Recognition of Visual Memory Recall Processes Using Eye Movement Analysis

Physical activity, location, as well as a person\textquoterights psychophysiological and affective state are common dimensions for developing context-aware systems in ubiquitous computing. An important yet missing contextual dimension is the cognitive context that comprises all aspects related to mental information processing, such as perception, memory, knowledge, or learning. In this work we investigate the feasibility of recognising visual memory recall. We use a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the methodology in a dual user study with a total of fourteen participants looking at familiar and unfamiliar pictures from four picture categories: abstract, landscapes, faces, and buildings. Using person-independent training, we are able to discriminate between familiar and unfamiliar abstract pictures with a top recognition rate of 84.3% (89.3% recall, 21.0% false positive rate) over all participants. We show that eye movement analysis is a promising approach to infer the cognitive context of a person and discuss the key challenges for the real-world implementation of eye-based cognition-aware systems

Signal Processing Technologies for Activity-aware Smart Textiles

Garments made of smart textiles have an enormous potential for embedding sensors in close proximity to the body in an unobtrusive and comfortable manner. Combined with signal processing and pattern recognition technologies, complex high-level information about human behaviors or situations can be inferred from the sensor data. The goal of this chapter is to introduce the reader to the design of activity-aware systems that use body-worn sensors, such as those that can be made available through smart textiles. We start this chapter by emphasizing recent trends towards ‘}wearable{’ sensing and computing and we present several examples of activity-aware applications. Then we outline the role that smart textiles can play in activity-aware applications, but also the challenges that they pose. We conclude by discussing the design process followed to devise activity-aware systems: the choice of sensors, the available data processing methods, and the evaluation techniques. We discuss recent data processing methods that address the challenges resulting from the use of smart textiles

Wearable {EOG} Goggles: {S}eamless Sensing and Context-awareness in Everyday Environments

In this article we introduce the analysis of eye motion as a new input modality for activity recognition, context-awareness and mobile HCI applications. We describe a novel embedded eye tracker that, in contrast to common systems using video cameras, relies on Electrooculography (EOG). This self-contained wearable device consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We show how challenges associated with wearability, eye motion analysis and signal artefacts caused by physical activity can be addressed with a combination of a special mechanical design, optimised algorithms for eye movement detection and adaptive signal processing. In two case studies, we demonstrate that EOG is a suitable measurement technique for the recognition of reading activity and eye-based human-computer interaction. Eventually, wearable EOG goggles may pave the way for seamless eye movement analysis and new forms of context-awareness not possible today

Wearable {EOG} Goggles: Eye-based Interaction in Everyday Environments

In this paper, we present an embedded eye tracker for context‐awareness\u000A and eye‐based human‐computer interaction â̑exteuro}{“\u000A the wearable EOG goggles. In contrast to common systems using video,\u000A this unobtrusive device relies on Electrooculography (EOG). It consists\u000A of goggles with dry electrodes integrated into the frame and a small\u000A pocket‐worn component with a powerful microcontroller for EOG signal\u000A processing. Using this lightweight system, sequences of eye movements,\u000A so‐called eye gestures, can be efficiently recognised from EOG signals\u000A in real‐time for HCI purposes. The device is self‐contained solution\u000A and allows for seamless eye motion sensing, context‐recognition and\u000A eye‐based interaction in everyday environments

What's in the eyes for context-awareness?

Eye movements are a rich source of information about a person's context. Analyzing the link between eye movements and cognition might even allow us to develop cognition-aware pervasive computing systems that assess a person's cognitive context