Classification Method for Thai Elderly People Based on Controllability of Sugar Consumption

Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe diseases for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors for CVDs should be addressed. Obesity, as one of the risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for the elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that the artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors

Acute mental stress assessment via short term HRV analysis in healthy adults : a systematic review with meta-analysis

Mental stress reduces performances, on the work place and in daily life, and is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. This study systematically reviewed existing literature investigating, in healthy subjects, the associations between acute mental stress and short term Heart Rate Variability (HRV) measures in time, frequency and non-linear domain. The goal of this study was to provide reliable information about the trends and the pivot values of HRV measures during mental stress. A systematic review and meta-analysis of the evidence was conducted, performing an exhaustive research of electronic repositories and linear researching references of papers responding to the inclusion criteria. After removing duplicates and not pertinent papers, journal papers describing well-designed studies that analyzed rigorously HRV were included if analyzed the same population of healthy subjects at rest and during mental stress. 12 papers were shortlisted, enrolling overall 758 volunteers and investigating 22 different HRV measures, 9 of which reported by at least 2 studies and therefore meta-analyzed in this review. Four measures in time and non-linear domains, associated with a normal degree of HRV variations resulted significantly depressed during stress. The power of HRV fluctuations at high frequencies was significantly depressed during stress, while the ratio between low and high frequency resulted significantly increased, suggesting a sympathetic activation and a parasympathetic withdrawal during acute mental stress. Finally, among the 15 non-linear measures extracted, only 2 were reported by at least 2 studies, therefore pooled, and only one resulted significantly depressed, suggesting a reduced chaotic behaviour during mental stress. HRV resulted significantly depressed during mental stress, showing a reduced variability and less chaotic behaviour. The pooled frequency domain measures demonstrated a significant autonomic balance shift during acute mental stress towards the sympathetic activation and the parasympathetic withdrawal. Pivot values for the pooled mean differences of HRV measures are provided. Further studies investigating HRV non-linear measures during mental stress are still required. However, the method proposed to transform and then meta-analyze the HRV measures can be applied to other fields where HRV proved to be clinically significant

Analysing the effects of working period on psychophysiological states of seafarers

Background: Human factor has become a critical issue due to the fact that all operational processes inmaritime transportation have been accelerated with technological innovations. Irregular and long workinghours, rapid change of working environments and other organisational and/or individual factors have negativeimpact on the performance of the seafarers as well as other transportation workers and/or shift workers.Seafarers working under the influence of these stressor factors, naturally, are affected from psychophysiologicaland cognitive aspects, which in return decrease their overall performances. The study mainly aimed toinvestigate which of the above mentioned affecting factors had greater impact on seafarers’ performances. Materials and methods: For this purpose, data for 14 healthy male seafarers on board different vessels wascollected. The collected data included the number of port visits, frequency of change of circadian rhythm, totalworking hours and watchkeeping periods in the recent month. In order to analyse the psychophysiologicaleffect of working period, electrodermal activity and heart rate variability measurements for arousal moodand data from 2-choice reaction time tests for cognitive performance were collected from the seafarers.Results: Obtained data was analysed in SPSS software programme applying multiple regression with factoranalysis to model the dependence between psychophysiological states of seafarers and working periods. Conclusions: According to the results, it was determined that the change of circadian rhythm is the maindeterminant factor for seafarers. The results showed that when seafarer’s circadian rhythm is changedmore frequently, they experience more drowsiness

Machine learning algorithms development for sleep cycles detection and general physical activity based on biosignals

In this work, machine learning algorithms for automatic sleep cycles detection were developed. The features were selected based on the AASM manual, which is considered the gold standard for human technicians. These include features such as saturation of peripheral oxygen or others related to heart rate variation. As normally, the sleep phases naturally differ in frequency, to balance the classes within the dataset, we either oversampled the least common sleep stages or undersampled the most common, allowing for a less skewed performance favouring the most represented stages, while simultaneously improving worst-stage classification. For training the models we used MESA, a database containing 2056 full overnight unattended polysomnographies from a group of 2237 participants. With the goal of developing an algorithm that would only require a PPG device to be able to accurately predict sleep stages and quality, the main channels used from this dataset were SpO2 and PPG. Employing several popular Python libraries used for the development of machine learning and deep learning algorithms, we exhaustively explored the optimisation of the manifold parameters and hyperparameters conditioning both the training and architecture of these models in order for them to better fit our purposes. As a result of these strategies, we were able to develop a neural network model (Multilayer perceptron) with 80.50% accuracy, 0.7586 Cohen’s kappa, and 77.38% F1- score, for five sleep stages. The performance of our algorithm does not seem to be correlated with sleep quality or the number of transitional epochs in each recording, suggesting uniform performance regardless of the presence of sleep disorders. To test its performance in a different real-world scenario we compared the classifications attributed by a popular sleep stage classification android app, which collected information using a smartwatch, and our algorithm, using signals obtained from a device developed by PLUX. These algorithms displayed a strong level of agreement (90.96% agreement, 0.8663 Cohen’s kappa).Neste trabalho, foram desenvolvidos algoritmos de aprendizagem de máquinas para a detecção automática de ciclos de sono. Os sinais específicos captados durante a extração de características foram selecionados com base no manual AASM, que é considerado o padrão-ouro para técnicos. Estas incluem características como a saturação do oxigénio periférico ou outras relacionadas com a variação do ritmo cardíaco. A fim de equilibrar a frequência das classes dentro do conjunto de dados, ora se fez a sobreamostragem das fases menos comuns do sono, ora se fez a subamostragem das mais comuns, permitindo um desempenho menos enviesado em favor das fases mais representadas e, simultaneamente, melhorando a classificação das fases com pior desempenho. Para o treino dos modelos criados, utilizámos MESA, uma base de dados contendo 2056 polissonografias completas, feitas durante a noite e sem vigilância, de um grupo de 2237 participantes. Do conjunto de dados escolhido, os principais canais utilizados foram SpO2 e PPG, com o objetivo de desenvolver um algoritmo que apenas exigiria um dispositivo PPG para poder prever com precisão as fases e a qualidade do sono. Utilizando várias bibliotecas populares de Python para o desenvolvimento de algoritmos de aprendizagem de máquinas e de aprendizagem profunda, explorámos exaustivamente a optimização dos múltiplos parâmetros e hiperparâmetros que tanto condicionam a formação como a arquitetura destes modelos, de modo a que se ajustem melhor aos nossos propósitos. Como resultado disto, fomos capazes de desenvolver um modelo de rede neural (Multilayer perceptron) com 80.50% de precisão, 0.7586 kappa de Cohen e F1-score de 77.38%, para cinco fases de sono. O desempenho do nosso algoritmo não parece estar correlacionado com a qualidade do sono ou o número de épocas de transição em cada gravação, sugerindo um desempenho uniforme independentemente da presença de distúrbios do sono. Para testar o seu desempenho num cenário de mundo real diferente, comparámos as classificações atribuídas por uma aplicação Android de classificação de fases do sono popular, através da recolha de informação por um smartwatch, e o nosso algoritmo, utilizando sinais obtidos a partir de um dispositivo desenvolvido pela PLUX. Estes algoritmos demonstraram um forte nível de concordância (90.96% de concordância, 0.8663 kappa de Cohen)

Integration of body sensor networks and vehicular ad-hoc networks for traffic safety

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.Peer ReviewedPostprint (author's final draft

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios

Human Resource Management in Emergency Situations

The dissertation examines the issues related to the human resource management in emergency situations and introduces the measures helping to solve these issues. The prime aim is to analyse complexly a human resource management, built environment resilience management life cycle and its stages for the purpose of creating an effective Human Resource Management in Emergency Situations Model and Intelligent System. This would help in accelerating resilience in every stage, managing personal stress and reducing disaster-related losses. The dissertation consists of an Introduction, three Chapters, the Conclusions, References, List of Author’s Publications and nine Appendices. The introduction discusses the research problem and the research relevance, outlines the research object, states the research aim and objectives, overviews the research methodology and the original contribution of the research, presents the practical value of the research results, and lists the defended propositions. The introduction concludes with an overview of the author’s publications and conference presentations on the topic of this dissertation. Chapter 1 introduces best practice in the field of disaster and resilience management in the built environment. It also analyses disaster and resilience management life cycle ant its stages, reviews different intelligent decision support systems, and investigates researches on application of physiological parameters and their dependence on stress. The chapter ends with conclusions and the explicit objectives of the dissertation. Chapter 2 of the dissertation introduces the conceptual model of human resource management in emergency situations. To implement multiple criteria analysis of the research object the methods of multiple criteria analysis and mahematics are proposed. They should be integrated with intelligent technologies. In Chapter 3 the model developed by the author and the methods of multiple criteria analysis are adopted by developing the Intelligent Decision Support System for a Human Resource Management in Emergency Situations consisting of four subsystems: Physiological Advisory Subsystem to Analyse a User’s Post-Disaster Stress Management; Text Analytics Subsystem; Recommender Thermometer for Measuring the Preparedness for Resilience and Subsystem of Integrated Virtual and Intelligent Technologies. The main statements of the thesis were published in eleven scientific articles: two in journals listed in the Thomson Reuters ISI Web of Science, one in a peer-reviewed scientific journal, four in peer-reviewed conference proceedings referenced in the Thomson Reuters ISI database, and three in peer-reviewed conference proceedings in Lithuania. Five presentations were given on the topic of the dissertation at conferences in Lithuania and other countries

Developing Driving Behaviour Models Incorporating the Effects of Stress

Driving is a complex task and several factors influence drivers’ decisions and performance including traffic conditions, attributes of vehicles, network and environmental characteristics, and last but not least characteristics of the drivers themselves. in an effort to better explain and represent driving behaviour, several driving behaviour models have been suggested over the years. In the existing literature, there are two main streams of driving behaviour models that can be found. The first is approaching driving behaviour from a human factors and cognitive perspective while the second is engineering-based. Driving behaviour models of the latter category are mathematical representations of drivers’ behaviour at the individual level, mostly focussing on acceleration/deceleration, lane-change and gap-acceptance decisions. Many of these factors are captured by existing driving behaviour models used in microscopic simulation tools. However, while the vast majority of existing models is approximating driving behaviour, primarily focusing on the effects of traffic conditions, little attention has been given to the impact of drivers’ characteristics. The aim of the current thesis is to investigate the effects of stress on driving behaviour and quantify its impact using an econometric modelling framework. This main research question emerged as a result of a widely acknowledged research gap in existing engineering-based driving behaviour models related to the incorporation of human factors and drivers’ characteristics within the model specification. The research was based on data collected using the University of Leeds Driving Simulator. Two main scenarios were presented to participants, while they were also deliberately subjected to stress induced by time pressure and various scenarios. At the same time, stress levels were measured via physiological indicators. Sociodemographic and trait data was also collected in the form of surveys. The data has been initially analysed for each main scenario and several statistics are extracted. The results show a clear effect of time pressure in favour of speeding, however relations related to physiological responses are not always clear. Moreover, two driving behaviour models are developed, a gap-acceptance and a car-following model. In the former model, increase in physiological responses is related to higher probability of accepting a gap and time pressure has a positive effect of gap-acceptance probability as well. In the car-following model, stress is associated with increased acceleration and potentially a more aggressive driving style. The aforementioned analysis is based on data collected in a driving simulator. Given the potential differences in driving behaviour between real and simulated driving, the transferability of a model based on the latter data to field traffic setting is also investigated. Results indicate significant differences in parameters estimated from a video and the simulator dataset, however these differences can be significantly reduced after applying parameter updating techniques. The findings in this thesis show that stress and drivers’ characteristics can influence driving behaviour and thus should be considered in the driving behaviour models for microscopic simulation applications. However, for real life applications, it is suggested that the extent of these effects should be treated with caution and ideally rescaled based on real traffic observations