Digital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety

Mobile technology is a cost effective and scalable platform for developing a therapeutic intervention. This paper discusses the development of a mobile application for people suffering with depression and anxiety. The application which we have developed is similar to a Cognitive Behavior Therapy (CBT) website, which is freely available on the internet. Past research has shown that CBT delivered over the internet is effective in alleviating the depressive symptoms in users. But, this delivery method is associated with some innate drawbacks, which caused user dropout and reduced adherence to the therapy. To overcome these shortfalls, from web based CBT delivery, a mobile application called MoodTrainer was developed. The application is equipped with mobile specific interventions and CBT modules which aim at delivering a dynamic supportive psychotherapy to the user. The mobile specific interventions using this application ensures that the user is constantly engaged with the application and focused to change the negative thought process. We present MoodTrainer as a self-efficacy tool and virtual CBT that is not meant to replace a clinical caregiver. Rather, it is a supportive tool that can be used to self-monitor, as well as a monitoring aid for clinicians.

To elucidate the structure of nurses’ clinical decisions based on visual observation; we established objectives targeting nurses with varying levels of experience. Subjects: Thirty-three nurses. As simulated patient information, written information on an 85-year old inpatient with pneumonia was provided to subjects. Four images of a simulated patient room with the simulated patient in the bed, each image was displayed on the monitor for five seconds. After observation, while confirming the path of line of sight during observing simulated patient room images, which was measured with Talk Eye II, subjects reviewed their thought processes and made an oral report. No significant differences were observed in indices of eye movement based on years of clinical experiences. But, this indicated that potential awareness and peripheral vision might be used to prioritize the order of areas to observe. There were 58 types of observation items, and then divided into the seven categories. From the relationship of included in the thought process, four types of thoughts were extracted. Overall, the proportion of visual observation being reflected in verbal data was 50.4–68.9%. Among nurses with 10 or more years of clinical experience, their visual observation was reflected in verbal data slightly less than among nurses with less clinical experiences. The results of analyses showed the eye movement of nurses, potential awareness and peripheral vision were used to determine the priority of areas for observation. In addition, during the observation of a simulated patient room, and years of clinical experience affected thought type.

Early and accurate diagnosis of sepsis is critical for patient safety. However, this is a challenging task due to the very general symptoms associated with sepsis, the immaturity of the tools used by the clinicians as well as the time-delays associated with the diagnostic methods used today. This paper explores current literature regarding guidelines for clinical decision support, and support for sepsis diagnosis in particular, together with guidelines extracted from interviews with four clinicians and one biomedical analyst working at a hospital and clinical laboratory in Sweden. The results indicate the need for the development of visual and interactive aids for enabling early and accurate diagnosis of sepsis.

This paper describes the APSEN system, a pre-screening tool for detecting sleep apnea in a home environment. The system was designed and evaluated in two parts; the apnea detection using SpO₂ and the posture detection using IR images. The two parts can work together or independently. During the preliminary study, the apnea detection algorithm was evaluated using an online database, and the right algorithms for detecting the sleep posture were determined. In the overnight study, both of the subsystems were tested on 10 subjects. The average accuracy for the apnea detection algorithm was 71.51% for apnea conditions, and 98.68% for normal conditions. For the posture detection algorithms, during the overnight study, the average accuracies are 74.91% and 89.71% for SVM and CNN, respectively. The results represented in the paper indicate that the APSEN system could be used to detect apnea and postural apnea in a home environment.

In Japan, it is no secret among nurses that some hospital nurses can sense that a patient is close to death in spite of there being no obvious changes. However, no empirical data about this phenomenon have ever been compiled. The purpose of this study is to clarify the characteristics of nurses with such ability. A questionnaire survey was given to 262 nurses anonymously during November 2013. The items to be asked in the questionnaire were whether or not they had ever sensed a patients’ being close to death as a dependent variable, and their charcteristics as independent variables. 143 nurses were responded. The mean age of the respondents was 50.2. 47 respondents responded “yes”, and 92 responded “no” to the question, “whether they had ever sensed that a patient was close to death without obvious changes in their vital signs”. As a result of chi-square test, significantly relevant to this variable were “educational background (BScN and Junior College)”, and “possessed license (RN and LPN)”. The t-test showed that a significant difference was noticed in years of experience as an RN between “yes” and “no” groups. These results suggest that the ability to sense patients’ coming close to the end of life without the presence of obviously objective signs depends not on their natural abilities, but on their experience. Of course, we must consider that these responses were based on self-report, so the issues such as confirmation or hindsight bias related to heuristics may take place. However, the frequency of nurses who self-reported they were able to sense patients close to death without any obvious changes shows something more than just a rumor among nurses.

DICOM images are the centerpiece of radiological imaging. They contain a lot of metadata information about the patient, procedure, sequence of images, device and location. To modify, annotate or simply anonymize images for distribution, we often need to convert DICOM images to another format like jpeg since there are a number of image manipulation tools available for jpeg images compared to DICOM. As part of a research at our institution to customize radiology images to assess cognitive ability of multiple user groups, we created an open-source tool called Jpg2DicomTags, which is able to extract DICOM metadata tags, convert images to lossless jpg that can be manipulated and subsequently reconvert jpg images to DICOM by adding back the metadata tags. This tool provides a simple, easy to use user-interface for a tedious manual task that providers, researchers and patients might often need to do.

Laparoscopic surgery is thought to be more difficult to acquire the surgical technique compared with conventional one. Eye movement differences between novices and experts have been shown in the various fields. However, a few papers compared the eye gaze movement behavior of novice and expert surgeons during performance of a laparoscopic surgery task with a simulator. The examinee operated the same case of the laparoscopic cholecystectomy of the simulator, and the eye movement detection method was a pupil corneal reflection method. The expert operator showed economical hand and eye movement compared with novices. Once their act change to a camera operator, their gaze behavior seemed to change to the trainer’s one. The medical students improved to shorten the duration time in procedure in one week of training, however, the gaging pattern did not change. Using this eye tracking system, the new educational system can be established to train the medical student, novice surgeon, and also expert surgeons as trainer.

Given the diversity and complexity of the Health Information Systems (HIS), and taking into account the impact of this type of systems in the clinical performance and patient outcome, a rigorous evaluation process in the system development life cycle (SDLC) is extremely important. An effective evaluation during development not only promotes the quality of the final solution, but also ensures motivated users, error-free systems, and can even establish good practices to minimize costs in future developments. However, the HIS evaluation is a difficult process due to the complex nature of the health care domain, the objects being evaluated, as well as the comprehensiveness of the concept of the evaluation itself. The present work intends to explore, based on a literature review, the main methods of HIS evaluation to support the development, identifying in which stage of the SDLC these methods can be applied. Additionally, this work discusses the reasons for the evaluation of such systems, illustrating these issues with two real case studies of HIS implementations, in which some of the methods were successfully applied.

A major issue in designing digital healthcare software solutions is ensuring they meet the clinical needs and requirements of key services, as well as the expectations of various healthcare professionals. Modern software requirements engineering must be adapted to cater for this demand; we argue that traditional (and popular) requirements engineering processes – particularly in relation to the elicitation and validation of key requirements – may not be the most appropriate within the context of a multi-disciplinary team of healthcare professionals. Successful software requirements engineering is vital in ensuring that digital healthcare solutions fulfill expectations and meet the clinical needs; we thus propose that new methods of gathering requirements in the ‘third space’ are needed. This paper draws on a case study of the multi-disciplinary team of healthcare professionals involved in the diagnosis and support of autism spectrum disorders (ASD) in young children within the UK’s National Health Service (NHS). It is worth noting that, in the context of our case study, requirements engineering is an iterative process and requires the input of numerous stakeholders from often stretched and fragmented services.

Excessive growth hormone secretion will result in acromegaly affect metabolic function. Patients with acromegaly is 2–4 times greater risk of death than the normal. Early diagnosis is the key follow-up treatment of acromegaly. The clinical diagnosis is based on typical acromegaly the face and body features, endocrine and radiological. However, acromegaly diagnosis is still quite deferred. Typical acromegaly, with the symptoms and appearance, the physician can diagnose. Obvious early symptoms, diagnosis is not easy. As imaging technology advances, one after another to explore the diagnosis of acromegaly, however, did not the size of the stereoscopic 3D image. The aim of this study is to compare the compare the Receiver operating characteristic (ROC) and discriminant analysis for acromegaly detection by three dimensional facial measurements. To explore the difference of detection rate between the two analysis methods. The result shows that the accuracies of three categories from the univariate discriminant analysis, the lateral angles displayed the highest accuracy between all three categories in the female but the lowest rate for the ROC analysis. However, the lateral angles displayed the lowest accuracy between all three categories in the male and the lowest rate for the ROC analysis. The lateral angles, calculated from the two prominent variables, made a larger difference than the other two categories. From the result, it shows that the accuracy difference analysis between the two analysis methods in both genders. The difference could come from the different operation of the analysis methods. It could use the different analysis method to analyze the different facial dimensions for the acromegaly detection in the future and increase the accuracy for disease detection.

Objective: To systematically review the studies related to the functionality and usability evaluation of diabetes mobile apps. Method: We searched three electronic databases: PubMed, Scopus, and Cochrane. The search terms used were “mobile app”, “mobile application”, “diabetes”, and “evaluation”. We limited the articles to those that were written in English and published from January 1, 2006 to October 4, 2016. Results: There were seven articles focused on type 1 diabetes, two articles focused on type 2 diabetes, two articles focused on both type 1 and type 2 diabetes, nine articles focused on diabetes that authors did not state specific type. With regard to types of evaluation, only one study reported solely on functionality, seven studies reported usability, and twelve studies reported both functionality and usability. The methods used for evaluations included survey, interview, laboratory testing, user testing, questionnaire, expert evaluation, and heuristic evaluation. Conclusion: Future studies should consider the standard evaluation methods for evaluate functionality and usability of diabetes self-management (DSM) apps.

Previously, I introduced the concept of affordance to support dementia persons. Limited merits of affordance for supporting dementia persons are pointed out by Bozeat and Hodges. In addition, after the extension of Gibson’s concept of affordance, it is mainly applied to the interface design. Based on the concept of affordance by Gibson, I proposed a dementia person support mechanism in which functions or meanings of things can be suggested. It is based on abduction framework and performed under the context of chance discovery to determine affordance. That is, the suggestion is not offered explicitly. I showed my assumption that complex situation can be transformed to a combination of simple situations and necessity of develop a mechanism to transform complex situation to a combination of simple situations. In addition I discussed it as curation in chance discovery. Thus the framework can be realized by the introduction of shikake’s concept. A shikake is a trigger to start a certain action or to change person’s mind and behaviour. As a result of the action, all or part of problem will be solved. It sometimes is not the person’s will. In this paper, I will discuss the support of dementia persons as an installation of shikake in the environment. By the installation of shikake dementia persons can be implicitly guided to behave properly. It can be regarded as a proper selection of affordance by a proper curation. I will also discuss this type of issue from the viewpoint of the first-person research and information design.

According to the estimation of US Census Bureau, the age demographic will change from 13 percent of the population aged 65 and older in 2010 to 19 percent in 2030 [1]. With the fast growing number of elderly population, designers may be driven by market to consider an aging-centered design. However, the real challenge of aging-centered design may not only be the preference or interest by age difference but also the technology gap of using computer.From the user testing results of a project on human-centered website design for elderly, we found out that elderly have lower performance than young people with a lower efficiency and a higher error rate. However, the difference wasn’t shown with a statistical significance because there’s a big in-between-group variance in elderly group. During the user testing process, an inconsistency of computer experience and skill level difference between elder users has been shown in their behavior. Some elderly with more computer experience show strong confidence in performing tasks independently and some totally rely on the guidance of experimenter. This result implies aging may not be the only factor affects user’s behavior in aging-centered design.In this paper, we planned a 2 by 2 factorial experiment. Our goal is to carefully examine the effects of each factor and their interactions. From the experiment, we expect to have 2 key findings: (1) Computer skill level difference affects the performance and it is confounded with the age factor. (2) Users’ subjective perceived value of the website will affect users’ subjective rating of usability.By this experiment, we could confirm that aging is not the only factor that prevents us from applying a universal design to different age groups. The emphasis on of aging-centered design may be highlighting the technology gap in between elderly.

At group home “Terado”, improvement of quality of lives of the elderly suffered from dementia is being attempted through a method called Media Therapy using interactive digital photo albums in order to achieve enjoyable days and realization of individuality in their lives. In Media Therapy, we utilize an interactive digital photo album that is a collection of photographs and videos of the personal history and life story of a resident with dementia. This album is used for several sessions where the resident watches this album projected on a screen with the people involved such as his/her family, the care staff, his/her regular doctor, nurse or occupational therapist, and enjoy a casual conversation while viewing the video. As a result, the resident displayed mental calmness during the implementation of Media Therapy. Moreover, by sharing the life history of the resident, the care staff also displayed improvement in their care skill and grew confidence toward care work. Judging from both subjective opinions and objective data, there was clear improvement in the quality of care. In this study, we report the result of applying this Media Therapy to the team care of care staff, which is necessary in the frontline care work. The interactive digital photo album produced for Media Therapy was used for the daily recreation at the group home, which involved not only the resident for whom the album was produced, but also other residents of the facility. Reduction of BPSD (behavioral and psychological symptom of dementia), which is difficult to treat through dementia care, was observed. This is inferred to have resulted from the change in the relationship between the dementia patient and the care staff, and the patient and other residents. Through the power of human relationship, the hardship of the dementia patient was alleviated, and resulted in the reduction of BPSD. Based on this observation, we focused on the relationship between the caregiver and care receiver, and discussed the method for constructing the relationship between them, which is the basis of care work.

The aging of Japan is proceeding at an unprecedented rate in the world, and the aging rate is very high. As the aging society advances and the living environment changes, the environment surrounding the elderly is also changing. Along with then, various social problems are caused. For example, a syndrome of shut itself up of the elderly may be mentioned. “Housebound” is to spend most of the living space and the living time in the house, resulting in the disuse of cognitive functions and further the opportunities and motivation for activities such as going out and interpersonal contact decreasing. In addition, there are elderly who are shut itself do need various support and nursing care in many cases.As for prevention, it is not only to increase the frequency of outings but also to revitalize all aspects of life of elderly by playing a role in society.Thus, in order to prevent the decline of cognitive function, it is thought that the connection with society becomes increasingly important. While the younger generations are accordingly expected to be talking partners for aged people, there is a problem in that they are unfamiliar with how to communicate with the elderly because so many of them grew up in small families without grandfathers or grandmothers. We examined the differences in the mental burden and the quality of communication between patients and caregivers/volunteers when they used photos as communication support content in order to find the best medium for communication. We revealed that what category is the more ideal as the contents but we did not mention the photos in the category. We will investigate the differences in the mental states and communication quality of elderly people and their younger conversation partners when photos are used to support communication, and quantification the best medium for this purpose.

The dementia counseling is a dementia care that cures physiologically unstable situation of a person with dementia, through receptive and attentive conversations. A person with dementia should receive the counseling as often as possible. However, it is difficult for a limited number of caregivers to spare sufficient time and effort. This motivated us to exploit the virtual agent technology we are developing, for implementing daily dementia counseling system at home. However, our previous system relies on static dialogue scripts. Therefore, it is difficult to realize person-centered conversations that are essential to the dementia counseling. In this paper, we propose a method that dynamically generates personalized dialogues for individual people with dementia. The proposed method extensively uses life history and linked open data (LOD). More specifically, we obtain the life history of a user based on The Center Method, then the system choose appropriate conversation considering the history. During the conversation, the system finds new information in LOD relevant to the response and uses it to develop further conversation. We also implement a prototype to show practical feasibility of the proposed method.

With the development of society, the smart phone for the elderly has been developed rapidly. To improve the design of the smart phone icons for the elderly, a set of experiments were made to study the usability differences between multi-colored icons and monochromatic icons. 8 pairs of icons where a pair of icons was composed of a multi-colored icon and a monochromatic icon with the same function were prepared. These 16 icons whose height are 9.81 mm were placed in random order in the Xiaomi MI 4 smart phone installed with the android system. 24 retired teachers aged from 60–70 of the university were called as participants. They were asked to find the right icon as required and click on it with one of their fingers. For every subjects, there were 6 finding tasks followed by satisfaction questionnaire surveys. Eye movement data and satisfaction questionnaire surveys data were acquired and analyzed to get the results: monochromatic flat icons were easier to recognize and operate than the ones that designed with multi-colors for old people, even though they may have monotonous forms; age 60–65 or 66–70 was not the reason one made an operation mistake; some older people liked multi-colored flat icons more than monochromatic flat icons for their colorful vision experience.

The ICT-based elderly monitoring systems attract great attention as a promising technology for home elderly care. However, the conventional systems have limitations of deployment cost and invasiveness, the effort of activity labeling, and a lack of communication. To cope with the limitations, we propose a system that captures activities of daily living (ADL) of the elderly, based on speech dialogue triggered by environment changes. Specifically, we deploy Autonomous Sensor Boxes, developed in our previous study, within a house of the elderly. The boxes gather and send house environmental data to the cloud. Then, the Change Finder algorithm is applied to the time-series data, to detect changes in the house online. On detecting a change, the Virtual Agent (VA) in the house asks the elderly what he/she is doing now. The elderly speaks to the VA, by which an ADL is recorded in the system. The proposed system can capture ADL with non-invasive sensing and create an opportunity for communication.

This study explores a system that estimates the health condition of an elderly individual using nonverbal information from daily conversations.We have already confirmed the effectiveness of using the fundamental frequency (F0) to estimate the atmosphere of a conversation between young individuals. A smooth conversation has a tendency for its average value of F0 (Ave-F0) to increase slightly, and its standard deviation value (SD-F0) to increase significantly compared with a non-smooth conversation. The differences are significant when using a t-test, where the confidence level is 95%. We confirmed that Ave-F0 and SD-F0 are useful in separating laughter utterances from usual speech utterances.In this paper, we report on the acoustic analysis results of a free conversation between elderly individuals, and compare it with the analysis results of young individuals. We describe the possibility of estimating a health condition using F0 characteristics.

With the deepening of aging and low birth rate in China, the single elderly or old couple living alone is more and more, who has a higher risk of senile dementia caused by disuse of cognitive function because of loneliness without communication. We propose an intergenerational conversation support system for Chinese elders for prevention of senile dementia. The most important part of this system is photos as contents, which can provide common topics to make conversation comfortable. This study aims to provide appropriate photos for conversation without burden between the elderly and the young. In order to examine the difference of the mental burden and the quality of communication by using photos as content, we measured the burden of both the young and the elderly depending on photo categories of “Food”, “Events”, “School” and “Commodity”. The methods of measuring burden were stress check, questionnaire and expression analysis. Results suggest that the more photos have in common between the elderly and the young, the less stress they have.

Understanding multi-dimensional data and in particular multi-dimensional dependencies is hard. Information visualization can help to understand this type of data. Still, the problem of how users gain insights from such visualizations is not well understood. Both the visualizations and the users play a role in understanding the data. In a case study, using both, a scatterplot matrix and a HyperSlice with six-dimensional data, we asked 16 participants to think aloud and measured insights during the process of analyzing the data. The amount of insights was strongly correlated with spatial abilities. Interestingly, all users were able to complete an optimization task independently of self-reported understanding of the data.

The reflective stage, which is crucial for behavior change, can be facilitated with suitable visualizations that allow users to answer specific questions with regard to their health data. To date, effective visualizations which combine time series data and the appraisal of this data in one chart are, however, rare. To close this gap in research, twenty participants compared two alternative long-term visualizations of health behavior: an accumulated bar chart and a point chart which both include appraisals of the underlying health data based on current recommendations of leading health organizations, such as the World Health Organization or the European Food Information Council. Participants answered three types of question (progress over time, correlations between different health behaviors, and health consciousness). The sequence of visualization for the underlying data sets was cross balanced over participants. The accumulated bar chart resulted in more trials in which participants were unable to answer. In some cases, this type of visualization also resulted in biased interpretations with regard to progress over time and health consciousness. Summarizing, we recommend the point chart, in which the background is colored according to the recommendation of the respective health behavior. Both types of visualization are, however, not optimal for the identification of correlations.

Massive online communication systems such as social networks, message boards and comment sections are widely used, yet fail in conveying a diverse public opinion. Limitations of models and protocols do not allow users to precisely express their intention and to maintain a complete overview in large-scale discussions. Data-driven approaches fail as well, as they remove the nuances of human communication and use coarse representations like trends, summaries and abstract visualizations. We argue that a new discussion model and a large-scale communication protocol is needed. We evaluate the comprehensibility of a hyperedge connection in modeling arguments for online discussions. An initial mechanical turk study ($$n=200$$) revealed that 30% of the subjects intuitively considered using hyperedges. This was followed by a user study of a prototype ($$n=51$$), where 80% actively used hyperedges. Both findings were independent of user diversity factors (age, gender, graph theory knowledge). The prototypical implementation was evaluated positively.

A common understanding of the state of a research project is vital for project planning and decision making in interdisciplinary research cooperation. Surveys in a production technology based environment shows, that project planning and decision support tools exist and are known, however they are not used in practice. Interviews indicate that the poor usage of such tools originate from the complexity of the tools and that there is no perceptible added value for the performing researcher in using these tools. A requirements analysis is performed to extract the non-functional and functional requirements of a web-based project planning tool, which is developed and tested in an interdisciplinary research cooperation, called the Cluster of Excellence for production research at the RWTH Aachen University. User interviews show that the acceptance of such tools is strongly related to the presence of all features that are considered vital by the user for project planning and decision support.

This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.

Increasing wearable usage in healthcare faces the challenge in low long-term adoption partially due to a redundancy of devices and lack of meaningful uses of wearable technology. This paper aims at the reality needs to present a holistic view of data analytics and medical wearables; and clarify how to apply analytics techniques to a wearable problem or opportunity. The paper addresses the challenges related to undergoing data analytics with medical wearables, details how certain data mining and analytical techniques impact on processing wearable data, and outlines a framework developed for using data analytics with medical wearables data.

Present article outlines characteristics of a general task analysis in terms of digital health visualization evaluation and design. Furthermore, a number of methodological approaches are discussed. One example, in which a hierarchical structure was empirically built with semantic classification by 98 users, will be discussed together with the expected benefits of its successful implementation with respect to system development and human factors research on health data visualizations. It is concluded that experimental approaches to taxonomy construction offer considerable promise in capturing tasks which are relevant but that further investigation is needed validating and iteratively extending the abstract task structures. We thus recommend based on our experiences to conduct a combination of semantic classification with users and hierarchical task analysis to capture all needed task abstraction levels.

A basic problem in the design of EEG signal based devices, which could help the upper limb disabled soldiers carrying on their shooting tasks, is presented by the extraction and classification of EEG features. Such system can extract EEG signals features during soldiers act their shooting tasks and transform the features into binary control signals for operation. This paper is about analyzing the EEG signals of health soldiers during their rifle practice during the aiming period, which is the most vital step for shooting and extracting EEG features. We put the special features into a support vector machine to classify two classes signals and compare the signals of the holding period with an aiming period. Results show that the power of alpha and beta in occipital and parietal regions have significant changed, so does the power of theta rhythm in frontal area. Thus, we put the combine of alpha and beta power which as EEG features into our support vector machine’s classification device, then get the accurate classification rates compare with the one that comes from theta power. The alpha and beta power join as the characters get higher classification accuracy than the theta.

In 2015, the Association of Workers’ Compensation Boards of Canada recorded around quarter-million workplace injuries, a staggering figure which does not include incidents that go undocumented. A lack of health and safety training and/or lack of safety awareness can lead to workplace injuries and in the worst cases a workplace death. It is imperative that organizations make Occupational Health and Safety (OHS) one of their top priorities. In this paper, we explore the implementation of an adaptive personalized learning support system within a game that is centered on health and safety training. The design of the game incorporates a feedback loop that constantly evaluates the player’s performance while they complete learning challenges. As the players proceed within the game’s environment their profile is constantly updated thus providing an insight into their strengths and weaknesses. The game is evolutionary i.e. it is designed to adjust the challenges given to the player in order to focus on improving the player’s underperforming skills. This game is a step towards overcoming a lack of health and safety training observed in small and medium enterprises. Through this game we try to create a fun and motivating environment where workers are being exposed to the health and safety mindset and learning through relevant challenges.The game is made in collaboration with the public services health and safety association (PSHSA) based in Toronto. The learning challenges aim to better the player’s health and safety performance in the organizational performance metric (OPM) and hone their underlying health and safety skills.

In this paper, we present an external pipeline inspection robotic system capable of detecting the surface defects on above-ground pipelines and modeling their degradation. This system consists of two subsystems, the main base and the sensing system. The main base is a self-driving autonomous ground vehicle (AGV) equipped with Lidar, acoustic sensors and motor encoders, which can track the pipeline on uneven terrains. The sensing system includes two cameras attached to a C-arm, which rotates around the pipe. The two cameras are placed 180° from each other and can take pictures at 90° intervals, allowing the system to analyze the full 360° of the pipe with only half of the rotation as compared to a single camera system. When the C-arm encounters a flange or support, it retracts off the pipe, moves past the obstacle, and extends back to continue taking images. These images are used for defect detection. The detected defect data is then used for modeling the defect degradation and predicting when the defects become critical and require maintenance.

The effects of digital car dashboard interface factors on driving experience were studied. Specifically, representative car dashboard interface design elements were analyzed and five representative samples were selected. Four aspects were explored, including dashboard design style, interface layout, information framework, and hierarchical table. The color proportions and shape division of dashboards were analyzed both longitudinally and transversally. By studying and analyzing the five samples, we find to design digital car dashboards, designers have to obey the rules of simple layout, color precision and experience richness. This study has high practical significance and values.

In the healthcare sector the number of patients rises while the staffs cover is decreasing. Due to cost pressure hospital stays are shortened. Thereby more and more clinical activities are migrated into the home environment, especially if these activities have a nursing character. Examples for these activities are infusion therapies or the need of a respiratory device. Due to this trend and the increasing cost pressure in healthcare more and more patients are incorporated as agents in their own care. Thereby more and more clinical products are used by patients and their nursing relatives. This raises the question whether the used medical devices have a proper usability for the use in home healthcare. For such investigations we introduce the “Emergency Usability Lab”. Focus of this lab is to evaluate and ensure usability of a medical product for the homecare environment in critical situations and emergencies.

Mobility is a fast developing, technological and simultaneously human field of research. V2X-technology is one major contributor that will influence the behavior, efficiency and safety of traffic participants. To include all participating members of traffic, we developed a V2X-smartphone application to empower vulnerable road user to be part of the technological integration. With a two-tiered research approach, we focused on both, the iconography and the feedback design of that application. One key finding of the presented work is a clear recommendation of combined features (color, size and geometrical form) for rear-end collision scenarios. The article concludes with practical recommendations that facilitate visualization-varieties from a users’ perspective.

The idea of Reason’s model is applied to the establishment of civil aviation safety performance index system. The index types and the multiple lines of defense of the unsafe events are placed in one-to-one correspondence to set five dimensions of the index types, including safety result, operation quality, risk management, safety assurance and safety foundation. Taking into account the management complexity and operating characteristics of different flight fleets, the concept of management difficulty coefficient is introduced to improve efficacy coefficient method, and the safety performance evaluation model is established based on the improved efficacy coefficient method. The data of 5 flying fleets in an airline is used as an example of application to verify the feasibility and applicability of the evaluation model. The evaluation results show that the evaluation model can compare fleet safety performance from five dimensions quantitatively, as well as obtain the results of comprehensive evaluation of safety performance for each fleet.

Workers’ unsafe behaviors are a top cause of safety accidents in construction. In practice, the industry relies on training and education at the group level to correct or prevent unsafe behaviors of workers. However, evidence shows that some individuals were identified to be showing risky behavior repeatedly and have a high rate to be involved in accidents and current safety training approach at the group level may not be effective for those workers. A worker’s evaluation of a hazard (risk perception) and tendency to take/avoid risks (risk propensity) determines how they respond to a hazard and identifying those workers with biased risk perceptions and high risk propensity can thus provide an opportunity to prevent behavior-based injuries and fatalities in the workplace. However, as risk perception and propensity are influenced not only by inherited personal traits (e.g. locus of control) but also by specific situational factors (e.g. mood and stress level), existing approaches relying on surveys are not sufficient when measuring workers’ risk perception and propensity continuously in day-to-day operations. In this context, this study examines the potential of ambulatory and continuous gait monitoring in the workplace as a means of identifying workers’ risk perception and propensity. Two experiments simulating construction work environments were conducted and subjects’ gait patterns in hazard zones were assessed with inertial measurement unit (IMU) data. The experimental results demonstrate changes in gait patterns at pre-hazard zones for most of the subjects. However, the results fail to identify the relationship between gait pattern changes at pre-hazard zones and risk propensities assessed using the Accident Locus of Control Scale.

With the rapid increasing of car quantity, traffic safety problem has become more and more serious. Traffic crash is also a major world public health problem; hence, it has become a topic of interest among many scholars. This study intends to analyze the factors that affect driving safety under different driving scenarios. The results of this study are based on conditional autonomous driving. The context-aware conditional autonomous safety driving process and the relationships among three elements, namely, the environment, the driver and the car, are analyzed based on the Haddon matrix and the system attribution model. The vehicle used is a conditional autonomous car capable of context awareness; it can send useful information as feedback to the driver and interact with driver behavior. Afterward, the results are applied in parking scenario and lane changing scenario, and the factors that influence safety during parking and lane changing are analyzed. Then, the obtained information is validated based on driver perspective, and a design concept for head-up display is proposed. This design is expected to assist drivers in parking and lane changing without accident.

With the quick development of information technology, people pay more and more attention to information security and property safety, where identity is one of the most important aspects of information security. Compared with the traditional means of identification, biometrics recognition technology offers greater security and convenience. Among which, electrocardiogram (ECG) human identification has been attracted great attention in recent years. As a new type of biometric feature authentication technology, the feature selection and classification of ECG has become a focus of the research community. However, there exist some problems that can impair the efficiency and accuracy of ECG identification, including information redundancy and high dimensionality in feature extraction, and insufficient stability in classification. In order to solve the problems, in this paper, we propose a recognition method based on PCA-RPROP. In this method, firstly, only R points are located to get the original single-cycle waveforms. Then, PCA and whitening are used to process original data, where whitening is to make the input less redundant and PCA is to reduce its dimensionality. Finally, the resilient propagation (RPROP) algorithm is used to optimize the neural network and establish a complete recognition model. In order to evaluate the effectiveness of the algorithm, we compared the PCA feature with the wavelet decomposition and multi-point localization features in an ECG-ID database, and also compared RPROP with traditional BP algorithm, SVM and KNN. The experimental results show that this method can improve the performance compared with other classifiers, and simultaneously reduce the complexity of localization and the redundancy of features. It is superior to the other methods both speed and accuracy in recognition, especially when compared with the traditional BP. It can solve the problems of traditional BP with 2.4% higher recognition accuracy than LIBSVM, and 14 s faster than KNN in terms of time efficiency. Therefore, it is an efficient, simple and practical recognition algorithm.