Multi-modal discrimination learning in humans: evidence for configural theory

Human contingency learning was used to compare the predictions of configural and elemental theories. In three experiments, participants were required to learn which indicators were associated with an increase in core temperature of a fictitious nuclear plant. Experiments 1 and 2 investigated the rate at which a triple-element stimulus (ABC) could be discriminated from either single-element stimuli (A, B, and C) or double-element stimuli (AB, BC, and AC). Experiment 1 used visual stimuli, whilst Experiment 2 used visual, auditory, and tactile stimuli. In both experiments the participants took longer to discriminate the triple-element stimulus from the more similar double-element stimuli than from the less similar single-element stimuli. Experiment 3 tested for summation with stimuli from either single or multiple modalities and summation was found only in the latter. Thus the pattern of results seen in Experiments 1 and 2 was not dependent on whether the stimuli were single- or multi-modal nor was it dependent on whether the stimuli could elicit summation. This pattern of results is consistent with the predictions of Pearce’s (1987) configural theory

Mid-Air Haptics for Control Interfaces

Control interfaces and interactions based on touch-less gesture tracking devices have become a prevalent research topic in both industry and academia. Touch-less devices offer a unique interaction immediateness that makes them ideal for applications where direct contact with a physical controller is not desirable. On the other hand, these controllers inherently lack active or passive haptic feedback to inform users about the results of their interaction. Mid-air haptic interfaces, such as those using focused ultrasound waves, can close the feedback loop and provide new tools for the design of touch-less, un-instrumented control interactions. The goal of this workshop is to bring together the growing mid-air haptic research community to identify and discuss future challenges in control interfaces and their application in AR/VR, automotive, music, robotics and teleoperation

Connecting Look and Feel: Associating the visual and tactile properties of physical materials

For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs

Multi-Modal Trip Hazard Affordance Detection On Construction Sites

Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type; for example wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colour and depth fusion approaches, including 4 fusion and one non fusion approach; using colour and two types of depth images. Trained and tested on over 600 labelled trip hazards over 4 floors and 2000m$\mathrm{^{2}}$ in an active construction site,this approach was able to differentiate between identical objects in different physical configurations (see Figure 1). Outperforming a colour-only detector, our multi-modal trip detector fuses colour and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset moves us one step closer to assistive or fully automated safety inspection systems on construction sites.Comment: 9 Pages, 12 Figures, 2 Tables, Accepted to Robotics and Automation Letters (RA-L

Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

GazeTouchPass: Multimodal Authentication Using Gaze and Touch on Mobile Devices

We propose a multimodal scheme, GazeTouchPass, that combines gaze and touch for shoulder-surfing resistant user authentication on mobile devices. GazeTouchPass allows passwords with multiple switches between input modalities during authentication. This requires attackers to simultaneously observe the device screen and the user's eyes to find the password. We evaluate the security and usability of GazeTouchPass in two user studies. Our findings show that GazeTouchPass is usable and significantly more secure than single-modal authentication against basic and even advanced shoulder-surfing attacks

Tactons: structured tactile messages for non-visual information display

Tactile displays are now becoming available in a form that can be easily used in a user interface. This paper describes a new form of tactile output. Tactons, or tactile icons, are structured, abstract messages that can be used to communicate messages non-visually. A range of different parameters can be used for Tacton construction including: frequency, amplitude and duration of a tactile pulse, plus other parameters such as rhythm and location. Tactons have the potential to improve interaction in a range of different areas, particularly where the visual display is overloaded, limited in size or not available, such as interfaces for blind people or in mobile and wearable devices. This paper describes Tactons, the parameters used to construct them and some possible ways to design them. Examples of where Tactons might prove useful in user interfaces are given

Mixed reality participants in smart meeting rooms and smart home enviroments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments