Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

Rahman, Saifur; Irfan, Muhammad; Raza, Mohsin; Moyeezullah Ghori, Khawaja; Yaqoob, Shumayla; Awais, Muhammad

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.

Citation

Rahman, S., Irfan, M., Raza, M., Moyeezullah Ghori, K., Yaqoob, S., & Awais, M. (2020). Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living. International Journal of Environmental Research and Public Health, 17(3), Article 1082. https://doi.org/10.3390/ijerph17031082

• Journal Article Type: Article
• Acceptance Date: Feb 5, 2020
• Online Publication Date: Feb 8, 2020
• Publication Date: Feb 1, 2020
• Deposit Date: Jun 19, 2020
• Publicly Available Date: Jun 22, 2020
• Journal: International Journal of Environmental Research and Public Health
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 3
• Article Number: 1082
• DOI: https://doi.org/10.3390/ijerph17031082
• Keywords: Activities of daily living; Boosting classifiers; Machine learning; Performance; Physical activity classification
• Public URL: https://hull-repository.worktribe.com/output/3502611
• Publisher URL: https://www.mdpi.com/1660-4601/17/3/1082