Button layout plays a crucial role in user–device interaction. An impractical layout can hinder user efficiency and satisfaction. Current joystick button designs lack systematic references, relying on common body measurements and subjective judgments, which makes it difficult to cope with diverse modes of operation. This paper introduces a basic grip state based on adult male hand size, defining three operational areas. We conducted experiments involving 15 participants at three hand length percentiles (10, 50, 90), revealing positive correlations of operation range with body dimensions. Further discussion analyzes the dynamic operating range and specific features in different areas. These findings inform an incremental design principle for joystick layouts, offering precise guidance for future designs.

Many unsupervised domain adaptation methods require simultaneous access to both source-domain and target-domain data during the training stage. However, in practical electro-mechanical actuator (EMA) fault diagnosis, source-domain data may be unavailable, or it might not be able to be directly shared because of data ownership, confidentiality, or deployment constraints. Moreover, variations in command waveform, amplitude, load, and operating speed can cause distribution shifts between different working conditions, which degrades the diagnostic performance of models trained under a single condition. The domain adaptation model will also experience decreased performance in the source domain due to catastrophic forgetting. Hence, a dual-domain adaptation model is proposed, one which can reduce the risk of data leakage and relieve catastrophic forgetting in the source domain while improving domain adaptation in the target domain. The proposed framework transfers a source-trained model to the target domain without directly accessing source-domain samples. Sparse domain attention is introduced into the feature network to construct source-related and target-related feature channels, aiming to reduce source-domain performance degradation during target-domain adaptation. In addition, the classifier is fixed according to the source-domain hypothesis, and local structure clustering is used to improve pseudo-label consistency for unlabeled target-domain samples. The effectiveness of the proposed method is compared with several methods, utilizing low-frequency and high-frequency monitoring signals of the electro-mechanical actuator dataset, respectively.

To address the lack of effective ergonomic evaluation methods for HOTAS (Hands-On Throttle And Stick) control sticks in dynamic operation scenarios, this study proposes a virtual hand motion data acquisition system based on Unreal Engine 5, combined with Leap Motion tracking technology. This system captures and records hand movements and interaction processes in real time, providing key quantitative indicators such as movement trajectories, operation durations, and key positioning deviations. Through four-stage HOTAS task experiments, the efficiency and accuracy differences of various operation types are quantitatively analyzed to provide data-driven support for the ergonomic optimization of HOTAS control rods. The experimental results verify the effectiveness of the system for dynamic human factors evaluation, and it is expected to serve as a preliminary virtual evaluation tool for the ergonomic optimization of HOTAS.

Flight intent cannot always be inferred from aircraft motion because the purpose of a maneuver depends on its surrounding context. This study examines multimodal flight-intent understanding in cinematic air-combat footage. We introduce CineAeroIntent, a benchmark constructed from 3972 annotated candidate clips collected from Chinese air-combat films and television programs. After quality control, 3958 clips were retained and organized by observation perspective. Each sample contains an intent label and an explanation linked to evidence in the clip. Using Qwen2.5-Omni as the backbone, we evaluate Modality-Decoupled Expert Projection (MDEP), which assigns a separate low-rank update to each input stream, and Post-Projection Value-Gating (PPVG), which reduces the contribution of low-utility value representations before attention aggregation. The full model obtains 89.6% overall accuracy and 88.4% Macro F1. Under matched adaptation settings, accuracy increases from 82.3% with shared LoRA to 86.1% with MDEP and 89.6% with MDEP–PPVG. In a natural-subset stress test, the full model retains 95.7% of its lower-interference accuracy on clips with prominent background music, compared with 82.6% for the unadapted backbone. These results support cue-grounded multimodal modeling for cinematic flight-scene understanding.