Particle swarm optimization-driven deep ‎maxout network for effective monitoring of ‎paralyzed persons

Authors

  • Dr. R. Sivaraman Associate Professor, Department of Mathematics, Dwaraka Doss Goverdhan Doss Vaishnav College, Arumbakkam, Chennai – 600
  • Dr. Nithya. S Assistant Professor, Department of Computer Applications, Faculty of Science & Humanities, SRMIST, Kattankulathur
  • Dr. B. Srinivasa Rao Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Cheeryal, Medchal, ‎Hyderabad-501301
  • Rama Devi C Assistant professor, Department of EEE, St.Joseph's College of Engineering, OMR road ,Chennai,
  • Dr. N. Venkatesh Assistant Professor, School of CS & AI-CSE,S R University-Warangal-506371
  • S. Sharanyaa Assistant Professor, Department: Information Technology, Institution name and address: Panimalar Engineering College, Chennai ‎‎600123

How to Cite

Sivaraman, D. R. ., S, D. N. ., Rao , D. B. S. ., C, R. D. . ., Venkatesh , D. N. ., & Sharanyaa , S. . (2025). Particle swarm optimization-driven deep ‎maxout network for effective monitoring of ‎paralyzed persons. International Journal of Basic and Applied Sciences, 14(1), 113-123. https://doi.org/10.14419/bk2d1638

Received date: March 24, 2025

Accepted date: April 20, 2025

Published date: April 27, 2025

DOI:

https://doi.org/10.14419/bk2d1638

Keywords:

Particle Swarm Optimization (PSO); Deep Maxout Network (DMN); Posture Recognition; Fall Detection; Human Activity Monitoring; ‎Healthcare AI; Real-Time Classification; Hyperparameter Optimization

Abstract

Effective monitoring of paralyzed individuals is crucial for ensuring their safety and well-being, particularly in detecting falls and abnormal ‎postural states. This research proposes a Particle Swarm Optimization (PSO)-driven Deep Maxout Network (DMN) to enhance the ‎accuracy and efficiency of human posture recognition. The proposed system utilizes RGB images from the Fall Detec-tion Dataset, which are ‎preprocessed through resizing, normalization, data augmentation, and bounding box transformations. The DMN model, enhanced with ‎Maxout activation, is employed for robust feature extraction, ensuring superior discrimination of pos-tural states. Additionally, PSO is ‎integrated for hyperparameter optimization, dynamically fine-tuning parameters to improve classi-fication performance. The optimized DMN ‎model achieved an accuracy of 96.4%, outperforming conventional classifiers. Further-more, PSO-driven optimization significantly reduced ‎computational complexity, ensuring faster convergence and improved general-ization. Comparative analysis shows that the optimized DMN ‎exhibits a lower inference time (6.1 ms) than traditional models. Ad-ditionally, ROC-AUC analysis yields a score of 0.98, ‎highlighting the model’s strong discriminative capability in distinguishing postural states. The proposed PSO-DMN framework presents a ‎reliable and efficient approach for paralyzed person monitoring, offer-ing real-time posture recognition with high accuracy. The system’s ‎ability to detect falls, classify different postural states, and oper-ate efficiently in real-time settings makes it a promising solution for ‎healthcare applications, particularly in home and assisted-living environments‎.

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How to Cite

Sivaraman, D. R. ., S, D. N. ., Rao , D. B. S. ., C, R. D. . ., Venkatesh , D. N. ., & Sharanyaa , S. . (2025). Particle swarm optimization-driven deep ‎maxout network for effective monitoring of ‎paralyzed persons. International Journal of Basic and Applied Sciences, 14(1), 113-123. https://doi.org/10.14419/bk2d1638

Received date: March 24, 2025

Accepted date: April 20, 2025

Published date: April 27, 2025