Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities

This study aimed to explore the application of deep neural networks for whole - body human posture prediction during dynamic load - reaching activities. Two time - series models were trained using bidirectional long short - term memory (BLSTM) and transformer architectures. The dataset consisted of 3D full - body plug - in gait dynamic coordinates from 2 0 normal - weight healthy male individuals each performing 204 load - reaching tasks from different load positions while adapting various lifting and handling techniques. The model input s consisted of the 3D position of the hand - load position, lifting (stoop, full - squat and semi - squat) and handling (one - and two - handed) techniques, body weight and height, and the 3D coordinate data of the body posture from the first 25% of the task duration. These inputs were used by the models to predict body coordinates during the remaining 75% of the task period. Moreover, a novel method was proposed to improve the accuracy of the previous and present posture prediction networks by enforcing constant body segment lengths through the optimization of a new cost function. The results indicated that the new cost function decreas ed the prediction error of the models by approximately 8% and 21% for the arm and leg models, respectively. We indicated that utilizing the transformer architecture, with a root - mean - square - error of 47.0 mm, exhibited ~58% more accurate long - term performan ce than the BLSTM - based model. This study merits the use of neural networks that capture time series dependencies in 3D motion frames, providing a unique approach for understand ing and predict motion dynamics during manual material handling activities.