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This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxial accelerometer. Time and frequency based features were extracted from two-second windows of raw accelerometer data and a subset of the features, together...
With the recent interest in physical therapy through sufficient physical activity, considerable efforts have been made to monitor and classify daily human activities, especially for people who need physical rehabilitation. In our previous study, we designed a classifier to identify 25 unique physical activities performed by 92 healthy participants between the ages of 20 and 65. In this study, with...
Human activity recognition (HAR) has many important applications in health care. While machine learning-based techniques have been applied for wearable sensor-based HAR, very few researchers have comprehensively studied the effects of various factors on the accuracy and robustness of activity classification. This paper presents a detailed empirical study of machine learning-based HAR schemes. The...
Energy expenditure (EE) estimation from accelerometer-based wearable sensors is important to generate accurate assessment of physical activity (PA) in individuals. Approaches hitherto have mainly focused on using accelerometer data and features extracted from these data to learn a regression model to predict EE directly. In this paper, we propose a novel framework for EE estimation based on statistical...
Recent popular emphasis on exercise for personal wellbeing has created a demand for techniques which monitor and classify human activities. Previous studies have shown promising results in applying various classification and feature extraction methods for identifying unique physical activities on various datasets. We apply learning techniques to GENEactiv accelerometer recordings to identify and monitor...
Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of interventions for smoking cessation, weight management, and other related health outcomes. SCT and other related theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly...
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