Analysis on the Correlation Degree between the Driver’s Reaction Ability and Physiological Parameters

In this paper, the correlation degree between driver’s reaction time and the physiological signal is analyzed. For this purpose, a large number of road experiments are performed using the biopac and the reaction time test systems to collect data. First, the electroencephalograph (EEG) signal is processed by using the fast Fourier and the inverse Fourier transforms. Then, the power spectrum densities (PSD) of α, β, δ, and EEG wave are calculated by Welch procedure. The average power of the power spectrum of α, β, and θ is calculated by the biopac software and two ratio formulas, (α+θ)/β and α/β, are selected to be the impact factors. After that the heart rate and the standard deviation of RR interval are calculated from the electrocardiograph (ECG) signal. Lastly, the correlation degree between the eight impact factors and the reaction time are analyzed based on the grey correlation analysis. The results demonstrate that α/β has the greatest correlation to the reaction time except EEG-PSD. Furthermore, two mathematical models for the reaction time-driving time and the α/β-driving time are developed based on the Gaussian function. These mathematical models are then finally used to establish the functional relation of α/β-the reaction time

Effects of Walking in Bamboo Forest and City Environments on Brainwave Activity in Young Adults

Background. In Japan, “Shinrin-yoku” or forest bathing (spending time in forests) is a major practice used for relaxation. However, its effects on promoting human mental health are still under consideration. The objective of this study was to investigate the physiological and psychological relaxation effects of forest walking on adults. Sixty participants (50% males; 50% females) were trained to walk 15-minute predetermined courses in a bamboo forest and a city area (control). The length of the courses was the same to allow comparison of the effects of both environments. Blood pressure and EEG results were measured to assess the physiological responses and the semantic differential method (SDM) and STAI were used to study the psychological responses. Blood pressure was significantly decreased and variation in brain activity was observed in both environments. The results of the two questionnaires indicated that walking in the bamboo forest improves mood and reduces anxiety. Moreover, the mean meditation and attention scores were significantly increased after walking in a bamboo forest. The results of the physiological and psychological measurements indicate the relaxing effects of walking in a bamboo forest on adults

Shinrin-yoku i terapia lasem — przegląd literatury

Forest therapy and shinrin-yoku are concepts that have been appearing more and more often in the literature on theprevention of stress and immune disorders for over a dozen years. In the context of research, it can be said that it plays animportant role not only in the prevention of somatic civilization diseases, such as hypertension or diabetes, but also protectsagainst development and helps in the treatment of mental disorders from the group of anxiety-depressive disorders.In the “Pub Med” database, the search terms “shinrin-yoku” were entered, 23 results and “forest bathing”, 90 results,of which 18 were rejected after repetitive and unrelated searches. Only original papers were analysed (30).Forest therapy eliminates the effects of stress caused by numerous external factors generated by lifestyle in an urbanizedenvironment and, for example, by overworking. It increases immunity, affecting, among others on the amountand activity of NK cells, it has a positive effect on metabolic parameters in ischemic heart disease and hypertension.It supports relaxation, attention and convalescence after stress. In Asian countries, it is an official branch of medicine,which is dedicated to profiled medical centers. In European countries we often meet conferences dedicated to foresttherapies and specialized trainings.Forest therapy is a well-documented therapeutic method that can be used in the prevention, support of treatment andrehabilitation of stress disorders and civilization diseases.Terapia lasem i shinrin-yoku to pojęcia, które od kilkunastu lat coraz częściej pojawiają się w literaturze dotyczącej profilaktyki stresu i zaburzeń odporności. W kontekście badań można stwierdzić, że terapia lasem odgrywa istotną rolę nie tylko w zapobieganiu somatycznym chorobom cywilizacyjnym, jak nadciśnienie tętnicze czy cukrzyca, ale także chroni przed rozwinięciem oraz pomaga w leczeniu chorób psychicznych z grupy zaburzeń lękowo-depresyjnych. W bazie danych „Pub Med” wpisano hasła „shinrin-yoku”, uzyskując 23 wyniki, oraz „forest bathing”, uzyskując 90 wyników, z czego po odrzuceniu powtarzających się w poprzednim wyszukiwaniu oraz niezwiązanych z tematem pozostało 18. Przeanalizowano tylko prace badawcze (30). Terapia lasem niweluje skutki stresu spowodowanego licznymi czynnikami zewnętrznymi, generowanymi przez styl życia w środowisku zurbanizowanym, a także na przykład z przepracowania. Podnosi odporność, wpływając między innymi na liczbę i aktywność komórek natural killers (NK), wpływa korzystnie na parametry metaboliczne w chorobie niedokrwiennej serca i nadciśnieniu tętniczym, wspomaga relaks, koncentrację uwagi oraz rekonwalescencję po stresie. W krajach azjatyckich stanowi oficjalną gałąź medycyny, której poświęcone są profilowane centra medyczne. W krajach europejskich coraz częściej organizowane są konferencje poświęcone terapii lasem oraz specjalistyczne szkolenia. Terapia lasem stanowi dobrze udokumentowaną metodę terapeutyczną i może mieć zastosowanie w profilaktyce, wspomaganiu leczenia i rehabilitacji zaburzeń stresowych i chorób cywilizacyjnych

Non-visual Effects of Road Lighting CCT on Driver's Mood, Alertness, Fatigue and Reaction Time: A Comprehensive Neuroergonomic Evaluation Study

Good nighttime road lighting is critical for driving safety. To improve the quality of nighttime road lighting, this study used the triangulation method by fusing "EEG evaluation + subjective evaluation + behavioral evaluation" to qualitatively and quantitatively investigate the response characteristics of different correlated color temperature (CCT) (3500K, 4500K, 5500K, 6500K) on drivers' non-visual indicators (mood, alertness, fatigue and reaction time) under specific driving conditions (monotonous driving; waiting for red light and traffic jam; car-following task). The results showed that the CCT and Task interaction effect is mainly related to individual alertness and reaction time. Individual subjective emotional experience, subjective visual comfort and psychological security are more responsive to changes in CCT than individual mental fatigue and visual fatigue. The subjective and objective evaluation results demonstrated that the EEG evaluation indices used in this study could objectively reflect the response characteristics of various non-visual indicators. The findings also revealed that moderate CCT (4500K) appears to be the most beneficial to drivers in maintaining an ideal state of mind and body during nighttime driving, which is manifested as: good mood experience; it helps drivers maintain a relatively stable level of alterness and to respond quickly to external stimuli; both mental and visual fatigue were relatively low. This study extends nighttime road lighting design research from the perspective of non-visual effects by using comprehensive neuroergonomic evaluation methods, and it provides a theoretical and empirical basis for the future development of a humanized urban road lighting design evaluation system.Comment: 38 pages, 15 figures, 103 conference

Advanced Internet of Things for Personalised Healthcare System: A Survey

As a new revolution of the Internet, Internet of Things (IoT) is rapidly gaining ground as a new research topic in many academic and industrial disciplines, especially in healthcare. Remarkably, due to the rapid proliferation of wearable devices and smartphone, the Internet of Things enabled technology is evolving healthcare from conventional hub based system to more personalised healthcare system (PHS). However, empowering the utility of advanced IoT technology in PHS is still significantly challenging in the area considering many issues, like shortage of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, multi-dimensionality of data generated and high demand for interoperability. In an effect to understand advance of IoT technologies in PHS, this paper will give a systematic review on advanced IoT enabled PHS. It will review the current research of IoT enabled PHS, and key enabling technologies, major IoT enabled applications and successful case studies in healthcare, and finally point out future research trends and challenges

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare