[17] introduced a method using an ANN framework integrated with the Hill muscle model to predict human joint moments accurately with online measurable inputs, and [18] developed a method to predict human lower extremity joint moments using a small number of input variables through an ANN optimized by Elastic Net, which predicted joint moment sequentially in the short term based on biomechanical principles. However, this approach has shown significant limitations in handling long sequence data for long-term predictions, particularly in capturing and forecasting dynamic changes at distant time points. To address this challenge, recent research has tended to predict biological signals in time-series models by forming overlapping windows of continuous data points and training at high batch sizes. [PERSON] et al. [19] developed TranSEMG, a model that integrates spatial and temporal sEMG features to accurately predict hip joint moments, surpassing existing models with its dual-stream architecture. [PERSON] et al. [20] developed and validated a Machine Learning Estimator using a deep convolutional neural network for dynamic control of ankle exoskeletons to enhance walking efficiency across variable speeds. [PERSON] et al. [21] developed and validated a convolutional neural network-based gait phase estimator for real-time control of a robotic hip exoskeleton during various walking conditions, significantly improving gait phase estimation accuracy. [PERSON] et al. [22] developed the DL-Kinetics-FM-Net to accurately predict hip, knee, and ankle joint moments using a single IMU sensor across various walking conditions, demonstrating substantial improvements over traditional deep learning models. [PERSON] et al. [23] developed deep learning models to predict stable gait trajectories for exoskeleton reference trajectories aimed at aiding children with Cerebral Palsy. All these methods have to some extend enhanced the ability to handle time series data and long-sequence predictions through deep architectures and optimization strategies. The aforementioned approach, which utilizes overlapping windows of data points during the training process, though enhancing the model's memory capacity, also introduces a series of issues. Firstly, high-batch training may result in redundant information in the model process, especially when the input data contains significant noise [24; 25], leading to the introduction of excessive, unnecessary information and thus reducing data utilization efficiency. Additionally, this method demands substantial computational resources [26; 27] and may be unsuitable for applications requiring real-time or near-real-time responses due to extended model reaction times. This is particularly limiting in fields requiring rapid decision-making and precise dynamic predictions, such as biomedical and rehabilitation technology, significantly restricting its practical utility. Moreover, the complexity and transient changes in human motion [28; 29; 30] present additional challenges to the design of predictive systems. The use of surface electromyography (sEMG) signals optimizes the immediate capture of muscle activity, significantly enhancing the model's accuracy and response speed in transient dynamics analysis. Particularly within 200 to 300 milliseconds after the generation of sEMG signals [31; 32; 33], the rapid short-term changes and the cumulative effects of long-term motion trends significantly impact joint moments, an aspect that existing models have not sufficiently addressed, necessitating higher demands for the design of prosthetic devices and mechanical control systems. Consequently, this study has developed a model utilizing the Multi-Branch Adaptive Encoding (MAE) approach with a small batch training regimen, leveraging sEMG signals and joint angles as inputs. This model predicts joint moments over a set duration through a single sliding window and employs a high-frequency loss function to adjust sensitivity to local features.