In the field of the muscle–computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models.

This paper proposes a velocity learning method based on the directional Gaussian model to detect speeding events in surveillance scenarios. The proposed method is of an uncalibrated type, but yet has considered the influences of projective transformation on estimating the motion velocities in an image plane, which is convenient and feasible to use in real application. We have analyzed the theory that the velocity in the image plane varies due to the changes of the direction of the moving object with constant velocity in a real world plane. With the support of this theory, we propose to learn the velocities calculated on a certain position in different direction bins to tolerate the effect of projective transformation on velocity modeling. To facilitate the whole framework, two key issues have to be addressed. First, we have designed an improved Fisher model to optimize the direction bins, which reflect the major moving directions in a scenario. Second, we have adopted a weighted sampling strategy and surface fitting to solve the lack of sample problem during the learning process. Experiments conducted on real surveillance videos show that the proposed method can obtain competitive results compared with the state-of-the-art methods.

Despite their success, existing human pose estimation approaches mostly have complex architectures, high cost, and lack of lightweight modules. To address this problem, this paper proposes a Ghost Shuffle Lightweight Pose Network (GSLPN) with a more lightweight and efficient network architecture than the popular Lightweight Pose Network. First, in order to condense the scale of the network while maintaining its performance, we stack two lightweight modules, depthwise convolution and the Ghost module, to build our initial prototype bottleneck. Then, we impose a channel shuffle operation on the prototype bottleneck to shuffle the sequence of the feature maps for constructing Ghost Shuffle Bottleneck (Ghost Shuffle Bottleneck) with effective feature representation so as to develop a GSLPN. Second, a lightweight, efficient parallel attention mechanism, Lightweight Pose Parallel Attention, is proposed to improve keypoint locating accuracy. An experiment validating the proposed method showed that GSLPN achieved competitive performance with a smaller model size and less computational complexity than state‐of‐the‐art methods, indicating that the GSLPN is a superior approach for human pose estimation.