Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft

Keeping Calm in the Digital Age: Theorizing on a Self-Monitoring System of Technostress

People spend increasing time interacting with information technologies (IT) due to teleworking, which has become an important cause of psychological stress. Meanwhile, technological advances enable the monitoring of stress via methods that capture individuals’ physiological states like automatic facial expression analysis (AFEA). This research-in-progress article proposes a novel theory that aims at explaining and predicting the impact of AFEA of stress self-monitoring systems on users’ psychological stress. The theory proposes that AFEA of stress self-monitoring systems can increase facial expression self-awareness, and consequently inhibit users’ facial expressions of stress, which can in turn decrease users’ psychological stress. The theory has implications for the design science, affective computing, and technostress domains. It is hoped that the theory will generate discussions on the potential of stress self-monitoring systems in the workplace, education, and society

Benchmarking of Tools for User Experience Analysis in Industry 4.0

Abstract Industry 4.0 paradigm is based on systems communication and cooperation with each other and with humans in real time to improve process performances in terms of productivity, security, energy efficiency, and cost. Although industrial processes are more and more automated, human performance is still the main responsible for product quality and factory productivity. In this context, understanding how workers interact with production systems and how they experience the factory environment is fundamental to properly model the human interaction and optimize the processes. This research investigates the available technologies to monitor the user experience (UX) and defines a set of tools to be applied in the Industry 4.0 scenario to assure the workers' wellbeing, safety and satisfaction and improve the overall factory performance

Detection of psychological stress using a hyperspectral imaging technique

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference p\< 0.005 and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

Methods for acquisition and integration of personal wellness parameters

Wellness indicates the state or condition of being in good physical and mental health. Stress is a common state of emotional strain that plays a crucial role in the everyday quality of life. Nowadays, there is a growing individual awareness of the importance of a proper lifestyle and a generalized trend to become an active part in monitoring, preserving, and improving personal wellness for both physical and emotional aspects. The majority studies in this field relies on the evaluation of the changes of sensed parameters passing from rest to “maximal” stress. However, the vast majority of people usually experiences stressing circumstances in everyday life. This led us to investigate the impact of mild cognitive activation which can be somehow comparable to usual situations that everyone can face in daily life. Several signals and data can be useful to characterize the state of a person, but not all of them are equally important. So it is crucial to analyse the mutual relevance of the different pieces of information. In this work we focus on a subset of well-established psychophysical descriptors and we identified a set of devices enabling the measurement of these parameters . The design of the experimental setup and the selection of sensing devices were driven by qualitative criteria such as intrusiveness, reliability, and ease of use. These are deemed crucial for implementing effective (self-)monitoring strategies. A reference dataset, named “Mild Cognitive Activation” (MCA), was collected. The last aim of the project was the definition of a quantitative model for data integration providing a concise description of the wellness status of a person. This process was based on unsupervised learning paradigms. Data from MCA were integrated with data from the “Stress Recognition in Automobile Drivers” dataset . This allowed a cross validation of the integration methodology

Discovering activity patterns in office environment using a network of low-resolution visual sensors

Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events

Monitoring Physiological Reactions of Construction Workers in Virtual Environment: A Feasibility Study Using Affective Sensing Technology

This research aims to monitor workers’ physiological reactions in virtual construction scenario. With the objective of leveraging affective sensing technology in construction scenario, experiments with Galvanic Skin Response (GSR) was conducted in a 3D simulation developed based on a real construction site. The GSR results obtained from sensor were analyzed in order (i) to assess the feasibility of using virtual environment to generate real emotions, (ii) to examine the relation between questionnaires used to ask people about their experience and their physiological responses and (iii) to identify the factors that affect people’s emotional reactions in virtual environment. Subjects of the experimental group exhibited incoherent responses, as expected in experiments with human subjects. Based on the various reasons for this incoherence obtained from questionnaire part of the experiment, the potential in research for developing training methods with respect to workers’ physiological response capability was identified