Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

In this study, we aim to better understand the cognitive-emotional experience of visually impaired people when navigating in unfamiliar urban environments, both outdoor and indoor. We propose a multimodal framework based on random forest classifiers, which predict the actual environment among predefined generic classes of urban settings, inferring on real-time, non-invasive, ambulatory monitoring of brain and peripheral biosignals. Model performance reached 93% for the outdoor and 87% for the indoor environments (expressed in weighted AUROC), demonstrating the potential of the approach. Estimating the density distributions of the most predictive biomarkers, we present a series of geographic and temporal visualizations depicting the environmental contexts in which the most intense affective and cognitive reactions take place. A linear mixed model analysis revealed significant differences between categories of vision impairment, but not between normal and impaired vision. Despite the limited size of our cohort, these findings pave the way to emotionally intelligent mobility-enhancing systems, capable of implicit adaptation not only to changing environments but also to shifts in the affective state of the user in relation to different environmental and situational factors

The multimodal parameter enhancement of electroencephalogram signal for music application

Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction, and music class. The weighted stress condition was obtained from stress classes. The EEG signal produces signal features extracted from the frequency domain and time-frequency domain via techniques such as power spectrum density (PSD), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). Power value is evaluated in PSD. The energy distribution is derived from STFT and CWT techniques. Two types of music were used in this experiment. The multimodal fusion is tested using a six-performance measurement method. The purposed multimodal parameter shows the highest accuracy is 97.68%. The sensitivity of this study presents over 95% and the high value for specificity is 89.5%. The area under the curve (AUC) value is 1 and the F1 score is 0.986. The informedness values range from 0.793 to 0.812 found in this paper

Overview of Biosignal Analysis Methods for the Assessment of Stress

Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation   of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems.  Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PD

The enhancement on stress levels based on physiological signal and self-stress assessment

The prolonged stress needs to be determined and controlled before it harms the physical and mental conditions. This research used questionnaire and physiological approaches in determine stress. EEG signal is an electrophysiological signal to analyze the signal features. The standard features used are peak-to-peak values, mean, standard deviation and root means square (RMS). The unique features in this research are Matthew Correlation Coefficient Advanced (MCCA) and multimodal capabilities in the area of frequency and time-frequency analysis are proposed. In the frequency domain, Power Spectral Density (PSD) techniques were applied while Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were utilized to extract seven features based on time-frequency domain. Various methods applied from previous works are still limited by the stress indices. The merged works between quantities score and physiological measurements were enhanced the stress level from three-levels to six stress levels based on music application will be the second contribution. To validate the proposed method and enhance performance between electroencephalogram (EEG) signals and stress score, support vector machine (SVM), random forest (RF), K- nearest neighbor (KNN) classifier is needed. From the finding, RF gained the best performance average accuracy 85% ±10% in Ten-fold and K-fold techniques compared with SVM and KNN

The influence of urban visuospatial configuration on older adults’ stress: A wearable physiological-perceived stress sensing and data mining based-approach

Population ageing raises many fundamental questions, including how the urban environment can be configured to promote active ageing. The perceived element for older adults' involvement in the environment differs from the average person. Despite this difference, there is little to no research into understanding how the perceived elements (specifically, the visuospatial configuration) of the environment influence older adults' involvement—most studies focused on younger adults. The focus here is stress, which occurs when environmental demand exceeds a person's capability. As stress impacts a person's involvement in the environment and older adults are more likely to feel stress due to their decline in functional capability, it is important to understand how the visuospatial configuration of urban environment influence stress. Older adults were recruited to participate in an urban environment walk while their physiological responses (Photoplethysmogram) were monitored using wearable sensors. Their perceived stress responses were also collected. Spatial clustering and hot spot analysis were conducted to detect locations with clusters of physiological responses caused by spatial factors. These locations were subsequently labelled as stress or non-stress based on participants' perceived stress. The perceived visual elements of the urban environment were extracted using isovist analysis. Principal component analysis, self-organising map and machine learning algorithms were used to understand the relationship. The results demonstrate that isovist minimum visibility, occlusivity, and isovist area are the most influential determinants of older adults' physiological stress. Older adults prefer urban configurations where they can be seen. This study can be used to inform urban design and planning

Detecting stressful older adults-environment interactions to improve neighbourhood mobility: A multimodal physiological sensing, machine learning, and risk hotspot analysis-based approach

Not only is the global population ageing, but also the built environment infrastructure in many cities and communities are approaching their design life or showing significant deterioration. Such built environment conditions often become an environmental barrier that can either cause stress and/or limit the mobility of older adults in their neighbourhood. Current approaches to detecting stressful environmental interactions are less effective in terms of time, cost, labour, and individual stress detection. This study harnesses the recent advances in wearable sensing technologies, machine learning intelligence and hotspot analysis to develop and test a more efficient approach to detecting older adults' stressful interactions with the environment. Specifically, this study monitored older adults' physiological reactions (Photoplethysmogram and electrodermal activity) and global positioning system (GPS) trajectory using wearable sensors during an outdoor walk. Machine learning algorithms, including Gaussian Support Vector Machine, Ensemble bagged tree, and deep belief network were trained and tested to detect older adults' stressful interactions from their physiological signals, location and environmental data. The Ensemble bagged tree achieved the best performance (98.25% accuracy). The detected stressful interactions were geospatially referenced to the GPS data, and locations with high-risk clusters of stressful interactions were detected as risk stress hotspots for older adults. The detected risk stress hotspot locations corresponded to the places the older adults encountered environmental barriers, supported by site inspections, interviews and video records. The findings of this study will facilitate a near real-time assessment of the outdoor neighbourhood environment, hence improving the age-friendliness of cities and communities

Wearable sensing and mining of the informativeness of older adults : physiological, behavioral, and cognitive responses to detect demanding environmental conditions

Due to the decline in functional capability, older adults are more likely to encounter excessively demanding environmental conditions (that result in stress and/or mobility limitation) than the average person. Current efforts to detect such environmental conditions are inefficient and are not person-centered. This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses (i.e., electroencephalography, photoplethysmography, electrodermal activity, and gait) and location data from older adults to detect demanding environmental conditions. Computationally, this study developed a Random Forest algorithm—considering the informativeness of the bodily response—and a hot spot analysis-based approach to identify environmental locations with high demand. The approach was tested on data collected from 10 older adults during an outdoor environmental walk. The findings demonstrate that the proposed approach can detect demanding environmental conditions that are likely to result in stress and/or limited mobility for older adults

СТРАТЕГІЇ ВПРОВАДЖЕННЯ АГІЛЬНОГО УПРАВЛІННЯ: ІННОВАЦІЇ ДЛЯ ЛІДЕРІВ

В статті авторами визначено, що агільне управління – це методологія управління проектами, яка ґрунтується на гнучкості, співпраці та швидкій адаптації до змін. Вказано, що агільне управління дозволяє командам ефективно працювати в швидкозмінних середовищах. забезпечуючи швидкий розвиток проекту, комунікувати, співпрацювати та бути гнучкими. У статті названі принципи агільного управління – перевага індивідуальності та взаємодії, акцент на робочому програмному забезпеченні, значення співпраці з клієнтом, гнучкість у реагуванні на зміни. Обґрунтовані характеристики агільного управління – здатність швидко реагувати на можливості, скорочувати цикли прийняття рішень, керувати змінами, прислухалися до клієнта, управляти ризиками, створювати проектні команди зі спеціалістів різних напрямів, знизити організаційну ізоляцію, впроваджувати планування, використовувати нові технології. Аргументовано переваги агільного управління, зокрема: свобода у виборі методів та напрямів роботи, ефективне використання ресурсів, гнучкість та адаптивність, швидке виявлення проблем, співпраця з клієнтами, швидкий зворотній звʼязок, можливість підвищити задоволеність клієнтів, прозорість та підзвітність, зростання продуктивності команд. Зазначені недоліки агільного управління, зокрема: непослідовні результати, прогрес важко виміряти, часові обмеження, труднощі при постійній співпраці між командами або кінцевими користувачами, що має відбуватися постійно. Доведено, що перехід до агільного управління може викликати труднощі, якщо команда раніше спиралася на традиційний підхід. Вказано, що агільне управління будується на довірі