Mid-air haptic rendering of 2D geometric shapes with a dynamic tactile pointer

An important challenge that affects ultrasonic midair haptics, in contrast to physical touch, is that we lose certain exploratory procedures such as contour following. This makes the task of perceiving geometric properties and shape identification more difficult. Meanwhile, the growing interest in mid-air haptics and their application to various new areas requires an improved understanding of how we perceive specific haptic stimuli, such as icons and control dials in mid-air. We address this challenge by investigating static and dynamic methods of displaying 2D geometric shapes in mid-air. We display a circle, a square, and a triangle, in either a static or dynamic condition, using ultrasonic mid-air haptics. In the static condition, the shapes are presented as a full outline in mid-air, while in the dynamic condition, a tactile pointer is moved around the perimeter of the shapes. We measure participants’ accuracy and confidence of identifying shapes in two controlled experiments (n1 = 34, n2 = 25). Results reveal that in the dynamic condition people recognise shapes significantly more accurately, and with higher confidence. We also find that representing polygons as a set of individually drawn haptic strokes, with a short pause at the corners, drastically enhances shape recognition accuracy. Our research supports the design of mid-air haptic user interfaces in application scenarios such as in-car interactions or assistive technology in education

Hybrid Systems of Soft Computing Technologies in Designing Team Decision for Supply Chain Management Systems of Organizations

Abstract—The main objective is development of hybrid systems for adaptive designing and supply chain management / strategizing of team decision “packages” for design work based on the use of soft computing technologies and system-creative thinking (SCT). An algorithm is proposed, and the results of case studies on predicting the effectiveness and optimal organization of team thinking, as well as designing team solutions using the technical package of social technologies are presented. They are exemplified by developing a system of products and marketing channels (points of contact) of an employer brand (EB) of an organization for individual stakeholder groups. An algorithm has been developed for using a system of hybrid “soft computing” technologies and system-creative thinking in supply chain process of project teamwork; practical calculations have been carried out using this algorithm. The algorithm and systems of models for using “soft computing” for supply chain developed allow us to obtain a synergistic effect from controlling a system of hybrid technologies at various stages of teamwork. The package includes a “basic” technology comprising “training teams”, and also the formation of a KPI system that characterize team work (units 1 and 2), “product” technologies comprising analysis of team organization thinking, forecasting team performance, team productivity management, as well as supply chain management of project (units 4, 5, 6), and also “closing” technology being a strategizing (adaptive management) of team work (dynamic control of the algorithm as a whole)

A feasibility study on pairing a smartwatch and a mobile device through multi-modal gestures

Pairing is the process of establishing an association between two personal devices. Although such a process is intuitively very simple, achieving a straightforward and secure association is challenging due to several possible attacks and usability-related issues. Indeed, malicious attackers might want to spoof the communication between devices in order to gather sensitive information or harm them. Moreover, offering users simple and usable schemes which attain a high level of security remains a major issue. In addition, due to the great diversity of pairing scenarios and equipment, achieving a single, usable, secure association for all possible devices and use cases is simply not possible. In this thesis, we study the feasibility of a novel pairing scheme based on multi-modal gestures, namely, gestures involving drawing supported by accelerometer data. In particular, a user can pair a smart-watch on his wrist and a mobile device (e.g., a smart-phone) by simply drawing with a finger on the screen at the device. To this purpose, we developed mobile applications for smart-watch and smart-phone to sample and process sensed data in support of a secure commitment-based protocol. Furthermore, we performed experiments to verify whether encoded matching-movements have a clear similarity compared to non-matching movements. The results proved that it is feasible to implement such a scheme which also offers users a natural way to perform secure pairing. This innovative scheme may be adopted by a large number of mobile devices (e.g., smart-watches, smart-phones, tablets, etc.) in different scenarios

Research on Application of Cognitive-Driven Human-Computer Interaction

Human-computer interaction is an important research content of intelligent manufacturing human factor engineering. Natural human-computer interaction conforms to the cognition of users' habits and can efficiently process inaccurate information interaction, thus improving user experience and reducing cognitive load. Through the analysis of the information interaction process, user interaction experience cognition and human-computer interaction principles in the human-computer interaction system, a cognitive-driven human-computer interaction information transmission model is established. Investigate the main interaction modes in the current human-computer interaction system, and discuss its application status, technical requirements and problems. This paper discusses the analysis and evaluation methods of interaction modes in human-computer system from three levels of subjective evaluation, physiological measurement and mathematical method evaluation, so as to promote the understanding of inaccurate information to achieve the effect of interaction self-adaptation and guide the design and optimization of human-computer interaction system. According to the development status of human-computer interaction in intelligent environment, the research hotspots, problems and development trends of human-computer interaction are put forward

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available