The frequent wayfinding-sequence (FWS) methodology: Finding preferred routes in complex virtual environments

Advances in computing techniques, as well as the reduction in the cost of technology, have made possible the viability and spread of complex virtual environments (VEs). However, efficient navigation within these environments remains problematic for the user. Several research projects have shown that users of VEs are often disoriented and have extreme difficulty completing navigational tasks. Furthermore, there is often more than one route to get to a specified destination. Novice users often lack the spatial knowledge needed to pick an appropriate route due to the deficiency of experience with the system. A number of navigation tools such as maps, 3-D thumbnails, trails, and personal agents have been proposed. The introduction of these tools have met with some degree of success, but most researchers agree that new techniques need to be developed to aid users efficiently navigate within complex VEs. In this paper, we propose the frequent wayfinding-sequence (FWS) methodology that uses a modified sequence mining technique to discover a model of routes taken by experienced users of a VE. The model is used to build an interface that provides navigation assistance to novice users by recommending routes. We conducted both real world and simulation experiments using our methodology. Results from the real world experiment suggest that the FWS approach has the potential to improve the user's navigation performance and the quality of the human-computer interaction. Our simulation studies showed that our approach is scalable, efficient, and able to find useful route models for complex VEs.     

Texting and walking: a controlled field study of crossing behaviours and inattentional blindness in Taiwan

The paper investigates the effects of phone use (talking, texting, and listening to music) on the street-crossing behaviours of pedestrians and their inattentional blindness in Taiwan. Recent handsets with touchscreens, as well as more advanced features including multimedia, and mobile applications (apps), exacerbate problems relating to cognitive distraction and reduced situation awareness. A controlled field study using video cameras was conducted for observing pedestrians’ crossing behaviours (e.g. crossing time, sudden stops, looking both ways before crossing, and disobeying traffic signals). Pedestrians were classified into two groups: experimental group (talking, texting, and listening to music) and control group (no phone use). Pedestrians’ inattentional blindness was examined by evaluating whether they saw and heard an unusual object (i.e. a clown) nearby. The results indicate that the proportions of unsafe crossing behaviours (e.g. sudden stops, disobeying traffic signals, and not looking both ways before crossing) were higher among distracted individuals and more pronounced among those using instant-messaging apps. These instant-message app users were the least likely to see the clown, and music listeners were the least likely to hear the horn that the clown was honking. Contributing factors to unsafe behaviours include being a student, having a phone screen of 5?inches or larger, and having unlimited 3G Internet access. Texting message via apps was the leading factor on unsafe crossing behaviours of pedestrians and their inattentional blindness.     

A cognitive pedestrian behavior model for exploratory navigation: Visibility graph based heuristics approach

Navigation is a complex issue in simulating pedestrian dynamics. Many existing studies have investigated how pedestrians navigate from an origin to a specific destination, i.e., traveling to familiar or novel destinations. However, pedestrians’ navigation through built environment without specific destinations still remains as an open issue. This exploratory navigation usually involves more spatial cognitive behaviors and perceptual considerations. To represent realistic route choice and natural movement of theses pedestrians, this paper presents a cognitive pedestrian behavior model with a focus on the space visibility and individual characteristics. Then the sensitivity of model parameters is analyzed, indicating that regional target, memory of visiting records and individual crowd tendency can have varied influences on pedestrians’ navigation decisions and movement patterns. As a case study, the proposed model is implemented using the pedestrian movement data collected from an observational study of Tate Gallery Museum. The simulation results show good consistency with the actual data at the aggregate level and those obtained individually, indicating that our proposed model is credible and can benefit real applications such as master planning of a museum.     

Learning to embed music and metadata for context-aware music recommendation

Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.     

A system for destination and future route prediction based on trajectory mining

Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward–Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin–destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80% and 60% accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user’s historical movement data and predict the destination and future route.     

The uses and gratifications of using Facebook music listening applications

Despite rapid adoption of social media as a means of music listening, little is known about users’ motivations. This study applies the uses and gratifications approach to users’ motivations for using music listening applications on Facebook. Participants completed an online survey, and 153 out of 576 respondents indicated that they used a Facebook music listening application. A principal axis factor analysis identified three different motivations for this usage, namely entertainment, communication, and habitual diversion gratifications. The entertainment and communication gratifications replicate those found in prior uses and gratifications research concerning other social networking features, illustrating the strong similarity between uses of music and social media. However, the habitual diversion gratification may serve to distinguish listening applications from other features. Identifying and explaining these factors is relevant to social media users, musicians and application designers, as they explain what motivates a means of music listening that is gaining prominence.     

Predicting user demographics from music listening information

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it’s recommendations.

     

Mobile information access in the real world: A story of three wireless devices

The importance of the user perspective to the wireless information access experience cannot be understated: simply put, users will not indulge in devices that are perceived to be difficult to use and in technologies that do not offer quality infotainment – combined information and entertainment – content. In this paper, we investigate the impact that mobile devices have on the user wireless infotainment access experience in practice. To this end, we have undertaken an empirical study placed in a ‘real-world’ setting, in which participants undertook typical infotainment access tasks on three different wireless-enabled mobile devices: a laptop, a personal digital assistant and a head mounted display device. Results show that, with the exception of participants’ level of self-consciousness when using such devices in public environments, the user wireless information access experience is generally unaffected by device type. Location was shown, though, to be a significant factor when users engage in tasks such as listening to online music or navigation. Whilst the interaction between device and environment was found to influence entertainment-related tasks in our experiments, the informational ones were not affected. However, the interaction effects between device and user type was found to affect both types of tasks. Lastly, a user’s particular computing experience was shown to influence the perceived ease of wireless information access only in the case of online searching, irrespective of whether this is done for primarily informational purposes or entertainment ones.     

Helping older pedestrians navigate in the city: comparisons of visual,auditory and haptic guidance instructions in a virtual environment

Preserving older pedestrians’ navigation skills in urban environments is a challenge for maintaining their quality of life. However, existing aids do not take into account older people’s perceptual and cognitive declines nor their user experience, and they call upon sensory modalities that are already used during walking. The present study was aimed at comparing different guidance instructions using visual, auditory, and haptic feedback in order to identify the most efficient and best accepted one(s). Sixteen middle-age (non-retired) adults, 21 younger-old (young-retired) adults, and 21 older-old (old-retired) adults performed a navigation task in a virtual environment. The task was performed with visual feedback (directional arrows superimposed on the visual scenes), auditory feedback (sounds in the left/right ear), haptic feedback (vibrotactile information delivered by a wristband), combinations of different types of sensory feedback, or a paper map. The results showed that older people benefited from the sensory guidance instructions, as compared to the paper map. Visual and auditory feedbacks were associated with better performance and user experience than haptic feedback or the paper map, and the benefits were the greatest among the older-old participants, even though the paper-map familiarity was appreciated. Several recommendations for designing pedestrian navigation aids are proposed.     

A Novel framework of EEG-based user identification by analyzing music-listening behavior

This paper introduces a novel framework for user identification by analyzing neuro-signals. Studies regarding Electroencephalography (EEG) revealed that such bio-signals are sensitive, hard to forge, confidential, and unique which the conventional biometric systems like face, speaker, signature and voice lack. Traditionally, researchers investigated the neuro-signal patterns by asking users to perform various imaginary, visual or calculative tasks. In this work, we have analyzed this neuro-signal pattern using audio as stimuli. The EEG signals are recorded simultaneously while user is listening to music. Four different genres of music are considered as users have their own preference and accordingly they respond with different emotions and interests. The users are also asked to provide music preference which acts as a personal identification mechanism. The framework offers the benefit of uniqueness in neuro-signal pattern even with the same music preference by different users. We used two different classifiers i.e. Hidden Markov Model (HMM) based temporal classifier and Support Vector Machine (SVM) for user identification system. A dataset of 2400 EEG signals while listening to music was collected from 60 users. User identification performance of 97.50 % and 93.83 % have been recorded with HMM and SVM classifiers, respectively. Finally, the performance of the system is also evaluated on various emotional states after showing different emotional videos to users.     

Navigation your way: from spontaneous independent exploration to dynamic social journeys

In this article, we describe a novel approach to pedestrian navigation using bearing-based haptic feedback. People are guided in the general direction of their destination via a minimal directional cue, but additional exploration is stimulated by varying feedback based on the potential for taking alternative routes. This extreme navigation method removes the complexities of maps and direction following, concentrating on allowing pedestrians to actively explore their surroundings, rather than offering perfect, but passive, turn-by-turn guidance. We simulate and build two mobile prototypes to examine the possible benefits of this approach, then further extend its impact by considering how social media might be incorporated to provide a real-time, dynamically evolving map of physical locations. The successful use of our mobile prototypes is demonstrated in a realistic field trial, and we discuss the results and interesting participant behaviours that were recorded, validating the predictions from their earlier simulation. We continue by simulating the use of publicly posted status updates and pictures as a proxy for location mapping, showing how these methods can produce comparable navigation results to real-world field trials, highlighting their potential as tools for real-world social journeys.     

虚拟骑行中的三种导航界面的体验度量

相关研究显示,大部分用户在虚拟现实中会遇到导航困难,而目前对导航方式的 研究仍十分稀缺。为此,以沉浸式虚拟骑行为例,对不同导航界面(地图、提示图标、寻路光带) 的用户体验进行了度量,包括效率、满意度、易学性等。试验对象在虚拟环境里沿着场景里的 路径骑行到达终点,在此过程中记录被试的时间、碰撞数等,以此量化用户体验的各要素。实 验结果表明：提示图标导航方式一定程度上能提高虚拟骑行的效率,但不够显著,更重要的是 会显著降低用户对虚拟场景的感知度;用户使用寻路光带导航方式执行任务时,易用性和易学 性方面有显著的提升;使用地图导航的体验与无导航时的体验相差不大。     

Robotic experience companionship in music listening and video watching

We propose the notion of robotic experience companionship (REC): a person’s sense of sharing an experience with a robot. Does a robot’s presence and response to a situation affect a human’s understanding of the situation and of the robot, even without direct human–robot interaction? We present the first experimental assessment of REC, studying people’s experience of entertainment media as they share it with a robot. Both studies use an autonomous custom-designed desktop robot capable of performing gestures synchronized to the media. Study I (\(n=67\)), examining music listening companionship, finds that the robot’s dance-like response to music causes participants to feel that the robot is co-listening with them, and increases their liking of songs. The robot’s response also increases its perceived human character traits. We find REC to be moderated by music listening habits, such that social listeners were more affected by the robot’s response. Study II (\(n=91\)), examining video watching companionship supports these findings, demonstrating that social video viewers enjoy the experience more with the robot present, while habitually solitary viewers do not. Also in line with Study I, the robot’s response to the video clip causes people to attribute more positive human character traits to the robot. This has implications for robots as companions for digital media consumption, but also suggests design implications based on REC for other shared experiences with personal robots.     

Enhancing Musical Experience for the Hearing-Impaired Using Visual and Haptic Displays

This article addresses the broad question of understanding whether and how a combination of tactile and visual information could be used to enhance the experience of music by the hearing impaired. Initially, a background survey was conducted with hearing-impaired people to find out the techniques they used to “listen” to music and how their listening experience might be enhanced. Information obtained from this survey and feedback received from two profoundly deaf musicians were used to guide the initial concept of exploring haptic and visual channels to augment a musical experience.

The proposed solution consisted of a vibrating “Haptic Chair” and a computer display of informative visual effects. The Haptic Chair provided sensory input of vibrations via touch by amplifying vibrations produced by music. The visual display transcoded sequences of information about a piece of music into various visual sequences in real time. These visual sequences initially consisted of abstract animations corresponding to specific features of music such as beat, note onset, tonal context, and so forth. In addition, because most people with impaired hearing place emphasis on lip reading and body gestures to help understand speech and other social interactions, their experiences were explored when they were exposed to human gestures corresponding to musical input.

Rigorous user studies with hearing-impaired participants suggested that musical representation for the hearing impaired should focus on staying as close to the original as possible and is best accompanied by conveying the physics of the representation via an alternate channel of perception. All the hearing-impaired users preferred either the Haptic Chair alone or the Haptic Chair accompanied by a visual display. These results were further strengthened by the fact that user satisfaction was maintained even after continuous use of the system over a period of 3 weeks. One of the comments received from a profoundly deaf user when the Haptic Chair was no longer available (“I am going to be deaf again”), poignantly expressed the level of impact it had made.

The system described in this article has the potential to be a valuable aid in speech therapy, and a user study is being carried out to explore the effectiveness of the Haptic Chair for this purpose. It is also expected that the concepts presented in this paper would be useful in converting other types of environmental sounds into a visual display and/or a tactile input device that might, for example, enable a deaf person to hear a doorbell ring, footsteps approaching from behind, or a person calling him or her, or to make understanding conversations or watching television less stressful. Moreover, the prototype system could be used as an aid in learning to play a musical instrument or to sing in tune.

This research work has shown considerable potential in using existing technology to significantly change the way the deaf community experiences music. We believe the findings presented here will add to the knowledge base of researchers in the field of human–computer interaction interested in developing systems for the hearing impaired.     

A novel method for personalized music recommendation

With the development of digital music technologies, it is an interesting and useful issue to recommend the ‘favored music’ from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the ‘favored music’ which has not been rated by anyone, (b) how to avoid repeatedly recommending the ‘disfavored music’ for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users’ interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users’ logs on music listening records. By the feedback of the user’s options, the proposed methods accommodate the variations of the users’ musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the ‘favored music’ to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.     

Mobile phone use while cycling: Incidence and effects on behaviour and safety

The effects of mobile phone use on cycling behaviour were studied. In study 1, the prevalence of mobile phone use while cycling was assessed. In Groningen 2.2% of cyclists were observed talking on their phone and 0.6% were text messaging or entering a phone number. In study 2, accident-involved cyclists responded to a questionnaire. Only 0.5% stated that they were using their phone at the time of the accident. In study 3, participants used a phone while cycling. The content of the conversation was manipulated and participants also had to enter a text message. Data were compared with just cycling and cycling while listening to music. Telephoning coincided with reduced speed, reduced peripheral vision performance and increased risk and mental effort ratings. Text messaging had the largest negative impact on cycling performance. Higher mental workload and lower speed may account for the relatively low number of people calling involved in accidents.

Statement of Relevance: Although perhaps mainly restricted to flat countries with a large proportion of cyclists, mobile phone use while cycling has increased and may be a threat to traffic safety, similar to phone use while driving a car. In this study, the extent of the problem was assessed by observing the proportion of cyclists using mobile phones, sending questionnaires to accident-involved cyclists and an experimental study was conducted on the effects of mobile phone use while cycling.     

顾及主观判断延误的行人路径选择模型

路径选择是人们日常生活中频繁遭遇的现实问题。针对在行人导航系统的辅助下,行人仍然需要通过主观判断识别路径指示信息中的地标和真实地标是否匹配的问题,建立了顾及主观判断延误的行人道路网络模型。通过将主观判断延误时间和路径行程距离引入前景理论中,发展了一种基于前景理论的行人路径选择模型。以中国地质大学(武汉)部分区域为实验对象进行了仿真实验,结果表明所提出的行人路径选择模型给出的最优路径的主观判断延误时间均不超过相同起止点中的最短主观判断延误时间的0.6 s,虽然其给出的行程距离均比最短路径更长,但均不超过16 m。实验结果表明,所提模型符合行人的实际出行需求。     

The efficiency and walking speed of visually impaired people

All pedestrians have a walking speed which they prefer. This appears to be the speed which, for them, is the most physically efficient. Blind pedestrians, if allowed to set the pace when accompanied by a sighted guide, will prefer to walk at a speed which is close to that of sighted pedestrians. However, when walking independently they adopt a pace which is slower than their preferred walking speed. By converting adopted walking speed into a proportion of preferred walking speed we are able to show that subject data may be pooled and comparisons made which had hitherto been impossible. Furthermore, statements may be made about the physiological efficiency of a performance. For example, efficiency varies with route difficulty, increases with increased preview, and only users of guide dogs reach their optimum efficiency.     

Generation and use of sparse navigation graphs for microscopic pedestrian simulation models

For the spatial design of buildings as well as for the layout of large event areas, the crowd behaviour of the future users plays a significant role. The designing engineer has to make sure that potentially critical situations, such as high densities in pedestrian crowds, are avoided in order to guarantee the integrity, safety and comfort of the users. To this end, computational pedestrian dynamics simulations have been developed and are increasingly used in practice. However, most of the available simulation systems rely on rather simple pedestrian navigation models, which reflect human behaviour only in a limited manner. This paper contributes to enhancing pedestrian simulation models by extending a microscopic model by a navigation graph layer serving as a basis for different routing algorithms. The paper presents an advanced method for the automated generation of a spatially embedded graph which is on the one hand as sparse as possible and on the other hand detailed enough to be able to serve as a navigation basis. Three different pedestrian types were modelled: pedestrians with good local knowledge, pedestrians with partly local knowledge and those without any local knowledge. The corresponding algorithms are discussed in detail. To illustrate how this approach improves on simulation results, an example scenario is presented to demonstrate the difference between results with and without using a graph as constructed here. Another example shows the application of the extended simulation in a real-world engineering context. The article concludes with an outlook of further potential application areas for such navigation graphs.     

基于信标导引的目标方位估计方法

基于准确测量目标方位进而快速实施救援的需要,采用无线信标、电子磁罗盘和卫导组合技术,设计了一种集发射端信标信号全向发射、接收端信标信号定向接收、数字化声谱处理、干扰野值剔除、信标方位精确测量、信标距离概略估计等功能于一体的户外搜救导引系统。并研究分析了一种基于信标导引的目标方位估计方法,该方法通过接收远程、配属在合作目标端信标发射的声波脉冲信号并进行FFT变换,进而通过分析频谱的特征参数来对目标的方位进行准确测量,对目标的距离进行概略估计;测试结果表明该方法比较适合郊区和城市等户外环境中对合作目标进行快速探测搜救和远程监视的需要。