Long bone fractures are common clinical conditions, yet the development of robot systems for closed reduction surgery remains in its early stages. The key challenge in this field is the lack of an efficient and precise path planning algorithm. To address this issue, this study proposes an improved A-star (A*) algorithm for path planning to enhance the accuracy and efficiency of fracture reduction. The algorithm begins by expanding a random node using the fundamental A* algorithm. An artificial potential field (APF) algorithm is then incorporated to optimize the generation of sample node and enhance obstacle avoidance. Additionally, a cylindrical bounding box method is employed for collision detection, and a B-spline curve is utilized to smooth the generated path. The experimental validation is conducted on a fracture reduction robot system, demonstrating that the optimized path achieves clinically acceptable accuracy, significantly enhancing the precision and reliability of the reduction procedure.

Wrist-worn devices hold significant potential for health monitoring, particularly for older people. However, the adoption and continuous usage of these devices are heavily affected by wearing comfort. Currently, there is still a lack of validated assessment tools specifically developed to evaluate the comfort of such devices. Additionally, while pressure is critical for both proper device placement and maintaining comfort, quantitative references for suitbale pressure levels are scarce. Moreover, the impact of age difference on comfort perceptions have been largely overlooked. To address these gaps, we conducted two experiments involving 18 older and 18 younger participants. In Experiment 1, we gathered comfort evaluations from participants while wearing the devices in both home/office and exercise scenarios. In Experiment 2, we focused on pressure, measuring the pressure on participants’ wrists under three strap material conditions and collecting their corresponding comfort evaluations. The results established three dimensions of comfort: movement comfort, thermophysiological comfort, and contact comfort, with their relative importance varying by age and use scenario. Also, we identified comfort-discomfort pressure ranges for both age groups, highlighting the influence of strap material and age on comfort perceptions. These findings offer valuable design guidance for manufacturers and enhance understanding of wrist-mounted device comfort.

Although short video applications are prevalent among young and senior populations, how their engagement behaviors are influenced remains unclear. To address this, we developed a research model grounded in affordance theory and perceived value theory. This model integrates four social media affordances (recommending, searching, meta-voicing, and persistence), three perceived values (utilitarian, hedonic, and social), and three user engagement behaviors (consuming, participating, and producing). Data were collected from 283 young participants and 344 senior participants (aged over 55 and retired) and analyzed using partial least squares structural equation modeling (PLS-SEM). Our findings indicated that the recommending, searching, and persistence affordances significantly positively affect perceived values across both age groups while meta-voicing affordance does not significantly influence any values in the senior group. Moreover, social value consistently drives all three levels of engagement across both age groups. These results offer valuable theoretical and practical insights for researchers and practitioners.

Motivated by the rapid technological advancements achieved in the last five years and the pervasiveness of Artificial Intelligence (AI), this paper investigates the evolving role of HCI and revisits the seven grand challenges outlined in 2019: human-technology symbiosis, human-environment interactions, ethics, privacy and security, well-being, health and eudaimonia, accessibility and universal access, learning and creativity, and social organization and democracy. Through literature analysis, the paper reevaluates the status of each challenge and highlights emerging requirements. Key findings reveal the widespread impact of AI across all domains and emphasize the need for improved AI transparency, alignment with human values, and the development of explainable, personalized, and privacy-preserving technologies to enhance user trust and control. The analysis also highlights the interconnected nature of these challenges and firmly asserts the role of technology in actively supporting human activities and collaborating harmoniously with humans to help them live meaningfully and fulfill aspirations.

With the rise of mobile payment services (MPS), there is an increasing risk of a digital payment divide, particularly for older adults who are slower to adopt these technologies. This study investigates the barriers older adults face in adopting MPS, focusing on perceived financial and privacy risks, and examines how age and experience moderate these relationships. A survey was conducted with 233 older non-users, 260 older users, and 206 younger users. Results revealed age-related differences in risk factors: for older adults, perceived uncertainty and possibility were key predictors, while for younger adults, perceived knowledge and control over consequences were also important. Furthermore, the relationship between perceived risks and the intention to use MPS was moderated by user experience: risks negatively affected intention only for non-users, not users. This finding may be linked to the impact of interface design features.

Physical activity (PA) is critical to health, but PA level declines dramatically in young adulthood. This study aims to compare the effectiveness of different social gamification designs (i.e. cooperation and competition) in mobile fitness applications among occasional exercisers among young adults. A mobile fitness application featuring competitive, cooperative, or individualistic gamification was designed and their effectiveness in promoting PA behaviours and socio-cognitive factors among occasional exercisers was compared through a four-week (i.e. one week for baseline and three weeks for use) field experiment involving 49 participants. The participants used one of the gamified applications with one or more friends in daily life during the experimental period. The results showed that cooperation and competition stimulate different types of social support: cooperation improves companion support, whereas competition improves appraisal support. Both gamification significantly increased daily steps in the first week, but after three-week usage, the increase of the cooperation groups reclined, whereas that of the competition groups was maintained. The application usage in cooperation also declined significantly after three weeks, while that of the other two conditions did not change significantly. The current study provided insights into gamification design for occasional exercisers among Chinese young adults.

The development of technology and the recent Covid-19 has moved a substantial part of education into online environments, and video-based learning has become a common format of online learning. An alternative to enhance the co-learning experience in video-based online learning is to make use of immersive virtual reality (VR) technology. The current study focused on how to build a sense of being together in an immersive VR classroom for video-based learning, when individual learners can schedule and pace their learning on their own. Two alternative in-class interaction mechanisms for video-based learning were proposed (i.e., real-time interaction and timeline-anchored interaction). The impact of in-class interaction design was investigated, together with the impact of different styles of visual embodiment of learners (i.e., realistic and cartoon-styled), through a laboratory experiment involving 48 participants, and the influences on learners’ immersion in a VR classroom, identification with the avatars, perceived social presence of peer learners, and learning outcomes were analyzed. The results showed that enabling in-class interaction with peer learners in VR classrooms enhances immersion in the learning environment and their identification of peer avatars, as well as helping learners to develop a stronger co-presence and more intimate perception of peers. Whereas real-time interaction improves perceived social presence the most, the reduced learning outcome suggests that it may distract learners from the video. The timeline-anchored interaction, on the other hand, improves social presence at no cost of learning performance. Appearance style, however, has no significant influence on learning experiences and outcomes.

Social media is convenient for older adults to obtain and share information (i.e., informational use). However, a major barrier to using social media for older adults is their relatively low social media self-efficacy. The effects of this on informational use and mental well-being have not been well studied. Therefore, this study surveyed 276 older Chinese adults aged 60–90 and constructed a structural equation model. We found that higher social media self-efficacy was strongly and directly associated with more informational use, less loneliness, and higher self-esteem. It also positively affected happiness, mediated by loneliness and self-esteem. Informational use decreased loneliness but did not significantly affect self-esteem. We explained these results by the moderation effects of age and social media self-efficacy. This study confirmed the urgency of increasing older adults’ social media self-efficacy for their mental well-being and successful aging. We also outlined design implications for increasing social media self-efficacy.

Post-purchase trust is formed after an online transaction is completed and the product or service is experienced. It influences consumers’ repurchase intention and the reputation of vendors. The aim of the current study was to deepen our understanding of post-purchase trust in e-Commerce and to develop a text mining-based assessment method by mining consumers’ online comments. By combining the expectancy-confirmation theory and product evaluation theory, this study proposes a comprehensive model of post-purchase trust encompassing consumers’ evaluation of product, delivery, service, and website. The model was verified by a survey involving 249 consumers. The results indicate that both product evaluation factors and transaction supporting factors have positive impacts on post-purchase trust. Based on this theoretical model, we proposed a text mining-based method to measure these factors through text-mining of consumers’ comments. To demonstrate the feasibility and benefits of the method, the method was applied to analyze 1,015,484 consumers’ comments on personal computer products from jd.com, a major Chinese e-Commerce website. The results suggest that the proposed method can provide practitioners with diagnostic suggestions to promote post-purchase trust and understand consumers better. HIGHLIGHTSIntegration of the consumers’ product evaluation model and expectation-confirmation theory is proposed to investigate post-purchase trust in e-commerce.The confirmation of delivery, service, website, and perceived value of products affect consumer satisfaction, thereby affect post-purchase trust.Perceived value is positively affected by perceived price and perceived quality, which can be increased by consumers’ evaluation of the appearance, authenticity, and brand reputation of the product.The proposed model can be used to design text mining-based tools to monitor important factors of post-purchase trust via text mining.

SDespite the enormous potential of wearables, the adoption rate of wearables among older people is low. To better understand the aspects influencing the adoption of wearables by older Chinese adults, this study investigates the changes in the usage behaviors, perceptions, and attitudes of older people toward wearables over four weeks. Twenty older people (age > 60 years) were included, and each of them was assigned a device for use at home. Information about features they desired and used, their perceptions of functional (i.e., affordances) and experiential qualities, perceived values, and adoption intention of wearables were collected before actual use and at weekly meetings during use. Results showed that health data monitoring was the most expected use of wearables before actual use and considered the most valued affordance throughout the period, although the less-than-anticipated actual experience reduced the usage. Interestingly, notification/reminders were not considered as important initially; however, the increasing usage increased its importance. Regression analysis showed that perceived utilitarian value was influential throughout the study, while hedonic value influenced the adoption intention only before actual use. These findings facilitate understanding how older people adopt wearables and provide practical design suggestions that could benefit the elderly population.

Many video sites or learning platforms allow real-time chatting or asynchronous commenting on specific time points during video lectures. Comments, as user-generated knowledge, facilitate social interaction but also affect cognitive learning. The visual layout of these comments can affect learners’ attention and learning, but the effect has rarely been studied. This study compares two common layouts (embedded vs. separated) and considers the content depth of comments through a laboratory eye-tracking experiment involving 40 participants. The results suggest that, with both layouts, learners switched attention to the comments every 10 seconds and stayed focused for 1.3 seconds on average before returning attention to the video. With an embedded layout, learners switched attention more frequently to the comments and remembered more surface-level comments. With a separate layout presenting deep-level comments, learners searched for information faster and performed better on open-book quizzes. We outline the design implications of using timeline-anchored comments to promote online learning.

In massive open online courses (MOOCs), learners have diverse types of motivation. Learners with different motivations have different interaction behaviours, presence, and learning outcomes. However, scant research has investigated the moderating role of learner motivations in the associations between presence and learning outcomes. This study examined MOOC learner motivation and its moderating role by surveying 646 MOOC learners. By exploratory factor analysis, this study identified four types of motivation: interest in knowledge, curiosity and expansion, connection and recognition, and professional relevance. Based on motivation, the study clustered learners into high-motivation, low-motivation, and asocial learners. Both high-motivation and asocial learners reported strong interest in knowledge and professional relevance, but asocial learners reported the lowest level of connection and recognition among the three groups of learners. Despite the low social presence, the asocial learners still had high levels of cognitive and teaching presence and learning outcomes. In addition, learners with higher presence generally perceived higher cognitive learning, but asocial learners with higher social presence were less satisfied. The results highlight the impacts of specific types of motivation to enrol in MOOCs and suggest designing different environments for learners with different motivation types.

The growth of live video streaming (LVS) technology provides new possibilities for online tutoring in that it accommodates a massive number of learners simultaneously. Questions still exist, however, about the extent to which new technology can support interactions between an instructor and a vast number of learners, as well as which factors would influence learners’ interactions with the instructor and peer learners. This study explored these questions by conducting a survey involving 189 senior high school students participating in online LVS tutoring. The results indicated that learner–instructor interaction dominated social interaction in the online tutoring environment with the current system design. This design may also contribute to the development of the perceived presence of peer learners with few direct information exchanges among peers. Social Connectedness and perceived enjoyment positively influenced learner–instructor interaction, whereas social fears and the social presence of the instructor negatively influenced learner–learner interaction.

Interaction is central to any learning experience. Currently, Massive Open Online Courses (MOOCs) rely on discussion forums as a primary means of interaction among learners and instructors. Threaded discussions of such forums help learners to hold an ongoing and recursive discussion over an extended period, but the lack of temporal and spatial contiguity makes it insufficient to deliver a smooth interaction experience. To facilitate such interaction, this study designed DanMOOC, a commenting tool that facilitates timeline-anchored discussion among MOOC learners and instructors. DanMOOC was the first to combine threaded discussions with Danmaku commenting, a video commenting feature that allows viewers of the same video to share comments at the top of the video screen. The design process followed an iterative process of user-centered design. The impact of the new design on learning was assessed empirically through a laboratory experiment comparing DanMOOC with the current MOOC system (video-based learning + forum). The results show that DanMOOC increases learners’ perceptions of social, teaching, and cognitive presence, engages learners more in discussion, and improves learners’ satisfaction with both the course and the platform.

By overlaying timeline-synchronized user comments on videos, Danmaku commenting creates a unique co-viewing experience of online videos. This study aims to understand the reasons for watching or not watching Danmaku videos. From a review of the literature and a pilot study, an initial pool of motivations and hindrances to Danmaku video viewing was gathered. Then, a survey involving 248 participants to identify the underlying factor structures of motivations and hindrances was conducted. Their influences on users’ attitude and behaviors with Danmaku videos were also examined. The results showed that people viewed Danmaku videos to obtain information, entertainment, and social connectedness. Introverted young men with high openness to new experience are more likely to view Danmaku videos. Infrequent viewers refused to watch Danmaku videos mainly because of the visual clutter that resulted from Danmaku comments.

In the spoken language translation pipeline, machine translation systems that are trained solely on written bitexts are often unable to recover from speech recognition errors due to the mismatch in training data. We propose a novel technique to simulate the errors generated by an ASR system, using the ASR system’s pronunciation dictionary and language model. Lexical entries in the pronunciation dictionary are converted into phoneme sequences using a text-to-speech (TTS) analyzer and stored in a phoneme-to-word translation model. The translation model and ASR language model are combined into a phonemeto-word MT system that “damages” clean texts to look like ASR outputs based on acoustic confusions. Training texts are TTSconverted and damaged into synthetic ASR data for use as adaptation data for training a speech translation system. Our proposed technique yields consistent improvements in translation quality on English-French lectures.

The objective of this study was to investigate the impact of knowledge representations on problem-oriented learning in online learning environments. The study compared the impact of knowledge map representation with traditional hierarchical representation with regard to learning memory and problem-solving performance. Twenty-nine students participated in an experiment in which they studied online materials with the goal of solving two programming problems (simple and complex). It was found that participants who used the hierarchical representation read in the depth-first sequence, whereas participants who used the knowledge map representation read in a sequence reflecting the system running mechanism implied by the graphical representation. In addition, participants who used the knowledge map representation had better memory of the learning content, especially about relations between knowledge nodes. When solving the complex problem, participants who used the knowledge map representation made a deeper analysis of the problem and had better problem-solving performance. These results were not significant in the simple problem-solving task.

We introduce recurrent neural networkbased Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilingual contexts.Traditional back-off n-gram models suffer under the sparse nature of MTUs which makes estimation of highorder sequence models challenging.We tackle the sparsity problem by modeling MTUs both as bags-of-words and as a sequence of individual source and target words.Our best results improve the output of a phrase-based statistical machine translation system trained on WMT 2012 French-English data by up to 1.5 BLEU, and we outperform the traditional n-gram based MTU approach by up to 0.8 BLEU.

The potential of microblogging networks to spread information, ideas, and influence via social links has been recognized. Individuals at central or critical positions in the microblogging networks are expected to play an important role in spreading information, but this belief must be tested with empirical investigations in actual information dissemination. Furthermore, it is possible that individuals with different types of structural importance influence the information dissemination in different ways. This article examines the impact of five structural properties (degree, betweenness, closeness, tie strength, and k-value) on information dissemination capabilities by tracking the dissemination of 150 messages in Sina Weibo. The results highlight the importance of betweenness centrality, which best explains the influence on information dissemination distance and coverage size. It also best predicts the information dissemination efficiency. Degree centrality is found to be a strong predictor of one’s influence among immediate followers. Tie strength negatively correlates with influence on the information dissemination distance and coverage size. These structural properties address different aspects of the information dissemination power and should be used in a complementary way when planning information dissemination strategies in microblogging systems.

Tagging allows users to organize their information and retrieve it later with multiple, freely chosen keywords, which is impossible with categorical folders. The need to organize information for personal later retrieval has been found to be one of the most important motivations for tagging. Despite the popularity of the concept, more empirical evidence is still required to verify the real benefit of tagging for information organization and retrieval. Furthermore, the problems of inconsistency in tagging hamper the usefulness of tagging as an effective organization tool. The current study aims to investigate users' motivation, performance, and workload when they use tagging to organize personal information and how the system design could improve the process. First, a pilot study combining think-aloud and interviews was conducted to obtain insights on why and how users select tags. Then, the first experiment with 40 participants was conducted to empirically compare the performance and workload difference in information organization and retrieval tasks between categorization and tagging interfaces. The results show that tagging users reported a significantly higher level of mental demand and frustration when performing organizational tasks and a significantly higher level of temporal demand and error rate when performing retrieval tasks compared with categorization. However, tagging users tend to have better memory of the organized content. The second experiment aimed to study how individual tagging consistency can be improved by the proper visualization of tag suggestions. The impact of frequency visualization by font size and semantically clustering was studied with 40 participants. The results show that semantically clustered tag clouds improve tagging consistency significantly; when a semantic clustering effect is presented, frequency visualization by font size can significantly alleviate the physical demand perceived by users.

Interactivity is considered a prominent characteristic of mobile advertising; however, a properly developed tool to measure the concept has not been developed. This study focusses on the development and validation of an instrument for measuring the interactivity perceived by individuals who interact with mobile advertisements on handheld devices. The confirmatory factor analysis results show that perceived interactivity in mobile advertising is comprised of six correlated but distinct constructs: user control, synchronicity, two-way communication, connectedness, playfulness and interpersonal communication. The result from a series of tests and examinations indicated that the multidimensional scale exhibits content validity and discriminant validity. The internal consistency of the scale reaches an acceptable level. The relationship with personal variables was also analysed. Gender, education, monthly income, years using cell phones, monthly cell phone charges, monthly cost for data services on cell phones and previous experiences with mobile advertisements have no influences on respondent interactivity perception with mobile advertisements. But user age is negatively correlated with perceived interactivity and years using the Internet are positively correlated with perceived interactivity. Young people with more Internet experience are more likely to perceive interactivity of mobile advertisements.

There are indications that interactivity could benefit the effectiveness of mobile advertisements. But there are few guidelines on how to design interactive mobile advertisements. This study investigated the influences of various design features of mobile advertisements on perceived interactivity and the relationship between perceived interactivity and attitude toward mobile advertisements. An experiment consisting of 2 sessions was conducted to test 5 proposed hypotheses with 40 participants. Results showed that the number of user control options and the customizability of advertisements have significant influences on users' perception of interactivity. The more options a message advertisement provides, the more interactive the participants perceive the advertisement; a customizable game advertisement is perceived as more interactive than an uncustomizable game advertisement. But no significant effects of presenting instructional information of mobile Web advertisements and including humorous elements were found. Finally, perceived interactivity was found a strong predictor of attitude toward mobile advertisement.

People with different cultural backgrounds think and behave differently. Different cultural characteristics of users of computing systems become significant issues. Studies on cultural differences from various perspectives such as cultural dimensions, presentation, and cognition provide useful information. However, we have not yet succeeded in making computing systems into a very usable environment supporting users with different cultural backgrounds. This paper provides an overview and a comprehensive appraisal of past literatures for cross-cultural effects on computing systems. Cultural dimensions in Hofstede's cultural model and Hall's cultural model are introduced. Then the cross-cultural effects on human-computer interaction are discussed from three perspectives: perceptual, cognitive, and affective, respectively. Finally, cross-cultural challenges in new technologies are discussed in order to throw light onto the future of computing.

Unlike most Web portals in the world, Chinese Web portals are characterized by a huge amount of information, excessive visual stimuli, and very long Web pages. The objective of this study is to investigate the effect of such rich Web portal designs and floating animations on visual search, emphasizing a comparison between Chinese users and German users. Two experiments were conducted to test 2 proposed hypotheses. Experiment 1 studied the effect of Web portal design (rich and simple) on visual search performance (performance time, errors, and satisfaction) with both Chinese and German participants. Experiment 2 studied the effects of static animations (leaderboards, couplets, and large squares) and floating animations (moving down, moving up/down, and random movement) on visual search performance on Web portals. The dependent variables were the performance time, error, satisfaction, and animation recognition. The results indicated that participants using simple Web portals searched faster, made fewer errors, and were more satisfied than participants using rich Web portals. No significant differences were found between the performance time of Chinese participants and German participants. However, satisfaction of Chinese participants was found to be less influenced by the differences between simple and rich Web portal designs, compared with German participants. No significant differences were found in performance time and animation recognition between static animations and floating animations, which indicated that users are able to detect the pattern of animation movements and were able to avoid floating animations as well as static animations. People searching pages with randomly floating animations were found to use significantly more time compared with those searching pages with no animations. Furthermore, users' satisfaction for pages with randomly floating animations, moving down animations, and moving up/down animations was significantly lower than for pages with no animations. Implications for designers and for future research are discussed.

This paper introduces a speech separation system as a front-end processing step for automatic speech recognition (ASR). It employs computational auditory scene analysis (CASA) to separate the target speech from the interference speech. Specifically, the mixed speech is preprocessed based on auditory peripheral model. Then a pitch tracking is conducted and the dominant pitch is used as a main cue to find the target speech. Next, the time frequency (TF) units are merged into many segments. These segments are then combined into streams via CASA initial grouping. A regrouping strategy is employed to refine these streams via amplitude modulate (AM) cues, which are finally organized by the speaker recognition techniques into corresponding speakers. Finally, the output streams are reconstructed to compensate the missing data in the abovementioned processing steps by a cluster based feature reconstruction. Experimental results of ASR show that at low TMR (<-6dB) the proposed method offers significantly higher recognition accuracy. Index term: speech separation, CASA, speaker recognition, pitch tracking, units grouping, reconstruction

A novel method is proposed, which is for performing just-in- time adaptation on language models in Chinese speech recognition using Web search engines. Latent semantic analysis (LSA) is employed to change the probability distribution of N-gram language model. The method has two advantages. First, it needs relatively small amount of data which can be obtained from Web on-the-fly. Second, comparing to traditional adaptation formula of LSA, the proposed approach is more efficient, which ensures second pass decoding to be performed with high speed. Experiments show that the perplexity of language model is reduced by over 13% after adaptation. A 4.29% relative reduction on WER is achieved in large vocabulary Chinese speech recognition over standard test set.