Assessing pilot trainees’ Situation Awareness (SA) is critical for aviation safety and efficiency. This study aimed to evaluate the effectiveness of three distinct eye-tracking metrics, gaze-based metrics, gaze patterns, and gaze entropy, in measuring the SA levels of pilot trainees. A between-subject experiment compared two trainee groups: one received standard training, and the other received training enhanced with vibrotactile feedback. Participants’ eye movements were recorded using wearable eye-tracking glasses before and after training. The experimental group showed improved flight performance, advanced gaze-based metrics, more organized gaze patterns, and increased gaze entropy. These results demonstrate that eye-tracking metrics can effectively reflect SA changes, with gaze entropy significantly correlating with SA measurements and indicating higher SA levels. The study highlights the feasibility of using eye-tracking to evaluate SA in pilot training. Future research should explore varied task complexities and extended training durations to validate these findings and assess long-term impacts.

Empathic medical conversation is central to patient-centered care within Narrative Medicine. However, difficulties, such as physicians’ limited empathic capabilities and lack of time, impede the practice. Research on real-time, on-site empathic medical exchanges has been limited in exploring technology to assist and enhance physicians’ capabilities. This paper proposed the Empathic Opportunity Perception and Distinction (EOPD) framework for building physician-AI collaboration based on Intelligence Augmentation (IA) for empathic conversations. The EOPD integrates two multi-modal machine learning (ML) models based on facial and verbal cues, presenting a physician-AI interaction framework and three distinctive visualization components: emotional reference, opportunity reminding and keyword collection, and situation understanding. To assess EOPD's effectiveness and gauge physicians’ and patients’ receptiveness, a prototype system named EMVIS ( EM otional VIS ualization ) was designed and developed. Results from the study demonstrated improvements in physicians’ empathy efforts and perceived empathy performance when using EMVIS, particularly for junior physicians. Physicians and patients held positive attitudes towards EMVIS, with patients expressing a high expectation that EMVIS would improve the physician-patient relationship. The research showed the efficacy of the multi-modal ML models in supporting complex affective empathy and EMVIS in facilitating and complementing empathy concerns. It highlighted the tailored support to junior and senior physicians and emphasized physician-AI collaboration to maintain user autonomy and mitigate potential biases. Future research should explore extensive system applications, tailor visual and interactive support for physicians, and implement adaptive and reflective ML models to improve the effectiveness and efficiency of empathy communications.

Ultrasonic mid-air haptics (UMH) has emerged as a promising technology for facial haptic applications, offering the advantage of contactless and high-resolution feedback. Despite this, previous studies have fallen short in thoroughly investigating individuals’ responses to UMH on the face. To bridge this gap, this study compares UMH feedback on various facial sites using the lateral modulation (LM) method. This method allows us to explore the impact of two LM parameters -frequency and amplitude - on both perceptual (intensity) and emotional (valence and arousal) responses. With 24 participants, positive relationships between LM amplitude and perceived intensity and arousal were observed, and the effect of LM frequency varied across facial sites. These findings not only contribute to the development of design guidelines and potential applications for UMH on the face, but also provide insights aimed to enhance the effectiveness and overall user experience in haptic interactions across diverse facial sites.

Considering that a significant portion of the current pedestrian population has limited exposure to automated vehicles (AVs), it is crucial to have a reliable instrument for assessing pedestrians’ initial trust in AVs. Using a survey of 436 pedestrians, this study developed and validated a PITQA (Pedestrians’ Initial Trust Questionnaire for AVs) scale using partial least squares structural equation modeling (PLS-SEM). The proposed scale will be valuable in monitoring the progression of trust over time and considering trust-related factors during the design process. The results revealed that seven key constructs significantly contribute to predicting initial trust between pedestrians and AVs. These constructs include propensity to trust, perceived statistical reliability, dependability and competence, perceived predictability, familiarity, authority/subversion, care/harm, and sanctity/degradation. These shed light on how the trust propensity of individuals, different trust/trustworthiness attributes might constitute different aspects of initial trust in the pedestrian-AV context. The developed scale can be a potentially useful tool for future research endeavors concerning trust calibration and the design of AVs specifically tailored for vulnerable road users.

Patient-physician communication is a crucial aspect of clinical diagnoses and treatments. However, there are barriers to effective empathic practices, including consciousness, busy working rhythms, and difficulties recognising patients’ implicit emotional expressions. While previous research has attempted to support asynchronous medical conversations, this study has explored the use of emotion visualisation techniques for synchronous, face-to-face medical encounters. After interviewing doctors to understand user requirements, an emotion-visualisation prototype, EMVIS, was created. The prototype was evaluated in a study with 31 patients and 37 healthcare providers within different specialist groups using a contextualised Technology Acceptance Model (TAM) and follow-up interviews. The results indicated that patients and physicians were generally accepting of emotion visualisation for medical encounters. Patients were more interested in their physicians’ attitudes and intentions, while physicians accepted the visualisation, but their requirements differed according to their skill levels and specialities. Hence, four supportive factors - emotional empathy, careful attention, human connection, and reflective conversation - elicited information on how EMVIS contributed to medical conversations. Five future opportunities for the emotion visualisation of medical conversations were discussed in respect of the human factors and potential requirements. These include communicating uncertainty, addressing user diversity, providing explanatory information, managing attention, and supporting negotiations.

Abstract Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.

The relationship between intellectual property rights (IPRs) and the development of creativity is always a controversial topic. However, it has seldom been explored from the user-centered design (UCD) perspective. This paper describes how the UCD approach has been employed to develop Design Patent Retrieval Application (acronym: DsPLAi), a mobile app aimed to integrate IPRs related information into early design processes to enhance designers’ IP practice and to facilitate the creative process. Interview studies were first conducted to identify end-users’ understanding of IPRs and related practices. Next, participatory design workshops with designers and IP processionals were organized to understand the interaction between the two parties and their needs, thereby deriving requirements for DsPLAi. A prototype of the app was developed and evaluated with ten industrial designers. The prototype received positive feedback in the usability evaluation. The empirical results showed that the provision of IPRs related information at an early stage could be helpful to the design process and that the designers were positive about the use of DsPLAi in their daily design routines.

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results.