aspg-3-3152-1791417432 20261007235712 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 17 1 Osamah A. Altammami College of Business Administration, Majmaah University, Majmaah 11952, Saudi Arabia This article focuses on improving the accuracy and efficiency of multimodal human motion analysis using advanced techniques. Initially, Generative Adversarial Networks (GANs) were used for skeletal enhancement, and then Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied on the enhanced images to check the quality Joint-level. Limb-level, Temporal, Statistical Features are effectively recovered from contrast enhancing images. Furthermore, with the selected optimal features acquired from PutterFish Customized Serval Optimizer (PFCSO), the RehabNet++ architecture that makes the human movement assessment has been trained. This PFCSO model has been developed based on the inspiration acquired from the Pufferfish Optimization Algorithm (POA) and the Serval Optimization algorithm (SOA), respectively. The RehabNet++ architecture includes an optimized Multilayer Perceptron (O-MLP), STR-ResNet architecture, Attention-based Convolutional Neural Networks and Transfer Learning. The O-MLP model has been formulated by optimizing the hidden layers of MLP using the PFCSO model. In addition, Grad-CAM visualization is included to provide a graphical description for model selection. A comparative study has been conducted to test the proposed deep learning algorithm against the original methods using the Kimore dataset. This analysis is implemented in PYTHON and is dedicated to multimodal human motion analysis. 2025 159 182 3152 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.170112 https://www.americaspg.com/journal/3/article/3152 https://www.americaspg.com/storage/91724155521.pdf