IJARP

International Journal of Advanced Research and Publications (2456-9992)

High Quality Publications & World Wide Indexing!

SMART - Machine Learning Based Fitness Mobile Application

Volume 6 - Issue 5, May 2023 Edition
[Download Full Paper]

Author(s)
Santhiramohan Madhushika, Mohomed Zowrie Mohomed Akil, Nifthas A.R.M, Pirtheep GT, Ravi Supunya Swarnakantha N.H.P, Suriyaa Kumari
Keywords
Fitness, Mobile application, Workouts, Machine learning, Image processing.
Abstract
Many people don’t pay attention to their health and fitness with their busy life styles. This situation vastly increased in Sri Lanka with the economy crisis of the country. People spending a stressful life bad economy and shortage of products and services. Most people spend time in the Fuel queues, and due to the shortage of fuel, they have trouble visiting fitness centers, etc. Meantime they miss fitness guidance, motivations, and diet plans and face physical and mental troubles because of the lack of time. SMART mobile application is very helpful for fitness users who really need to improve their health. SMART includes various aspects like workout, food schedule, and gym equipment suggestion, and trainer help are covered. In today's world, mobile applications have become an indispensable tool for many people. This is especially true in countries where other forms of technology may not be as readily available. For fitness enthusiasts looking to save time and maintain their fitness goals, the SMART mobile app provides a secure and reliable solution. While the app is designed for users with basic fitness knowledge, it also provides advanced features that cater to the needs of professional fitness coaches. The SMART app leverages the power of machine learning, image processing, and the Python module to deliver a seamless user experience. With its innovative technology and user-friendly interface, the SMART app is poised to revolutionize the way people approach fitness.
References
[1]. H. -Y. Kao and Y. -J. Lee, "Design and Implement a Mobile Fitness Application based on Realtime Image Detection," 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW, Penghu, Taiwan, 2021.

[2]. Jia-He Ye, Development of App for Strength Fitness Products. 2017, Master Thesis, National Kaohsiung University of Applied Sciences, https://reurl.cc/KxXY2g .

[3]. D. Das, S. M. Busetty, V. Bharti and P. K. Hegde, "Strength Training: A Fitness Application for Indoor Based Exercise Recognition and Comfort Analysis," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.

[4]. A. Soontornwat, S. Funilkul and U. Supasitthimethee, "Essential social attributes and Habit in fitness mobile applications usage to motivate a physical activity," 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand, 2016.

[5]. V. Singh, A. Patade, G. Pawar and D. Hadsul, "trAIner - An AI Fitness Coach Solution," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022.

[6]. M. Parmar, S. Khant and A. Patel, "COVIFIT Mobile Application for Fitness Improvement during Third Wave of Covid19," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Gautam Buddha Nagar, India, 2022.

[7]. H. Haitao and D. Xinyan, "Development and Application of Undergraduate Physical Fitness and Mental Health Detection System," 2010 Second International Conference on Computer Modeling and Simulation, 2010.

[8]. R. Haji, S. Naik and R. Singh, "Fitness Tracking and Advisory Application," 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018.

[9]. X. Yu, J. Yang, L. Luo, W. Li, J. Brandt and D. Metaxas, "Customized expression recognition for performance-driven cutout character animation," 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016.

[10]. Jia-He Ye, Development of App for Strength Fitness Products. 2017, Master Thesis, National Kaohsiung University of Applied Sciences.

[11]. M. Mardi and M. R. Keyvanpour, "GBKM: A New Genetic Based KMeans Clustering Algorithm," 2021 7th In