Timely detection of behavioural and psychological symptoms of dementia is important for the prevention and reduction of distress for people with dementia and their loved ones. Wearable computing-based systems can be used to predict such difficulties in a timely manner, but a data collection experiment is needed to collect data to develop such a system. Caregivers can be vital assets in such experiments, however, often face high burden and stress due to their caring obligations. An even greater burden has been experienced by many due to the ongoing COVID-19 pandemic. In this paper, the roles that caregivers played in physiological data collections are reviewed. Three main roles were identified as being performed by caregivers in such data collection experiments: observation of difficulties; consenting for participants and themselves; device set-up and maintenance. Roles such as aiding in recruitment and providing information to participants before and during the study were also performed. Each of these roles can present their own burdens, which can be mitigated in a number of ways. Overall, it is vital that researchers consider the burden that may be placed on the caregiver who isfulfilling any of these roles in an experiment with sufficient mitigations to those burdens being implemented. Furthermore, we propose a pivot in our research towards analysing stress during the pandemic, justifying how this will help towards developing a system to detect dementia-related difficulties.

Mild Cognitive Impairment (MCI) is a definition of the diagnosis of early memory loss and disorientation. This study aims to identify people's symptoms through technology. However, machine learning (ML) can classify Cognitive Normal (CN) and Mild Cognitive Impairment (MCI) and Early Mild Cognitive Impairment (EMCI) using standard assessments from the Alzheimer's Disease Neuroimaging Initiative (ADNI); Montreal Cognitive (MoCA), Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ). Consequently, a Multilayer Perceptron (MLP) model was assembled into tables; MCI vs CN, MCI vs EMCI, and CN vs MCI. Additionally, an MLP model was developed for CN vs MCI vs EMCI. As a result, of advanced model performance, a cascade 3-path categorisation approach was created. Similarly, the exploitation of meta-analysis indicated a combination of MLP models (MCI vs CN, MCI vs EMCI, and CN vs MCI) with an overall accuracy within an acceptable limit. In addition, better results were found when assessments were combined rather than individually. Furthermore, applying class weights and probability thresholds could improve the MLP framework by performance achieving a balanced specificity and sensitivity ratio. Altering class weights and probability thresholds when training the MLP neuro network model, the sensitivity and Accuracy could be progressed further. In conclusion, ML, VR and electrodermal activity are constrained. Introducing the possibility of activity-based applications to enhance innovative solutions for cognitive impairment diagnosis and treatment.

We present a systematic review of wearable devices used for predicting or identifying dementia-related agitation. In the review, six named and described devices were found in the literature and each was evaluated and reviewed to identify their strengths and weaknesses, with all possessing at least one weakness which made it not ideal for future research on the prediction or detection of dementia-related agitation. Only two of the wearables contained all three of the desired sensing modalities, with one of those devices being prohibitively expensive for use in many applications and studies and the other being untested and unevaluated as well as not being available commercially or for use. Future work should focus on the development of a device which contains the three desired sensing modalities while remaining inexpensive and usable enough to be accessible for use in a wide variety of studies and applications.

Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

Data science is the process of liberating meaning from raw data using scientific methods and algorithms, and is becoming much more commonly used in healthcare with the emergence of personalised healthcare. Alzheimer's disease (AD) is a neurodegenerative disease that has no proven curative treatment, however a new treatment protocol, ReCODE, has been proposed to slow and reverse the progression of the disease. In this paper, an overview of AD is provided, followed by a description of the ReCODE protocol, including the new proposed methods and data to be used in prediction diagnosis and treatment. The ways in which data science can help with prediction and diagnosis are then reviewed, along with the data science techniques that can help with each treatment in the protocol. It is concluded that current data science techniques are useful in aiding the successful treatment of AD patients with he ReCODE protocol, and though there is much promise to the use of data science techniques to predict and diagnose AD, no such technique yet exists that can process all the necessary data. Future research should be conducted to develop such a data science technique. Further research should also be conducted to improve current data science techniques used to support the treatment of AD.

In this paper, we report on our findings from the first evaluation of MOT 2.0, an Adaptive Web 2.0 e-learning tool, which supports: 1) collaborative authoring (i.e. editing content of other users, describing content using tags, rating, commenting on the content, etc); 2) authoring for collaboration (i.e., adding author activities, such as defining groups of authors, subscribing to other authors, communication between authors, etc); 3) group-based adaptive authoring via group-based privileges; 4) social annotation i.e., tagging, rating, and feedback on the content via group-based privileges; 5) adaptive authoring, by recommending related content and/or other authors; adaptive delivery based on users' activities. Our main contributions are: 1) defining a new social layer in LAOS, a five-layer model for generic adaptive hypermedia authoring; 2) removing the barrier between tutors, learners and authors, which all become authors, with different sets of privileges; 3) adding the power of group-based authoring to the course creating.

E-learning 2.0 is a term refers to the second generation of e-learning, which uses the technologies of the Social Web, such as collaborative authoring and social annotation, in order to enhance e-learning environments. In this paper, we report the utilization of Social Web techniques for e-learning at the University of Warwick, UK, in general; then we present MOT 2.0, an E-learning 2.0 adaptive authoring and delivery system, followed by a case study that examines the usefulness of E-learning 2.0. Our main contributions are: 1) adding a new Social Layer for adaptive hypermedia and e-learning systems; 2) allowing students to contribute in the authoring process of e-learning, with different privileges; 3) adding collaborative authoring and social annotation to e-learning environments.