A simple technique for improving multi-class classification with neural networks

We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.Comment: European Symposium on artificial neural networks (ESANN), Jun 2015, Bruges, Belgiu

An MCDM approach to the selection of novel technologies for innovative in-vehicle information systems

Driving a car is a complex skill that includes interacting with multiple systems inside the vehicle. Today’s challenge in the automotive industry is to produce innovative In-Vehicle Information Systems (IVIS) that are pleasant to use and satisfy the costumers’ needs while, simultaneously, maintaining the delicate balance of primary task vs. secondary tasks while driving. The authors report a MCDM approach for rank ordering a large heterogeneous set of human-machine interaction technologies; the final set consisted of hundred and one candidates. They measured candidate technologies on eight qualitative criteria that were defined by domain experts, using a group decision-making approach. The main objective was ordering alternatives by their decision score, not the selection of one or a small set of them. The authors’ approach assisted decision makers in exploring the characteristics of the most promising technologies and they focused on analyzing the technologies in the top quartile, as measured by their MCDM model. Further, a clustering analysis of the top quartile revealed the presence of important criteria trade-offs.Operational Competitiveness Program – COMPETE, QREN (Quadro de Referência Estratégico Nacional), European Regional Development Funds (European Union), R&D project in joint-promotion (HMIEXCEL-2013-2015 36265/2013) HMIEXCEL - I&D crítica em torno do ciclo de desenvolvimento e produção de soluções multimédia avançadas para automóvelStrategic program FCT-UID/EEA/00066/2013Fundação para a Ciência e a Tecnologia (IF/00217/2013)Fundação para a Ciência e a Tecnologia (PD/BD/105966/2014

A simple technique for improving multi-class classification with neural networks

International audienceWe present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results , overall as well as for particular difficult cases, on the hard 10-class gesture classification task

A user experience‐based toolset for automotive human‐machine interface technology development

The development of new automotive Human-Machine Interface (HMI) technologies must consider the competing and often conflicting demands of commercial value, User Experience (UX) and safety. Technology innovation offers manufacturers the opportunity to gain commercial advantage in a competitive and crowded marketplace, leading to an increase in the features and functionality available to the driver. User response to technology influences the perception of the brand as a whole, so it is important that in-vehicle systems provide a high-quality user experience. However, introducing new technologies into the car can also increase accident risk. The demands of usability and UX must therefore be balanced against the requirement for driver safety. Adopting a technology-focused business strategy carries a degree of risk, as most innovations fail before they reach the market. Obtaining clear and relevant information on the UX and safety of new technologies early in their development can help to inform and support robust product development (PD) decision making, improving product outcomes. In order to achieve this, manufacturers need processes and tools to evaluate new technologies, providing customer-focused data to drive development. This work details the development of an Evaluation Toolset for automotive HMI technologies encompassing safety-related functional metrics and UX measures. The Toolset consists of four elements: an evaluation protocol, based on methods identified from the Human Factors, UX and Sensory Science literature; a fixed-base driving simulator providing a context-rich, configurable evaluation environment, supporting both hardware and software-based technologies; a standardised simulation scenario providing a repeatable basis for technology evaluations, allowing comparisons across multiple technologies and studies; and a technology scorecard that collates and presents evaluation data to support PD decision making processes

Ambient hues and audible cues: An approach to automotive user interface design using multi-modal feedback

The use of touchscreen interfaces for in-vehicle information, entertainment, and for the control of comfort settings is proliferating. Moreover, using these interfaces requires the same visual and manual resources needed for safe driving. Guided by much of the prevalent research in the areas of the human visual system, attention, and multimodal redundancy the Hues and Cues design paradigm was developed to make touchscreen automotive user interfaces more suitable to use while driving. This paradigm was applied to a prototype of an automotive user interface and evaluated with respects to driver performance using the dual-task, Lane Change Test (LCT). Each level of the design paradigm was evaluated in light of possible gender differences. The results of the repeated measures experiment suggests that when compared to interfaces without both the Hues and the Cues paradigm applied, the Hues and Cues interface requires less mental effort to operate, is more usable, and is more preferred. However, the results differ in the degradation in driver performance with interfaces that only have visual feedback resulting in better task times and significant gender differences in the driving task with interfaces that only have auditory feedback. Overall, the results reported show that the presentation of multimodal feedback can be useful in design automotive interfaces, but must be flexible enough to account for individual differences

人間中心型自動運転車における協調運転のための適応的マルチモーダルインタフェースに関する研究

早大学位記番号:新7967早稲田大

EMPOWERING THE FRONT-SEAT PASSENGER: DESIGN AND EXPERIENCE PROTOTYPING OF LUXURY INFOTAINMENT SYSTEMS THROUGH VR SIMULATION

Automotive user interfaces have been designed within the limitations of driving activity. Therefore, there has been a lack of infotainment solutions that target the front-seat passenger as another car occupant with his/her own needs, interests and capabilities. This research is built on the motivation of empowering the front-seat passengers in luxury car journeys through infotainment systems. It handles front-seat passenger’s empowerment through the investigation of how a pleasant and luxury infotainment experience is manifested via new functionalities and interactions. This research tackles the challenge of understanding how these unprecedented infotainment solutions will add to front-seat passenger’s travel experience with experience prototyping through VR simulation. It follows the ‘research through design approach’ by i) presenting a design proposal for the front-seat passenger infotainment system, ii) developing a VR simulation to communicate the infotainment system interactions within a travel scenario in an immersive way, and iii) conducting experience prototyping study where participants reflect on the design proposal (VR simulation) through administration of mixed data collection methods including semantic differential questionnaires and semi-structured interviews. To achieve the above-mentioned objectives, the research makes use of an extensive literature on User Experience (UX)-Human Computer Interaction (HCI), Automotive UX, Luxury Marketing, and Simulation. The synthesis of the UX and marketing literature enables deconstruction of pleasant and luxury user experience into a set of qualities/metrics to be referred in design and design evaluation. The synthesis of aesthetics of interaction studies in the UX literature helps to categorize the diverse aspects of the infotainment system. The analysis of the academic and industrial efforts to empower front-seat passengers through automotive user interfaces is used for identification of promising technologies and trends for the infotainment system. The literature review on experience prototyping with VR constitutes a reference in prototyping-related decisions and using VR as part of the user study. The thesis finally presents the analysis of the experience prototyping study through i) the quantitative representation and discussion of the diverse aspects of the infotainment system (functionalities and interaction aesthetics) that play role in delivery of the various qualities of luxury experience, ii) structured analysis of the participants’ suggestions for the system with specification of the underlying motivations and iii) development of a framework that conceptualizes the front-seat passengers’ changing role and relations with the infotainment system. Based on these investigations of the link between the infotainment system aspects and the participants’ expectations/concerns, the research concludes with key design considerations and recommendations for the future (luxury) front-seat passenger-oriented infotainment system solutions. It also presents recommendations for integration of VR simulation into future car HMI appraisals by reflecting on the experience prototyping study conducted as part of the PhD research

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations

Wearable technology and gesture recognition for live performance augmentation

The use of physical gestures within interactions between humans and computer systems is a rapidly progressing research field that find itself increasingly present in smartphone and computer applications. This dissertation intends to outline the various engineering design processes involved in the creation of a simplistic and novel gesture recognition system geared towards use in live entertainment performances. The system aims to help in increasing fluidity of human-machine interaction in the entertainment industry by providing an alternative input method for controlling other performance related systems such as mixers, monitors, digital audio workstations and stage lighting. Electronic methods of wearable body movement tracking, gesture recognition and wireless interfacing are explored in order to determine a suitable design for the system to achieve a practical result. The resulting system consists of a wearable hardware group as well as a terminal hardware group, with associated software for each. The wearable design contains an MPU6050 motion processing unit, Arduino Uno development board and a HopeRF HM-TR wireless data link transceiver. The terminal group is responsible for receiving MIDI commands and consists of an Arduino compatible ‘LeoStick’ board coupled with a second transceiver. The usage of modern additive manufacturing methods was also investigated for hardware enclosure creation to allow potential for rapid prototyping. The GR is able to accurately provide movement data to a processor, which utilises a running average based gesture recognition algorithm in an attempt to extract movement features and respond to the presence of a pre-determined gesture by generating a MIDI command that is then sent to a computer terminal for use with external applications. Although the system did not fully live up to design objectives, it plays the role of an important stepping stone in the creation of practical, entertainment-oriented gesture recognition devices that are more accessible to the general public