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Rahman, Saifur, Irfan, Muhammad, Raza, Mohsin, Ghori, Khawaja Moyeezullah, Yaqoob, Shumayla and Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245
(2020)
Performance analysis of boosting classifiers in recognizing activities of daily living.
International Journal of Environmental Research and Public Health, 17 (3).
ISSN 1660-4601
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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.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding information: This research got funded by Deanship of Scientific Research, Najran University, Saudi Arabia, for the award of research fund NU/ESCI/16/104. |
| Uncontrolled Keywords: | activities of daily living,boosting classifiers,machine learning,performance,physical activity classification,public health, environmental and occupational health,pollution,health, toxicology and mutagenesis,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2739 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:45 |
| Last Modified: | 24 Oct 2023 01:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93312 |
| DOI: | 10.3390/ijerph17031082 |
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