A machine learning approach to measure and monitor physical activity in children

Abstract

ABSTRACT-Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments.The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases.

Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People

“…It is envisaged that the prediction accuracy can be further improved by using a different set of parameters, using big data and other machine learning techniques such as deep learning to handle big data. Reference [30] applied machine learning techniques to measure and monitor physical activity in children. They evaluated Multilayer Perceptrons (MLPs), Support Vector Machines, Decision Trees, Naïve Bayes and K = 3 Nearest Neighbour algorithms.…”

Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

“…Since researchers mostly cannot know whether the forces reflected in acceleration signals are truly applied by the subject or whether the device is worn as instructed (sensor jitter, vehicular transport, and non-compliant uses), machine learning approaches have gained popularity for classifying the type [43] and the intensity [44] of PA from wearable accelerometers. Fergus et al [45] explored thoroughly several machine learning approaches and feature combinations to classify children's PA type and intensity based on wearable accelerometers achieving best performance on test data with a multilayer perceptron artificial neural network. Deep neural networks have achieved state of the art performance for the prediction of PAEE in pre-school children [46].…”

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

“…Additionally, the users' physical activities have been monitored with inertial locomotion sensors such as the accelerometer, gyroscope, and magnetometer with mechanisms to collect data to monitor people's movement [34,35,225]. Also, in [30,31,216,226]used the data collected from inertial sensors to extract the features required in recognition of human activity.…”

“…The integration of wearable technology with ML approaches is being used to identify patterns that support personalized clinical diagnoses for health care systems [229] through kNN algorithms [205] and DT [206,233,234]. The users' lifestyle was supported in physical activity recommenders based on SVM algorithms, RF [30,235,236], kNN [225], and LR [31]. Some affective recognition studies based on data collected from sensors used decision rule classifiers, and DT required in the music recommendation [12,185,188].…”