Inland waterway network mapping of AIS data for freight transportation planning

Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

Kinematics analysis and gait planning for a hemiplegic exoskeleton robot

Background: As the world's aging population increases, the number of hemiplegic patients is increasing year by year. At present, in many countries with low medical level, there are not enough rehabilitation specialists. Due to the different condition of patients, the current rehabilitation training system cannot be applied to all patients. so that patients with hemiplegia cannot get effective rehabilitation training. Methods: Through a motion capture experiment, the mechanical design of the hip joint, knee joint and ankle joint was rationally optimized based on the movement data. Through the kinematic analysis of each joint of the hemiplegic exoskeleton robot, the kinematic relationship of each joint mechanism was obtained, and the kinematics analysis of the exoskeleton robot was performed using the Denavit-Hartenberg (D-H) method. The kinematics simulation of the robot was carried out in automatic dynamic analysis of mechanical systems (ADAMS), and the theoretical calculation results were compared with the simulation results to verify the correctness of the kinematics relationship. According to the exoskeleton kinematics model, a mirror teaching method of gait planning was proposed, allowing the affected leg to imitate the movement of the healthy leg with the help of an exoskeleton robot. Conclusions: A new hemiplegic exoskeleton robot designed by Shenzhen Institute of Advanced Technology (SIAT-H) is proposed, which is lightweight, modular and anthropomorphic. The kinematics of the robot have been analyzed, and a mirror training gait is proposed to enable the patient to form a natural walking posture. Finally, the wearable walking experiment further proves the feasibility of the structure and gait planning of the hemiplegic exoskeleton robot.

Custom emoji based emotion recognition system for dynamic business webpages

PurposeOne of the contributions of artificial intelligent (AI) in modern technology is emotion recognition which is mostly based on facial expression and modification of its inference engine. The facial recognition scheme is mostly built to understand user expression in an online business webpage on a marketing site but has limited abilities to recognise elusive expressions. The basic emotions are expressed when interrelating and socialising with other personnel online. At most times, studying how to understand user expression is often a most tedious task, especially the subtle expressions. An emotion recognition system can be used to optimise and reduce complexity in understanding users' subconscious thoughts and reasoning through their pupil changes.Design/methodology/approachThis paper demonstrates the use of personal computer (PC) webcam to read in eye movement data that includes pupil changes as part of distinct user attributes. A custom eye movement algorithm (CEMA) is used to capture users' activity and record the data which is served as an input model to an inference engine (artificial neural network (ANN)) that helps to predict user emotional response conveyed as emoticons on the webpage.FindingsThe result from the error in performance shows that ANN is most adaptable to user behaviour prediction and can be used for the system's modification paradigm.Research limitations/implicationsOne of the drawbacks of the analytical tool is its inability in some cases to set some of the emoticons within the boundaries of the visual field, this is a limitation to be tackled within subsequent runs with standard techniques.Originality/valueThe originality of the proposed model is its ability to predict basic user emotional response based on changes in pupil size between average recorded baseline boundaries and convey the emoticons chronologically with the gaze points.

Adaptive Aging Safety of Guidance Marks in Rail Transit Connection Systems Based on Eye Movement Data

The aim of this study was to meet the visual cognition needs of the elderly population for the guidance marks and safety guidance marks of the rail transit connection system. Based on the visual characteristics of the elderly population, this paper firstly determined the visual field and sight range of the marks of the elderly population from three aspects—visual angle, visual distance, and height of the elderly population—and constructed the visual recognition space of the elderly population. Then, from the perspective of the setting position, the setting height, and the deflection angle, an adaptive aging safety design method for the guidance marks in the rail transit connection system is proposed. Then, based on the eye movement data of fixation duration, initial fixation duration, and the number of visits, a visual behavior index model is constructed to iteratively optimize the adaptive aging safety design of guidance marks in a rail transit connection system. A radar map is used to calculate the comprehensive index of visual behavior to determine the optimal scheme. Finally, taking the traffic connection system of Shanghai Songjiang University Town Station as an example, the eye movement data of 37 participants were collected, according to the principle that each connection path should only be taken once per person; the above method was used to design 7 connection path guidance marks for an adaptive aging safety design. The results showed that the comprehensive index of visual behavior of different paths had different degrees of improvement of up to 14.00%, which verified the effectiveness of the design method. The research results have certain theoretical significance and application value for the adaptive aging safety design and retrofit of guidance marks of rail transit connection systems.

Potsdam Eye-Movement Corpus for Scene Memorization and Search with Color and Spatial-Frequency Filtering

The availability of large eye-movement corpora has become increasingly important over the past years. In scene viewing, scan-path analyses of time-ordered fixations, for example, allow for investigating individual differences in spatial correlations between fixation locations, or for predicting individual viewing behavior in the context of computational models. However, time-dependent analyses require many fixations per scene, and only few large eye-movement corpora are publicly available. This manuscript presents a new corpus with eye-movement data from two hundred participants. Viewers memorized or searched either color or grayscale scenes while high or low spatial frequencies were filtered in central or peripheral vision. Our database provides the scenes from the experiment with corresponding object annotations, preprocessed eye-movement data, and heatmaps and fixation clusters based on empirical fixation locations. Besides time-dependent analyses, the corpus data allow for investigating questions that have received little attention in scene-viewing research so far: (i) eye-movement behavior under different task instructions, (ii) the importance of color and spatial frequencies when performing these tasks, and (iii) the individual roles and interaction of central and peripheral vision during scene viewing. Furthermore, the corpus allows for validation of computational models of attention and eye-movement control, and finally, analyses on an object- or cluster-based level.

Elevators as media objects manipulating information in time

We investigate elevators as media. Our central argument is that elevators manipulate information in time. Time manipulation of elevators (movement data + genetic algorithms) produces temporal order. Elevators have become media objects because they produce data that are digitally manipulated to optimize movement. We conducted an empirical study in a multinational corporation that manufactures elevators, including 4 months of field research at multiple locations and interviewed 64 people. We show how time manipulation changes with the information architecture: first, time manipulation took place inside and during the movement of elevators by pushing the buttons. Second, time manipulation took place in the cloud by statistical mathematics. The latest development is toward decentralized social application where elevators as independent media objects manipulate time using genetic algorithms and communicate with each other. We reveal how largely hidden media affects our temporality and argue that media theory should study its implications in contemporary society.

A Quantitative Framework for Identifying Patterns of Route-Use in Animal Movement Data

Animal movement along repeatedly used, “habitual” routes could emerge from a variety of cognitive mechanisms, as well as in response to a diverse set of environmental features. Because of the high conservation value of identifying wildlife movement corridors, there has been extensive work focusing on environmental factors that contribute to the emergence of habitual routes between protected habitats. In parallel, significant work has focused on disentangling the cognitive mechanisms underlying animal route use, as such movement patterns are of fundamental interest to the study of decision making and navigation. We reviewed the types of processes that can generate routine patterns of animal movement, suggested a new methodological workflow for classifying one of these patterns—high fidelity path reuse—in animal tracking data, and compared the prevalence of this pattern across four sympatric species of frugivorous mammals in Panama. We found the highest prevalence of route-use in kinkajous, the only nocturnal species in our study, and propose that further development of this method could help to distinguish the processes underlying the presence of specific routes in animal movement data.

The Fusion of Eye Movement and Piezoelectric Sensing Technology Assists Ceramic Art Process Optimization and Mechanical Characterization

Ceramic product shape evaluation is an important part of product development, an important method to optimize product shape design, and is of great significance to reasonably locate users’ consumption psychology and promote the development of ceramic product industry. In this paper, we propose an eye-tracking-based evaluation method for ceramic products from the user’s point of view, in view of the fact that there are few studies on ceramic product shape evaluation, and it is mainly led by designers and enterprise leaders subjectively, with low user participation and lack of objective evaluation means and objective data support. In this paper, through the implementation of eye-movement experiments, we obtain and analyze the eye-movement data related to the semantic perception evaluation of product modeling and the overall evaluation of modeling, establish the mapping relationship between user evaluation and eye-movement data, and provide objective data support for modeling evaluation. This paper provides an objective data support for the styling evaluation. This paper provides new ideas for the ceramic product modeling evaluation method, which helps to promote the development of ceramic product industry, improve the brand recognition of enterprises, and help the marketing personnel to make reasonable marketing planning plans. For the semantic perceptual evaluation of ceramic product styling based on eye-tracking, the effectiveness of product styling design concept communication is evaluated. Ceramic products are constantly changing and developing, with new shapes appearing and old shapes being eliminated. Continual innovation and development of ceramics based on inherited traditions can give them a new look and color under the existing modeling style. Compared with other categories, although ceramic modeling has relatively abstract formal characteristics, but it is the extension of the modeling, still has obvious morphological characteristics, and the impact on people’s aesthetic mood.

Context-Aware Human Activity Recognition in Industrial Processes

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.