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

Year: 2025
Type: article
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.
DOI: 10.30564/jees.v7i6.9012
Authors: Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, V. Purna Kumari Pechetty
Language: English
Open Access status: diamond
OpenAlex ID: W4411615399
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Cites: 16
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Related to: 10