Dimitrios Giakoumis

Automatic Recognition of Boredom in Video Games Using Novel Biosignal Moment-Based Features

This paper presents work conducted toward the biosignals-based automatic recognition of boredom, induced during video-game playing. For this purpose, common biosignal feature extraction methods were exploited and their capability to identify boredom was assessed. Moreover, for the first time, Legendre and Krawtchouk moments, as well as novel moment variations, were extracted as biosignal features and their potential toward automatic affect recognition was examined using the specific application scenario. The present analysis was conducted with ECG and GSR data collected from 19 different subjects, while boredom was naturally induced during the repetitive playing of a 3D video game. Conventional biosignal features as well as moment-based ones were found to be effective for the automatic recognition of boredom by achieving classification accuracies around 85 percent. Then, the joint use of moments and moment variations with conventional features was found to significantly improve classification accuracy by producing a maximum correct classification ratio of 94.17 percent.

Can a Virtual Reality Cognitive Training Application Fulfill a Dual Role? Using the Virtual Supermarket Cognitive Training Application as a Screening Tool for Mild Cognitive Impairment

BACKGROUND Recent research advocates the potential of virtual reality (VR) applications in assessing cognitive functions highlighting the possibility of using a VR application for mild cognitive impairment (MCI) screening. OBJECTIVE The aim of this study is to investigate whether a VR cognitive training application, the virtual supermarket (VSM), can be used as a screening tool for MCI. METHODS Two groups, one of healthy older adults (n = 21) and one of MCI patients (n = 34), were recruited from day centers for cognitive disorders and administered the VSM and a neuropsychological test battery. The performance of the two groups in the VSM was compared and correlated with performance in established neuropsychological tests. At the same time, the effectiveness of a combination of traditional neuropsychological tests and the VSM was examined. RESULTS VSM displayed a correct classification rate (CCR) of 87.30% when differentiating between MCI patients and healthy older adults, while it was unable to differentiate between MCI subtypes. At the same time, the VSM correlates with various established neuropsychological tests. A limited number of tests were able to improve the CCR of the VSM when combined with the VSM for screening purposes. DISCUSSION VSM appears to be a valid method of screening for MCI in an older adult population though it cannot be used for MCI subtype assessment. VSMs concurrent validity is supported by the large number of correlations between the VSM and established tests. It is considered a robust test on its own as the inclusion of other tests failed to improve its CCR significantly.

RAMCIP: Towards a Robotic Assistant to Support Elderly with Mild Cognitive Impairments at Home

During the last decades the mild cognitive impairments (MCI) as well as the early stage of dementia comprises a societal challenge in the growing elderly population. This fact is highly related to the physical and cognitive decline of aged people, influencing the way they apprehend their environment and, thus, their daily activities. Towards this direction, the “Robotic Assistant for MCI patients at home” (RAMCIP) project, initiated by the European Union, intends to build a service robot that will operate in domestic environments with the aim to proactively and discreetly support older persons and MCI patients. The key component to achieve this goal is the design of a robot endowed with high-level cognitive functions, driven by advanced human and environment perception mechanisms, that will enable the artificial agent to autonomously decide when and how to assist. The paper in hand demonstrates the RAMCIP concept through identified user requirements and provides an overall system description. Additionally, the architecture design of the robotic system is exhibited here, firstly by providing a conceptual analysis and then by further decomposing the identified modules into functional components. The overall architecture envisaged in a user centric manner aiming to convert the real needs of the MCI patients into capabilities of the robotic assistant.

A Preliminary Study on the Feasibility of Using a Virtual Reality Cognitive Training Application for Remote Detection of Mild Cognitive Impairment

BACKGROUND It has been demonstrated that virtual reality (VR) applications can be used for the detection of mild cognitive impairment (MCI). OBJECTIVE The aim of this study is to provide a preliminary investigation on whether a VR cognitive training application can be used to detect MCI in persons using the application at home without the help of an examiner. METHODS Two groups, one of healthy older adults (n = 6) and one of MCI patients (n = 6) were recruited from Thessaloniki day centers for cognitive disorders and provided with a tablet PC with custom software enabling the self-administration of the Virtual Super Market (VSM) cognitive training exercise. The average performance (from 20 administrations of the exercise) of the two groups was compared and was also correlated with performance in established neuropsychological tests. RESULTS Average performance in terms of duration to complete the given exercise differed significantly between healthy(μ  = 247.41 s/ sd = 89.006) and MCI (μ= 454.52 s/ sd = 177.604) groups, yielding a correct classification rate of 91.8% with a sensitivity and specificity of 94% and 89% respectively for MCI detection. Average performance also correlated significantly with performance in Functional Cognitive Assessment Scale (FUCAS), Test of Everyday Attention (TEA), and Rey Osterrieth Complex Figure test (ROCFT). DISCUSSION The VR application exhibited very high accuracy in detecting MCI while all participants were able to operate the tablet and application on their own. Diagnostic accuracy was improved compared to a previous study using data from only one administration of the exercise. The results of the present study suggest that remote MCI detection through VR applications can be feasible.

Enabling user interface developers to experience accessibility limitations through visual, hearing, physical and cognitive impairment simulation

His paper presents a tool facilitating developers of user interfaces (UIs) to experience accessibility limitations that can be posed from various disabilities during the interaction of impaired users with their developments. In this respect, various aspects of visual, hearing, physical and cognitive impairments have been modelled through filters providing approximate, yet, realistic simulations over them. These filters have formed the basis for the developed tool, which can be used either on its own (as a standalone application), or be embedded in the NetBeans Integrated Development Environment. The tool, named DIAS, allows for impairment simulations to be performed over Java, mobile and web applications. Moreover, it integrates two of the most common assistive technologies (ATs), namely a screen reader and a magnifier. As a result, developers of UIs can not only experience how interaction would be affected from various impairments, but they can also understand how their developments would be perceived by impaired users through an AT. This work aims to provide an integrated, practical solution for impairment simulation, which could be easily adopted by developers, thus realistically increasing the possibilities for the future development of interactive applications that are more accessible to users with disabilities.

Recognizing Daily Activities in Realistic Environments Through Depth-Based User Tracking and Hidden Conditional Random Fields for MCI/AD Support

This paper presents a novel framework for the automatic recognition of Activities of Daily Living (ADLs), such as cooking, eating, dishwashing and watching TV, based on depth video processing and Hidden Conditional Random Fields (HCRFs). Depth video is provided by low-cost RGB-D sensors unobtrusively installed in the house. The user’s location, posture, as well as point cloud -based features related to gestures are extracted; a standing/sitting posture detector, as well as novel features expressing head and hand gestures are introduced herein. To model the target activities, we employed discriminative HCRFs and compared them to HMMs. Through experimental evaluation, HCRFs outperformed HMMs in location trajectories-based ADL detection. By fusing trajectories data with posture and the proposed gesture features, ADL detection performance was found to further improve, leading to recognition rates at the level of 90.5 % for five target activities in a naturalistic home environment.

Robust Human Pose Tracking For Realistic Service Robot Applications

Robust human pose estimation and tracking plays an integral role in assistive service robot applications, as it provides information regarding the body pose and motion of the user in a scene. Even though current solutions provide high-accuracy results in controlled environments, they fail to successfully deal with problems encountered under real-life situations such as tracking initialization and failure, body part intersection, large object handling and partial-view body-part tracking. This paper presents a framework tailored for deployment under real-life situations addressing the above limitations. The framework is based on the articulated 3D-SDF data representation model, and has been extended with complementary mechanisms for addressing the above challenges. Extensive evaluation on public datasets demonstrates the frameworks state-of-the-art performance, while experimental results on a challenging realistic human motion dataset exhibit its robustness in real life scenarios.

A Living Lab Infrastructure for Investigating Activity Monitoring Needs in Service Robot Applications

This paper presents a framework that has been developed for automatic activity recognition and domestic behavior monitoring, towards supporting elderly MCI patients in their daily domestic life. Our frameworks infrastructure consists of a network of smart-home sensors and RGB-D cameras that can be adapted and be unobtrusively installed in a variety of indoor living areas, collecting data relative to the humans movement and the state of the home environment. User activities and behavior are then assessed through machine learning algorithms applied on these data. The developed framework has been applied in real house settings and extensive analysis has been performed, so as to investigate how human activity and behavior monitoring needs, in the scope of ICT solutions for supporting active and healthy ageing of MCI patients, can be covered in the scope of corresponding service robot applications.

Human Aware Robot Navigation in Semantically Annotated Domestic Environments

In the near future, the seamless human robot cohabitation can be achieved as long as the robots to be released in the market attain socially acceptable behavior. Therefore, robots need to learn and react appropriately, should they be able to share the same space with people and to adapt their operation to human’s activity. The goal of this work is to introduce a human aware global path planning solution for robot navigation that considers the humans presence in a domestic environment. Towards this direction, hierarchical semantic maps are built upon metric maps where the human presence is modelled using frequently visited standing positions considering also the proxemics theory. During the human’s perambulation within the domestic environment the most probable humans pathways are calculated and modeled with sequential, yet descending Gaussian kernel’s. This way, the robot reacts with safety when operating in a domestic environment taking into consideration the human presence and the physical obstacles. The method has been evaluated on a simulated environment, yet on realistic acquired data modeling a real house space and exhibited remarkable performance.

Introducing web service accessibility assessment techniques through a unified quality of service context

This paper introduces novel web service (WS) accessibility assessment techniques through a unified Quality of Services (QoS) context. The goal is to enable future QoS-aware service selection systems to select and provide accessible web services, ones that are properly designed so as to allow their consumption from end-user applications, used from people with disabilities. In this line, a WS accessibility assessment Framework (WSaaF) has been developed, on the basis of WS accessibility guidelines, dealing with accessibility issues that can appear both on the presentation level of content delivered through WSs and on the content level itself. The WSaaF and its guidelines follow the rationale behind W3C WCAG 2.0-based accessibility standardization of web content. It provides the basis toward building future accessible WSs, a task that can be further facilitated by the use of an appropriate Tool (WSaaT), developed with the aim to provide automatic assessment of services, against guidelines of the proposed framework. Then, the WS accessibility attribute is introduced, as a metric that can be used in conjunction to ones typically utilized so far, within QoS-aware service selection systems. As a result, a novel unified QoS framework is proposed, incorporating the notion of accessibility in the service selection process. The proposed unified QoS framework can eventually lead to the provision of services, which are selected from appropriate repositories and better suit the special needs of people with disabilities.