Forest fires, severe natural disasters causing substantial damage, necessitate accurate predictive modeling to guide preventative measures effectively. This study introduces an enhanced window-based Transformer time series forecasting model aimed at improving the precision of forest fire predictions. Leveraging time series data from 2020 to 2021 in Chongli, a myriad of forest fire influencing factors were ascertained using remote sensing satellite and GIS technologies, with their interrelationships estimated through a multicollinearity test. Given the intricate nature of real-world forest fire prediction tasks, we propose a novel window-based Transformer architecture complemented by a dual time series input strategy premised on 13 influential factors. Subsequently, time series data were incorporated into the model to generate a forest fire risk prediction map in Chongli District. The model’s effectiveness was then evaluated using various metrics, including accuracy (ACC), root mean square error (RMSE), and mean absolute error (MAE), and compared with traditional deep learning methods. Our model demonstrated superior predictive performance (ACC = 91.56%, RMSE = 0.37, MAE = 0.05), harnessing spatial background information efficiently and effectively utilizing the periodicity of forest fire factors. Consequently, the study proves this method to be a novel and potent approach for time series fire prediction.

Hand gesture recognition based on surface electromyography (sEMG) plays an important role in the field of biomedical and rehabilitation engineering. Recently, there is a remarkable progress in gesture recognition using high‐density surface electromyography (HD‐sEMG) recorded by sensor arrays. On the other hand, robust gesture recognition using multichannel sEMG recorded by sparsely placed sensors remains a major challenge. In the context of multiview deep learning, this paper presents a hierarchical view pooling network (HVPN) framework, which improves multichannel sEMG‐based gesture recognition by learning not only view‐specific deep features but also view‐shared deep features from hierarchically pooled multiview feature spaces. Extensive intrasubject and intersubject evaluations were conducted on the large‐scale noninvasive adaptive prosthetics (NinaPro) database to comprehensively evaluate our proposed HVPN framework. Results showed that when using 200 ms sliding windows to segment data, the proposed HVPN framework could achieve the intrasubject gesture recognition accuracy of 88.4%, 85.8%, 68.2%, 72.9%, and 90.3% and the intersubject gesture recognition accuracy of 84.9%, 82.0%, 65.6%, 70.2%, and 88.9% on the first five subdatabases of NinaPro, respectively, which outperformed the state‐of‐the‐art methods.

As a machine‐learning‐driven decision‐making problem, the surface electromyography (sEMG)‐based hand movement recognition is one of the key issues in robust control of noninvasive neural interfaces such as myoelectric prosthesis and rehabilitation robot. Despite the recent success in sEMG‐based hand movement recognition using end‐to‐end deep feature learning technologies based on deep learning models, the performance of today’s sEMG‐based hand movement recognition system is still limited by the noisy, random, and nonstationary nature of sEMG signals and researchers have come up with a number of methods that improve sEMG‐based hand movement via feature engineering. Aiming at achieving higher sEMG‐based hand movement recognition accuracies while enabling a trade‐off between performance and computational complexity, this study proposed a progressive fusion network (PFNet) framework, which improves sEMG‐based hand movement recognition via integration of domain knowledge‐guided feature engineering and deep feature learning. In particular, it learns high‐level feature representations from raw sEMG signals and engineered time‐frequency domain features via a feature learning network and a domain knowledge network, respectively, and then employs a 3‐stage progressive fusion strategy to progressively fuse the two networks together and obtain the final decisions. Extensive experiments were conducted on five sEMG datasets to evaluate our proposed PFNet, and the experimental results showed that the proposed PFNet could achieve the average hand movement recognition accuracies of 87.8%, 85.4%, 68.3%, 71.7%, and 90.3% on the five datasets, respectively, which outperformed those achieved by the state of the arts.

Surface Electromyography (sEMG)-based pattern recognition system is a kind of instrumentation and measurement system that captures, processes and recognizes bioelectrical signals, like other pattern recognition systems, it can benefit from multimodal data to enhance its recognition performance. However, acquiring multimodal data typically requires additional sensors, thereby increasing hardware complexity and costs. This paper proposes a novel Generative Auxiliary Modality (GAM) method. It improves the overall performance of unimodal sEMG-based hand gesture recognition (HGR) by integrating a multimodal generative adversarial learning process into the HGR model training phase. Specifically, we trained a deep generative model using real sEMG and IMU signals, which is able to generate corresponding virtual IMU signals from the input real sEMG signals. During gesture recognition, the generated virtual IMU signals were used as the auxiliary modality alongside sEMG signals to provide multimodal information for unimodal sEMG-based gesture recognition, both of which are subsequently fed into a multimodal convolutional neural network for gesture recognition. Evaluations were conducted on six databases, including five publicly available databases and our collected database containing sEMG and IMU data from 28 subjects performing 38 gestures. Results demonstrate that by incorporating generated virtual IMU signals, our method can significantly improve unimodal sEMG-based HGR accuracy (with increases of 2.15%-11.43% and 2.54%-8.29% in intra-subject and inter-subject HGR accuracy, respectively), while only incurring an acceptable increase in computational cost. This improvement is particularly evident in distinguishing gestures with identical hand gestures across different forearm postures. Moreover, the accuracy of our proposed GAM method is comparable to multimodal HGR systems using both real sEMG and IMU signals as their inputs. This work represents a successful attempt to provide auxiliary modal signals for sEMG-based HGR through multimodal generative adversarial learning without the need for physical IMU sensors, thus having the potential to reduce hardware complexity and costs.

Autonomous systems’ “black-box” nature impedes user trust and adoption. To investigate explainable visualizations’ impact on trust and cognitive states, we conducted a within-subjects study with 29 participants performing high-fidelity driving tasks across three transparency conditions: black-box, standard, and enhanced visualization. Multimodal data analysis revealed that enhanced visualization significantly increased perceived usefulness by 28.5% (p < 0.001), improved functional trust, and decreased average pupil diameter by 15.3% (p < 0.05), indicating lower cognitive load. The black-box condition elicited minimal visual exploration, lowest subjective ratings, and “out-of-the-loop” behaviors. Fixation duration showed no significant difference between standard and enhanced conditions. These findings demonstrate that well-designed visualizations enable balanced trust calibration and cognitive efficiency, advocating “meaningful transparency” as a core design principle for effective human–machine collaboration in autonomous vehicle interfaces. This study provides empirical evidence that transparency enhances user experience and system performance.

Objective: Multimodal hand gesture recognition (HGR) systems can achieve higher recognition accuracy compared to unimodal HGR systems. However, acquiring multimodal gesture recognition data typically requires users to wear additional sensors, thereby increasing hardware costs. Methods: This paper proposes a novel generative approach to improve Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial Measurement Unit (IMU) signals. Specifically, we trained a deep generative model based on the intrinsic correlation between forearm sEMG signals and forearm IMU signals to generate virtual forearm IMU signals from the input forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU signals were fed into a multimodal Convolutional Neural Network (CNN) model for gesture recognition. Results: We conducted evaluations on six databases, including five publicly available databases and our collected database comprising 28 subjects performing 38 gestures, containing both sEMG and IMU data. The results show that our proposed approach significantly outperforms the sEMG-based unimodal HGR approach (with increases of 2.15%-13.10%). Moreover, it achieves accuracy levels closely matching those of multimodal HGR when using virtual Acceleration (ACC) signals. Conclusion: It demonstrates that incorporating virtual IMU signals, generated by deep generative models, can significantly improve the accuracy of sEMG-based HGR. Significance: The proposed approach represents a successful attempt to bridge the gap between unimodal HGR and multimodal HGR without additional sensor hardware, which can help to promote further development of natural and cost-effective myoelectric interfaces in the biomedical engineering field.