Advances in Wearable Sensors for Real-Time Internet of things based Biomechanical Analysis in High-Performance Sports

Vilas Alagdeve1, Ranjan K. Pradhan2, R. Manikandan3, P. Sivaraman4, Sarihaddu Kavitha5, Shaeen Kalathil6*

1Assistant Professor, Department of Electronics Engineering, YCCE, Nagpur, India

2Associate Professor, School of Electrical Sciences, Department of Biotechnology, Odisha University of Technology and Research, Bhubaneswar, Odisha, India

3Professor, Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, TN, India

4Professor, Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Erode, TN,  India

5Assistant Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

6Department of Electrical Engineering, Princess Nourah bint Abdulrahman University Riyadh, Saudi Arabia,

Text Box: Abstract Interest in wearable technology and the need for eco-friendly solutions have spurred new methodologies. This research examines how sophisticated deep learning and biomimetic designs benefit each other. The results may change smart technology forever. The introduction highlights the global appeal of wearable technology and the importance of environmental considerations in design. Deep learning and biomimicry are a fresh and exciting combination that might increase smart device accuracy, energy efficiency, and biomimicry. This project seamlessly integrates biomimetic design elements with deep learning methods. Biomimicry affects wearable technology design and functioning. However, deep learning techniques based on artificial neural networks boost user flexibility and predictive analytics. The controlled experiment allows a thorough examination of a number of datasets designed to cover a wide range of biomimetic settings and user behaviours. The data prove that the proposed technique beats alternatives across several performance parameters. Integrating biomimetic principles with deep learning systems boosts accuracy. This proves the system's reliability. The biomimetic method is eco-friendly since energy efficiency grows dramatically. Biological mimicry indications show that the suggested strategy resembles natural systems. A new exploratory method enhances sustainable technologies. Integrating biomimicry and deep learning efficiently enhances gadget performance and meets environmental standards. This research emphasizes the transformational power of nature-friendly technology, changing our worldview. Our study helps ensure that upcoming wearable technologies are cutting-edge and ecologically beneficial. Deep learning and biomimetic designs are converging, marking a tipping point in sustainable technology. This helps move toward an eco-friendly future. Emails: vilas.alag@gmail.com; rkpradhan@outr.ac.in; eiemanikandan.r@gmail.com; sivaramanresearch@gmail.com; kavitha.sarihaddu@gmail.com; skalathil@pnu.edu.sa

Received: October 17, 2023   Revised: February 23, 2024   Accepted: June 17, 2024

 

Keywords: Advanced Deep Learning; Biomimetic Designs; Eco-Friendly; Environmental Impact; Next-Generation; Sustainable Technology; User Adaptability; Wearable Devices; Wearable Technologies; Biomimicry.