Solar-Powered Aerobics Training Robot with Adaptive Energy Management for Improved Environmental Sustainability

Authors

  • Bevl Naidu

    Department of Management Studies, Aditya Degree & PG Colleges, Andhra Pradesh 533001, India
  • Krishna Babu Sambaru

    Department of Digital Marketing, Aditya Degree & PG College , Kakinada, Andhra Pradesh 533001, India
  • Guru Prasad Pasumarthi

    Department of Research and Analytics, PB Siddhartha Arts and Science College, Vijayawada, Andhra Pradesh 521108, India
  • Romala Vijaya Srinivas

    Department of Research and Analytics, Business School, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, A.P 522302, India
  • K. Srinivasa Krishna

    Department of Management Studies, Madanapalle Institute of Technology and Science, Madanapalle, Annamayya District 517325, India
  • V. Purna Kumari Pechetty

    Department of Research and Analytics, SR University, Anantha Sagar, Hasanparthy, Hanamkonnda 506371, India

DOI:

https://doi.org/10.30564/jees.v7i6.9012
Received: 8 March 2025 | Revised: 7 May 2025 | Accepted: 21 May 2025 | Published Online: 14 June 2025

Abstract

With the rapid advancement of robotics and Artificial Intelligence (AI), aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy. This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency. The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network (DynCO-BNN) to enable precise exercise monitoring and real-time feedback. Solar tracking technology ensures optimal energy absorption, while a microcontroller-based regulator manages power distribution and robotic movement. Dual-battery switching ensures uninterrupted operation, aided by light and I/V sensors for energy optimization. Using the INSIGHT-LME IMU dataset, which includes motion data from 76 individuals performing Local Muscular Endurance (LME) exercises, the system detects activities, counts repetitions, and recognizes human movements. To minimize energy use during data processing, Min-Max normalization and two-dimensional Discrete Fourier Transform (2D-DFT) are applied, boosting computational efficiency. The robot accurately identifies upper and lower limb movements, delivering effective exercise guidance. The DynCO-BNN model achieved a high tracking accuracy of 96.8%. Results confirm improved solar utilization, ecological sustainability, and reduced dependence on fossil fuels—positioning the robot as a smart, energy-efficient solution for next-generation fitness technology.

Keywords:

Aerobics Training Robot; Energy Power Supply Control; Dynamic Cheetah Optimizer (DynCO); Bayesian Neural Network (BNN); Motion Recognition

References

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How to Cite

Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, & V. Purna Kumari Pechetty. (2025). Solar-Powered Aerobics Training Robot with Adaptive Energy Management for Improved Environmental Sustainability. Journal of Environmental & Earth Sciences, 7(6), 482–496. https://doi.org/10.30564/jees.v7i6.9012

Issue

Vol. 7 , Iss. 6 (June 2025)

Article Type

Article

License

Copyright © 2025 Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, V. Purna Kumari Pechetty

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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