Cognitive State Measurement from Eye Gaze Analysis in an Intelligent Virtual Reality Driving System for Autism Intervention

Abstract-Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities with a high prevalence rate. While much research has focused on improving social communication deficits in ASD populations, less emphasis has been devoted to improving skills relevant for adult independent living, such as driving. In this paper, a novel virtual reality (VR)-based driving system with different difficulty levels of tasks is presented to train and improve driving skills of teenagers with ASD. The goal of this paper is to measure the cognitive load experienced by an individual with ASD while he is driving in the VR-based driving system. Several eye gaze features are identified that varied with cognitive load in an experiment participated by 12 teenagers with ASD. Several machine learning methods were compared and the ability of these methods to accurately measure cognitive load was validated with respect to the subjective rating of a therapist. Results will be used to build models in an intelligent VR-based driving system that can sense a participant's real-time cognitive load and offer driving tasks at an appropriate difficulty level in order to maximize the participant's long-term performance

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy

Automatic emotion recognition in clinical scenario: a systematic review of methods

none4Automatic emotion recognition has powerful opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts, to deeply analyse clinical and technical aspects, how they were addressed, and relationships among them. The literature review was conducted on: IEEEXplore, ScienceDirect, Scopus, PubMed, ACM. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Moreover, the works were analysed according to a reference model to deeply examine both clinical and technical topics. 52 scientific papers passed inclusion criteria. Most clinical scenarios involved neurodevelopmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The most adopted signals are video and audio, while supervised shallow learning is mostly used for emotion recognition. A poor study design, tiny samples, and the absence of a control group emerged as methodological weaknesses. Heterogeneity of performance metrics, datasets and algorithms challenges results comparability, robustness, reliability and reproducibility.openPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria GabriellaPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria Gabriell

Visualization and Interaction Technologies in Serious and Exergames for Cognitive Assessment and Training: A Survey on Available Solutions and Their Validation

Exergames and serious games, based on standard personal computers, mobile devices and gaming consoles or on novel immersive Virtual and Augmented Reality techniques, have become popular in the last few years and are now applied in various research fields, among which cognitive assessment and training of heterogeneous target populations. Moreover, the adoption of Web based solutions together with the integration of Artificial Intelligence and Machine Learning algorithms could bring countless advantages, both for the patients and the clinical personnel, as allowing the early detection of some pathological conditions, improving the efficacy and adherence to rehabilitation processes, through the personalisation of training sessions, and optimizing the allocation of resources by the healthcare system. The current work proposes a systematic survey of existing solutions in the field of cognitive assessment and training. We evaluate the visualization and interaction technologies commonly adopted and the measures taken to fulfil the need of the pathological target populations. Moreover, we analyze how implemented solutions are validated, i.e. The chosen experimental designs, data collection and analysis. Finally, we consider the availability of the applications and raw data to the large community of researchers and medical professionals and the actual application of proposed solutions in the standard clinical practice. Despite the potential of these technologies, research is still at an early stage. Although the recent release of accessible immersive virtual reality headsets and the increasing interest on vision-based techniques for tracking body and hands movements, many studies still rely on non-immersive virtual reality (67.2%), mainly mobile and personal computers, and standard gaming tools for interactions (41.5%). Finally, we highlight that although the interest of research community in this field is increasingly higher, the sharing of dataset (10.6%) and implemented applications (3.8%) should be promoted and the number of healthcare structures which have successfully introduced the new technological approaches in the treatment of their host patients is limited (10.2%)

Ubiquitous Integration and Temporal Synchronisation (UbilTS) framework : a solution for building complex multimodal data capture and interactive systems

Contemporary Data Capture and Interactive Systems (DCIS) systems are tied in with various technical complexities such as multimodal data types, diverse hardware and software components, time synchronisation issues and distributed deployment configurations. Building these systems is inherently difficult and requires addressing of these complexities before the intended and purposeful functionalities can be attained. The technical issues are often common and similar among diverse applications. This thesis presents the Ubiquitous Integration and Temporal Synchronisation (UbiITS) framework, a generic solution to address the technical complexities in building DCISs. The proposed solution is an abstract software framework that can be extended and customised to any application requirements. UbiITS includes all fundamental software components, techniques, system level layer abstractions and reference architecture as a collection to enable the systematic construction of complex DCISs. This work details four case studies to showcase the versatility and extensibility of UbiITS framework’s functionalities and demonstrate how it was employed to successfully solve a range of technical requirements. In each case UbiITS operated as the core element of each application. Additionally, these case studies are novel systems by themselves in each of their domains. Longstanding technical issues such as flexibly integrating and interoperating multimodal tools, precise time synchronisation, etc., were resolved in each application by employing UbiITS. The framework enabled establishing a functional system infrastructure in these cases, essentially opening up new lines of research in each discipline where these research approaches would not have been possible without the infrastructure provided by the framework. The thesis further presents a sample implementation of the framework on a device firmware exhibiting its capability to be directly implemented on a hardware platform. Summary metrics are also produced to establish the complexity, reusability, extendibility, implementation and maintainability characteristics of the framework.Engineering and Physical Sciences Research Council (EPSRC) grants - EP/F02553X/1, 114433 and 11394

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

Driver’s performance under different secondary tasks and disruptions on rural road environment

Nowadays, detection of driver’s fatigue is a major concern in vehicle design, road safety and transportation research. Driving tasks requires full attention from the drivers while operating the vehicle. Occasionally, drivers are exposed to perform other activities such as talking to a passenger and using an in-vehicle technology or phone which is known as secondary task while driving. Thus, this study aims to analyse the driver’s performance via three types of physiological measurements in a simulated condition. An integrated approach by combining subjective and objective methods were used in this study. There are Karolinska Sleepiness Scale (KSS), Electroencephalogram (EEG) and Heart Rate (HR). Twelve participants were recruited to evaluate their responses towards different types of secondary tasks and disruptions in 25-minutes of driving duration. The findings showed that there are differences in physiological responses for this driving session. Beta Activity shows higher event-related power modulation values from start until the end of the driving session. In conclusion, the type of disruption during driving and secondary tasks shows different findings towards driver driving performances. This study can be used as reference to drivers and related agencies by taking into account the physiological effects of driver’s performance based on different secondary tasks and disruptions while driving