Embedded system for calorie consumption estimation in gyms based on ESP32

  • Luıs Eduardo Brito Ferreira Siebra UFPE
  • João Marcelo Xavier Natário Teixeira USP / UFPE

Abstract


In Brazil, as in many parts of the world, as health and well-being become increasing priorities, the search for ways to exercise, such as joining gyms, has increased. In the case of small and medium-sized gyms, it is crucial to implement new technologies to ensure student engagement and provide a more efficient training experience. Therefore, as a way to help modernize this gym sector, this work presents a software and hardware solution that aims to democratize access to health monitoring and management tools, through a device that tracks student performance in real time and provides calorie estimates and other data as instant feedback for an app-based platform. By offering this new experience, gyms are modernizing and guaranteeing users the possibility of tracking their progress, which can generate greater motivation and personal satisfaction. The designed embedded system consists of a printed circuit board equipped with sensors, fixed to the exercise machine and is fed in real time from a database, also accessed by the created application, allowing all data captured by the device to be available in real time in the palm of the student’s hand for control and consultation.
Keywords: Embedded systems, caloric estimation, database

References

2023. Adafruit MPU6050 Library. [link]. Accessed: 2025-03-23.

2023. Adafruit Unified Sensor Library. [link]. Accessed: 2025-03-23.

Barbara A Bushman. 2023. Metabolic Calculations Cases. ACSM’s Health & Fitness Journal 27, 2 (2023), 6–10.

Mohamed Ibrahim. 2023. Drone_ESP32. [link]. Accessed: 2025-03-23.

Neville Owen, Geneviève N Healy, Charles E Matthews, and David W Dunstan. 2010. Too much sitting: the population health science of sedentary behavior. Exercise and sport sciences reviews 38, 3 (2010), 105–113.

Karen Rose, Scott Eldridge, Lyman Chapin, et al. 2015. The internet of things: An overview. The internet society (ISOC) 80, 15 (2015), 1–53.

James F Sallis, Myron F Floyd, Daniel A Rodríguez, and Brian E Saelens. 2012. Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation 125, 5 (2012), 729–737.

Sergioeabarcaf. 2023. ESP32-DHT11-Firebase. [link]. Accessed: 2025-03-23.

U.S. Department of Health and Human Services. 2018. Physical Activity Guidelines for Americans. U.S. Government Printing Office, Washington, DC. [link]

World Health Organization. 2010. Global Recommendations on Physical Activity for Health. World Health Organization, Geneva, Switzerland. [link]
Published
2025-06-03
SIEBRA, Luıs Eduardo Brito Ferreira; TEIXEIRA, João Marcelo Xavier Natário. Embedded system for calorie consumption estimation in gyms based on ESP32. In: ACM INTERNATIONAL CONFERENCE ON INTERACTIVE MEDIA EXPERIENCES WORKSHOPS (IMXW), 25. , 2025, Niterói/RJ. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 70-75. DOI: https://doi.org/10.5753/imxw.2025.1957.