: 2020-07-17 Impact factor: 3.576
Authors: Antonio García-Domínguez; Carlos E Galvan-Tejada; Laura A Zanella-Calzada; Hamurabi Gamboa; Jorge I Galván-Tejada; José María Celaya Padilla; Huizilopoztli Luna-García; Jose G Arceo-Olague; Rafael Magallanes-Quintanar Journal: PeerJ Comput Sci Date: 2020-11-09