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Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

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Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
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Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
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Faculty of Medicine and Health, School of Psychology, University of Leeds, Leeds LS2 9JT, UK
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Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 1082; https://doi.org/10.3390/ijerph17031082
Received: 22 January 2020 / Revised: 5 February 2020 / Accepted: 5 February 2020 / Published: 8 February 2020
Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing. View Full-Text
Keywords: activities of daily living; boosting classifiers; machine learning; performance; physical activity classification activities of daily living; boosting classifiers; machine learning; performance; physical activity classification
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MDPI and ACS Style

Rahman, S.; Irfan, M.; Raza, M.; Moyeezullah Ghori, K.; Yaqoob, S.; Awais, M. Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living. Int. J. Environ. Res. Public Health 2020, 17, 1082.

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