Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

Saifur Rahman, Muhammad Irfan, Mohsin Raza, Khawaja Moyeezullah Ghori, Shumayla Yaqoob and Muhammad Awais
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Saifur Rahman: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Muhammad Irfan: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Mohsin Raza: Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
Khawaja Moyeezullah Ghori: Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Shumayla Yaqoob: Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Muhammad Awais: Faculty of Medicine and Health, School of Psychology, University of Leeds, Leeds LS2 9JT, UK

IJERPH, 2020, vol. 17, issue 3, 1-15

Abstract: 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.

Keywords: activities of daily living; boosting classifiers; machine learning; performance; physical activity classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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