In complex environments, users frequently need to manage multiple tasks simultaneously, which poses significant challenges for user interface design. For instance, when driving, users must maintain continuous visual attention on the road ahead while also monitoring rearview mirrors and performing shoulder checks. These multitasking scenarios present substantial design challenges in effectively guiding users. To address these challenges, we focus on investigating and designing visual and haptic guidance systems to augment users’ performance. We initially propose the use of visual guidance, specifically employing a dynamic arrow as a guidance technique. Our evaluation shows that dynamic arrows significantly expedite both reaction and selection times. We further introduce and evaluate haptic feedback, which users perceive as more salient than visual guidance, leading to quicker responses when switching from primary to secondary tasks. This allows users to maintain visual attention on the primary task while simultaneously responding effectively to haptic cues. Our findings suggest that multimodal guidance, especially haptic guidance, can enhance both reaction time and user experience in dual-task environments, offering promising practical implications and guidelines for designing more user-friendly interfaces and systems.

Augmented reality (AR) technologies can blend digital and physical space and serve a variety of applications intuitively and effectively. Specifically, wearable AR enabled by optical see-through (OST) AR head-mounted displays (HMDs) might provide users with a direct view of the physical environment containing digital objects. Besides, users could directly interact with three-dimensional (3D) digital artefacts using freehand gestures captured by OST HMD sensors. However, as an emerging user interaction paradigm, freehand interaction with OST AR still requires further investigation to improve user performance and satisfaction. Thus, we conducted two studies to investigate various freehand selection design aspects in OST AR, including target placement, size, distance, position, and haptic feedback on the hand and body. The user evaluation results indicated that 40 cm might be an appropriate target distance for freehand gestural selection. A large target size might lower the selection time and error rate, and a small target size could minimise selection effort. The targets positioned in the centre are the easiest to select, while those in the corners require extra time and effort. Furthermore, we discovered that haptic feedback on the body could lead to high user preference and satisfaction. Based on the research findings, we conclude with design recommendations for effective and comfortable freehand gestural interaction in OST AR.

The application of intelligent robots will change the service model of hotels. However, users’ willingness to use robots in hotels is not so strong. The research aims to identify the factors influencing potential consumers’ intention to use hotel robots. Based on the technology acceptance model and social presence theory, this study constructs a hotel robot acceptance model (HRAM), and this model includes seven variables: social presence, perceived playfulness, trust, perceived ease of use, perceived usefulness, attitude, and willingness to use hotel robots. The research involved a combination of quantitative (N = 261) and qualitative (N = 20) methods used to collect data on potential hotel customers in China, and structural equation modeling was applied for verification. The research results showed that social presence positively influences perceived playfulness, attitude, and trust, with an indirect influence on users’ behavioral intention to use hospitality robots. Perceived ease of use has a positive impact on perceived usefulness; it also positively affects users’ attitudes. Perceived playfulness, perceived usefulness, attitude, and trust positively influence consumers’ behavioral intention to use hospitality robots. This research reveals the influence of social presence, perceived playfulness, trust, perceived ease of use, perceived usefulness, and attitude on users’ willingness to use hotel robots. This research expands the technology acceptance model and its application fields so that the model can serve as a theoretical framework for studies on hotel user behaviors. The findings can provide reference and guidance for the design of hospitality robots, the innovation of hospitality service models, and the decision-making of hospitality managers. The R&D of new hotel robots can lead to higher user acceptance and expand the model applications, thus advancing the sustainable development of hotel tourism.

Safe street crossing poses significant challenges for visually impaired pedestrians, who must rely on non-visual cues to assess crossing safety. Conventional assistive technologies often fail to provide real-time, actionable information about oncoming traffic, making independent navigation difficult, particularly in uncontrolled or vehicle-based crossing scenarios. To address these challenges, we designed and evaluated two assistive systems utilizing haptic and visual feedback, tailored for traffic signal-controlled intersections and vehicle-based crossings. The results indicate that visual feedback significantly improved decision efficiency at signalized intersections, enabling users to make faster decisions, regardless of their confidence levels. However, in vehicle-based crossings, where real-time hazard assessment is crucial, haptic feedback proved more effective, enhancing decision efficiency by enabling quicker and more intuitive judgments about approaching vehicles. Moreover, users generally preferred haptic feedback in both scenarios, citing its comfort and intuitiveness. These findings highlight the distinct challenges posed by different street-crossing environments and confirm the value of multimodal feedback systems in supporting visually impaired pedestrians. Our study provides important design insights for developing effective assistive technologies that enhance pedestrian safety and independence across varied urban settings.

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of collaboration networks, publication trajectories, and intellectual structures. The results indicate a clear evolution from single-purpose, stand-alone devices to integrated ecosystem solutions that address the needs of diverse VRU groups. Six dominant knowledge clusters emerged—street-crossing assistance, obstacle avoidance, human–computer interaction, cyclist safety, blind navigation, and smart glasses. Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. The review provides a consolidated knowledge map to inform researchers, practitioners, and policy-makers striving for inclusive and proactive road safety solutions.

Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays and Haptic Feedback mechanisms, this research aims to identify effective strategies for recognizing and localizing potential traffic hazards. Study 1 examines the design and effectiveness of Visual Feedback, focusing on factors such as feedback type, traffic scenarios, and target locations. Study 2 investigates the integration of Haptic Feedback through wearable vests to enhance cyclists’ awareness of peripheral vehicular activities. By conducting experiments in realistic traffic conditions, this research seeks to develop safety systems that are intuitive, cognitively efficient, and tailored to the needs of diverse user groups. This work advances multimodal interaction design for road safety and aims to contribute to a global reduction in traffic incidents involving vulnerable road users. The findings offer empirical insights for designing effective assistance systems for cyclists and other non-motorized vehicle users, thereby ensuring their safety within complex traffic environments.