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

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.