Virtual hand selection techniques in AR/VR face a persistent challenge due to the inherent speed–accuracy trade-off. Although target prediction offers a promising direction, its practical adoption is limited by the inevitable errors of predictive models. We present Motion-Touch, a selection technique that integrates a Kinematics-Based Adaptive Switch (KBAS) with deep-learning-based target prediction. KBAS switches between the two phases of pointing process: an untriggerable ballistic phase and a corrective phase in which only the AI-predicted target can be triggered through Touch. The technique can adaptively switch between these phases under distinct kinematic conditions. We collected a hand kinematics dataset from 20 participants to support model training and mechanism calibration. Compared to baseline techniques, Motion-Touch achieves selection times statistically comparable to the fastest reliable controller, while offering controller-free, error-free selection with minimal trigger effort. Our findings demonstrate how Motion-Touch achieves a near-optimal compromise for the speed–accuracy trade-off in virtual hand selection.

Although an increasing number of studies explore the factors influencing users’ privacy concerns regarding social robots, the existing understanding of this issue remains largely fragmented. Previous studies have mainly focused on the "net effect" between variables, leaving the complexity of causal configurations, and the holistic impact of design characteristics of social robots on user privacy concerns remains unclear. Based on the Stimuli-Organism-Response (S-O-R) framework and Communication Privacy Management Theory (CPMT), this study integrates social robot design characteristics such as Anthropomorphism, Warmth, Competence, and Transparency into causal configurations, and uses Perceived Privacy Risk and Perceived Privacy Control as mediating variables to propose a Comprehensive conceptual model. Based on valid data from a sample of 198 Chinese social robot users, this study conducted empirical analyses of the conceptual model using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). PLS-SEM results show that anthropomorphism, warmth, competence, and transparency are key factors influencing privacy concerns, and perceived privacy risk mediates the relationship between warmth, information transparency, and privacy concerns. The FsQCA results further validated the findings of PLS-SEM and identified five configurations of factor combinations that led to higher levels of user privacy concerns. Among them, the combination of high anthropomorphism design, high competence, and low warmth of social robots is the core configuration that leads to users’ privacy concerns. Overall, this study broadens our understanding of social robot users’ privacy concerns and reveals the causal complexity behind social robot users’ privacy concerns. It provides some theoretical and practical insights for subsequent scholars and designers.

This study explores the design of a selection technique in virtual environments leveraging kinematic data derived from hand movements. We first identified the intrinsic challenges of virtual hand selection techniques, particularly in complex settings, including Accidental Selection, Slow Selection, Failed Selection, and Fragmented Selection. To mitigate these issues, we introduce Magic-Tap, a selection technique that ascertains the trigger of an object based on real-time variations in virtual hand acceleration and speed, seamlessly integrating the pointing and triggering processes without requiring explicit triggering signals. The parameter settings of Magic-Tap were fine-tuned through Study One, ameliorating its trigger rate, error rate, and trigger time. Furthermore, we compared Magic-Tap with three conventional virtual hand selection techniques (Touch, Dwell-Time, and Pinch) in Study Two. The results indicate that the task completion time of Magic-Tap is comparable to Touch in all situations while exhibiting an error rate as low as Dwell-Time and Pinch.