Supernumerary robotic limbs (SRLs) offer substantial potential in both the rehabilitation of hemiplegic patients and the enhancement of functional capabilities for healthy individuals. Designing a general-purpose SRL device is inherently challenging, particularly when developing a unified theoretical framework that meets the diverse functional requirements of both upper and lower limbs. In this paper, we propose a multi-objective optimization (MOO) design theory that integrates grasping workspace similarity, walking workspace similarity, braced force for sit-to-stand (STS) movements, and overall mass and inertia. A geometric vector quantification method is developed using an ellipsoid to represent the workspace, aiming to reduce computational complexity and address quantification challenges. The ellipsoid envelope transforms workspace points into ellipsoid attributes, providing a parametric description of the workspace. Furthermore, the STS static braced force assesses the effectiveness of force transmission. The overall mass and inertia restricts excessive link length. To facilitate rapid and stable convergence of the model to high-dimensional irregular Pareto fronts, we introduce a multi-subpopulation correction firefly algorithm. This algorithm incorporates a strategy involving attractive and repulsive domains to effectively handle the MOO task. The optimized solution is utilized to redesign the prototype for experimentation to meet specified requirements. Six healthy participants and two hemiplegia patients participated in real experiments. Compared to the pre-optimization results, the average grasp success rate improved by 7.2%, while the muscle activity during walking and STS tasks decreased by an average of 12.7% and 25.1%, respectively. The proposed design theory offers an efficient option for the design of multi-functional SRL mechanisms.

Objective:While neuroscience research has established a link between vision and intention, studies on gaze data features for intention recognition are absent. The majority of existing gaze-based intention recognition approaches are based on deliberate long-term fixation and suffer from insufficient accuracy. In order to address the lack of features and insufficient accuracy in previous studies, the primary objective of this study is to suppress noise from human gaze data and extract useful features for recognizing grasp intention.Methods:We conduct gaze movement evaluation experiments to investigate the characteristics of gaze motion. The target-attracted gaze movement model (TAGMM) is proposed as a quantitative description of gaze movement based on the findings. A Kalman filter (KF) is used to reduce the noise in the gaze data based on TAGMM. We conduct gaze-based natural grasp intention recognition evaluation experiments to collect the subject's gaze data. Four types of features describing gaze point dispersion ($f_{var}$), gaze point movement ($f_{gm}$), head movement ($f_{hm}$), and distance from the gaze points to objects ($f_{d_{j}}$) are then proposed to recognize the subject's grasp intentions. With the proposed features, we perform intention recognition experiments, employing various classifiers, and the results are compared with different methods.Results:The statistical analysis reveals that the proposed features differ significantly across intentions, offering the possibility of employing these features to recognize grasp intentions. We demonstrated the intention recognition performance utilizing the TAGMM and the proposed features in within-subject and cross-subject experiments. The results indicate that the proposed method can recognize the intention with accuracy improvements of 44.26% (within-subject) and 30.67% (cross-subject) over the fixation-based method. The proposed method also consumes less time (34.87 ms) to recognize the intention than the fixation-based method (about 1 s).Conclusion:This work introduces a novel TAGMM for modeling gaze movement and a variety of practical features for recognizing grasp intentions. Experiments confirm the effectiveness of our approach.Significance:The proposed TAGMM is capable of modeling gaze movements and can be utilized to process gaze data, and the proposed features can reveal the user's intentions. These results contribute to the development of gaze-based human-robot interaction.