Lower limb motion recognition plays a vital role in intelligent prosthetic control and wearable assistance systems. While traditional methods typically utilize multi-channel surface electromyography (sEMG) signals to ensure recognition performance, such systems often involve complex hardware setups, are uncomfortable to wear, and require high computational resources. To address these challenges, this study aims to develop an efficient, low-complexity motion recognition approach based on single-channel sEMG signals. The proposed method integrates fast iterative filtering decomposition (FIFD) with a hybrid deep learning framework. FIFD is employed to decompose raw single-channel sEMG signals into multiple sub-components, allowing the extraction of informative intrinsic features. These features are then processed by a deep neural network that combines convolutional neural networks (CNN), attention mechanisms, and long short-term memory (LSTM) units to jointly capture spatial and temporal characteristics of the sEMG signal. Experiments were conducted on a self-constructed dataset comprising sEMG recordings of lower limb movements. The proposed method achieved a recognition accuracy of 99.35%, outperforming conventional multi-channel approaches and other baseline models. The model demonstrated strong robustness and generalizability using only single-channel input. This study presents a novel single-channel sEMG motion recognition method that significantly reduces system complexity while maintaining high recognition accuracy. The approach offers a promising solution for developing low-cost, efficient, and user-friendly wearable systems, particularly suitable for lower limb rehabilitation and human-computer interaction in resource-constrained environments.

Fruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long-short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.