JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

eISSN: 2980-177X
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Original Article
Personalized daily-weekly workout arrangement application "cardio fit"
Abstract
Having mobile applications has become an indispensable part of our lives in recent years. There are many mobile applications that are already being used in the field of fitness. By using these apps, people can lead a more organized and planned lifestyle. However, many apps fail to offer personalized programs for individuals. This makes mobile applications an inefficient method for users to reach their goals. Our mobile application aims to enable users to reach their fitness level effectively and quickly with a program suitable for their body type, physical characteristics, training goals and exercise preferences. Body mass indexes are calculated based on the body measurements that users enter and their maximum oxygen capacity is determined according to their age, the user chooses their fitness goals, selects the equipment they will train with, and indicates whether they have heart rate monitors. Based on this, the appropriate program is presented to the user. This app is not suitable for users over 50 years old. Users have two goal options for the app: “Feeling Good – Breaking a Sweat” and “Achieving Fitness Level (Firming-Fat Burning).” In the “Reach Fitness Level” option, a tightening program is recommended if the user’s weight is above their ideal weight, while a fat burning program is recommended for users who are significantly above their ideal weight. The Kotlin programming language was used to develop this application, and the Firebase database was used to speed up the development process to ensure the effectiveness of fitness programs, the cardio program was provided by a fitness trainer who is an expert in the field. This app retrieves the necessary user information from the database and provides them with the most suitable fitness program. As a result, users can achieve their goals without needing a fitness trainer or going to the gym.

Keywords : Mobile app, kotlin, firebase, fitness, fitness app

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1. Farrokhi, A., Farahbakhsh, R., Rezazadeh, J., & Minerva, R. (2021).Application of Internet of Things and artificial intelligence for smartfitness: A survey. Computer Networks, 189, 107859.
2. Azar, K.M., Lesser, L.I., Laing, B.Y., Stephens, J., Aurora, M.S., Burke, L.E., andPalaniappan, L.P. (2013). Weight management for mobile apps: theory-basedcontent analysis. American journal of preventive medicine, 45(5), 583-589.
3. Dabbish, L., Stuart, C., Tsay, J., and Herbsleb, J. (2012). Social in GitHubcoding: transparency and open collaboration software repository.Computer-Aided ACM 2012 conference Proceedings in CooperativeWork (pp. 1277-1286).
4. Direito, A., Carraça, E., Rawstorn, J., Whittaker, R., Maddison, R., andOlds, T. (2017). Technologies influencing mHealth physical activityand sedentary behaviors: behavior modification techniques, systematicreview and randomized meta-analysis of controlled trials BehavioralChronicles Medicine, 51(2), 226-239-348.
5. Fanning, J., Mullen, S.P., McAuley, E. (2012). Increased physical activitywith mobile devices: A meta-analysis. Journal of Medical InternetResearch, 14(6), e161.
6. Fitbit: Activity and Health Trackers. (Accessed 19 May 2023). AccessAddress: https://www.fitbit.com/global/us/home
7. Google Fit: Health & Fitness Tracking App (Accessed May 19, 2023).Access Address: https://www.google.com/fit/
8. Comparison Java to Kotlin (Accessed 19 May 2023). https://kotlinlang.org/docs/reference/comparison-to-java.html
9. Firebase Realtime Database (Accessed 19 May 2023). https://firebase.google.com/products/realtime-database
10. Getting Started with Firebase Authentication: A Comprehensive Guide(Accessed 19 May 2023) https://dev.to/shahabmalikk/getting-started-with-firebase-authentication-a comprehensive-guide-1j81
11. Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D.M.,and Damian, D. (2014). these promises and the dangers of GitHubmining. Proceedings of the 11th Mining Software Pools WorkingConference (pp. 92-101).
12. Martin, S.S., Feldman, D.I., Blumenthal, R.S., Jones, S.R., Post, W.S.,& McKibben, R.A. (2015). mActive: A clinical trial that randomizedautomation interfered with mHealth physical activity promotion.Journal American Heart Association, 4(11), e002239.
13. MyFitnessPal: Calorie counter and nutrition tracking app. (Accessed 19May 2023). Access Address: https://www.myfitnesspal.com/en
14. Nike Training Club: NTC App. (Accessed May 19, 2023). AccessAddress: https://www.nike.com/en/ntc-app
15. Peterson, D., & Duncan, D.P. (2017). Development and availability ofevidence-based testing to improve decision-making aid informationfor tablet devices and promote positive health behavior change. JMIRresearch protocols, 6(11), e225.
16. Seven: Fitness and Workout App. (Accessed May 19, 2023). AccessAddress: https://seven.app/
17. Smith, J.J., Morgan, PJ, Plotnikoff, R.C., Dally, KA, Salmon, J.,Okely, A.D., and Lubans, D.M. (2016). Adolescent low-income boys’populations for smartphone obesity prevention trial: ATLAS RCT.Paediatrics, 137(3), e20153144.
18. Strava: Sports Tracking and Sharing App. (Accessed 19 May 2023).Access Address: https://www.strava.com/mobile
19. Trust in Collaborative Automation in High Stakes SoftwareEngineering Work: A Case Study at NASA (Accessed 19 May 2023)https://dl.acm.org/doi/10.1145/3411764.3445650
20. Wang, J.B., Cadmus-Bertram, L.A., Natarajan, L., White, M.M.,Madanat, H., Nichols, J.F., & Ayala, GX (2016). Wearable sensor/device(Fitbit One) and SMS text messaging seek to boost physical overweightactivity and obese adults: randomized controlled. Trial of Telemedicineand eHealth, 22(5), 345-352.
Article available in :
Volume 1, Issue 2, 2023
Page : 25-28
https://doi.org/10.51271/JCEEES-0006
Article Information
Received : 13 Tem 2023
Accepted : 21 Ağu 2023
Published : 31 Eki 2023
Corresponding Author :

Zülal Ataman: Gazi Üniversitesi

[email protected]
Citation : Ataman Z, Heybet ME, Sarısoy F, Yılmaz B, Günay M, Şenel Ö. Personalized daily-weekly workout arrangement application “cardio fit”. J Comp Electr Electron Eng Sci. 2023;1(2):25-28.

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