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Fine-Grained Independent Approach for Workout Classification Using Integrated Metric Transfer Learning
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Author(s): S. Rubin Bose (SRM Instıtute of Science and Technology, India), M. Abu Shahil Sirajudheen (SRM Instıtute of Science and Technology, India), G. Kirupanandan (SRM Instıtute of Science and Technology, India), S. Arunagiri (SRM Instıtute of Science and Technology, India), R. Regin (SRM Instıtute of Science and Technology, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
Copyright: 2024
Pages: 15
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch017
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Abstract
Physical activity helps manage weight and stay healthy. It becomes more critical during a pandemic since outside activities are restricted. Using tiny wearable sensors and cutting-edge machine intelligence to track physical activity can help fight obesity. This study introduces machine learning and wearable sensor methods to track physical activity. Daily physical activities are typically unstructured and unplanned, and sitting or standing may be more common than others (walking stairs upstairs down). No activity categorization system has examined how class imbalance affects machine learning classifier performance. Fitness can boost cardiovascular capacity, focus, obesity prevention, and life expectancy. Dumbbells, yoga mats, and horizontal bars are used for home fitness. Home gym-goers utilise social media to learn fitness, but its effectiveness is limited.
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