A Comprehensive Framework for Caloric Expenditure Estimation Utilizing Supervised Learning Techniques and Regression-Based Algorithms
Dharmaiah Devarapalli1, Yannam Satya Amrutha2, Bala Satya Sri Pasupuleti3, Srujana Maddula4, Hema Sri Puppala5
1Dr. Devarapalli Dharamaiah, Department of Computer Science and Engineering, Koreru Lakshmaiah Educational Foundation, Vaddeswaram (Andhra Pradesh), India.
2Ms. Yannam Satya Amrutha, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram (Andhra Pradesh), India.
3Ms. Bala Satya Sri Pasupuleti, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram (Andhra Pradesh), India.
4Ms. Srujana Maddula, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram (Andhra Pradesh), India.
5Ms. Hema Sri Puppala, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 12 October 2024 | Revised Manuscript received on 26 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 36-40 | Volume-14 Issue-5, November 2024 | Retrieval Number: 100.1/ijsce.L931911111222 | DOI: 10.35940/ijsce.L9319.14051124
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the increasing importance of health and well-being in today’s culture, exercise is becoming a significant element of daily activities. However, individuals often focus more on the outcomes of their efforts—such as the number of calories they burn—than on the processes that produce them. This study presents the development of a prediction model integrated into a web application to determine an individual’s caloric intake during physical exercise. The program examines key factors that significantly affect calorie burn using machine learning approaches, providing users with information on the effectiveness of their workouts. To enhance the model’s predicted accuracy, this study analysed domain-specific parameters related to caloric expenditure. Heart rate, exercise duration, body temperature, height, and weight are among the factors selected for the model. Because it indicates the body’s oxygen demand, which is a crucial component of the metabolic processes involved in producing energy from carbohydrates during physical exercise, heart rate is significant. Heart rate fluctuation is a valuable predictor since it is correlated with the degree of exercise. The length of the exercise is also essential because longer workouts tend to burn more calories. To account for individual physiological variations that impact energy consumption, body temperature, height, and weight were also taken into consideration. A dataset that recorded these characteristics during various physical activities was used to train the model using supervised learning techniques. Accuracy, mean squared error, and R-squared values were among the performance evaluation metrics used to assess the model’s ability to estimate caloric expenditure accurately. This research offers a valuable tool for users seeking to monitor and improve their physical health, providing customised estimates tailored to their characteristics.
Keywords: Supervised Learning; Regression Algorithm; Predictions; Calories.
Scope of the Article: Computer Networks and Its Applications