OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study’s comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model’s generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility.