Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.

In the 2020s, human-machine interaction (HMI) is everywhere. However, most HMI systems perform interaction based on physical control, which does not always work well for people with motor impairments, especially people suffering from quadriplegia or amputation. In this paper, we propose a multimodal asynchronous HMI method based on electrooculography (EOG) and speech recognition for wheelchair control in a virtual reality (VR) environment. Specifically, we recruited ten healthy participants to control a wheelchair in a virtual park by performing eye blinks and speaking out voice commands right after a highlighted button. The HMI system simultaneously acquired the participants’ EOG and voice signals and then made output decisions for the wheelchair movement. The participants achieved a satisfactory average accuracy of 99.1±1.3 % and an average response time (RT) of 1.45±0.18 s during the experiment using the proposed method. After workload evaluation with NASA Task Load Index (NASA-TLX), overall workload of the participants using the proposed method during the experiment was at a low level. These results showed that the participants completed the wheelchair control experiment with satisfying performance using the proposed method and offered new directions for assistive HMI methods for people with motor impairments.

Stroke survivors with upper-limb impairments often have difficulty using conventional computer interfaces, which limits their ability to perform daily computer-related activities independently. This study developed a wearable multimodal assistive interface that enables computer interaction through a virtual cursor. A lightweight headband equipped with electrooculography (EOG), electroencephalography (EEG), and an inertial measurement unit (IMU) was used to acquire multimodal signals for interaction control. EOG signals were processed to detect voluntary blinks to generate clicks, head movements were mapped to cursor movements through IMU-based control, and frontal EEG signals were used to estimate attention as an auxiliary mechanism for command verification. A rapid user-specific calibration procedure was introduced to adapt blink-detection thresholds to individual EOG characteristics without requiring extensive training. Thirty stroke patients with upper-limb impairments participated in experiments involving common computer tasks, including news reading, video playback, and character spelling. The system achieved an average operation accuracy of 87.53 ± 4.92%, an average operation time of 3.49 ± 0.49 s, and an information transfer rate of 62.04 ± 15.93 bits/min in the spelling task. The mean NASA-TLX score was 32.1 ± 5.4, indicating a moderate subjective workload during system use. These results demonstrate the feasibility of the proposed wearable multimodal assistive interface for supporting computer interaction in stroke survivors with upper-limb impairments.

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.