Trends in Daily Heart Rate Variability Fluctuations Are Associated with Longitudinal Changes in Stress and Somatisation in Police Officers

The emergence of wearable sensors that allow for unobtrusive monitoring of physiological and behavioural patterns introduces new opportunities to study the impact of stress in a real-world context. This study explores to what extent within-subject trends in daily Heart Rate Variability (HRV) and daily HRV fluctuations are associated with longitudinal changes in stress, depression, anxiety, and somatisation. Nine Dutch police officers collected daily nocturnal HRV data using an Oura ring during 15–55 weeks. Participants filled in the Four-Dimensional Symptoms Questionnaire every 5 weeks. A sample of 47 five-week observations was collected and analysed using multiple regression. After controlling for trends in total sleep time, moderate-to-vigorous physical activity and alcohol use, an increasing trend in the seven-day rolling standard deviation of the HRV (HRVsd) was associated with increases in stress and somatisation over 5 weeks. Furthermore, an increasing HRV trend buffered against the association between HRVsd trend and somatisation change, undoing this association when it was combined with increasing HRV. Depression and anxiety could not be related to trends in HRV or HRVsd, which was related to observed floor effects. These results show that monitoring trends in daily HRV via wearables holds promise for automated stress monitoring and providing personalised feedback.

A new method for high resolution curvature measurement applied to stress monitoring in thin films

By combining the well-known grid reflection method with a digital image correlation algorithm and a geometrical optics model, a new method is proposed for measuring the change of curvature of a smooth reflecting substrate, a common reporter of stress state of deposited layers. This tool, called Pattern Reflection for Mapping of Curvature (PReMC), can be easily implemented for the analysis of the residual stress during deposition processes and is sufficiently accurate to follow the compressivetensile-compressive stress transition during the sputtering growth of a Ag film on a Si substrate. Unprecedented resolution below 10-5m-1can be reached when measuring a homogeneous curvature. A comparison with the conventional laser-based tool is also provided in terms of dynamical range and resolution. In addition, the method is capable of mapping local variations in the case of a non-uniform curvature as illustrated by the case of a non-homogeneous Mo film under high compressive stress. PReMC offers interesting perspectives for in situ accurate stress monitoring in the field of thin film growth.

USE OF EXPERT SOFTWARE AND PHEROMONES TRAPS FOR BIOCENOTIC STRESS MONITORING AND EARLY WARNING OF TREATMENTS AGAINST INSECTS IN ORCHARDS

This paper presents our results in use of the specialized software and specific modules for microclimate monitoring and pest biological cycle assessment, to evaluate and quantify the attack risk for microclimate monitoring, combined with 6 type specific pheromones produced in Romania, in order to determine their efficacy in detecting the targeted micro Lepidoptera, assess their population flight pattern, as well and the biocenotic stress, both tools categories aiming to the precise positioning of the treatments to achieve integrated pests management and reduce the overall impact of the treatments with insecticides on the environment. According the fruit species, several strategies have been defined and followed by several insecticide applications into the bearing orchards, to achieve a better control of damaging micro Lepidoptera. Use of the mixed monitoring systems in tandem with specific pheromones contributed to a more efficient use of the insecticides and increased performances, both for pome and stone fruit species as well.

Cloud Server and Internet of Things Assisted System for Stress Monitoring

Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.

An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress

Life in modern societies is fast-paced and full of stress-inducing demands. The development of stress monitoring methods is a growing area of research due to the personal and economic advantages that timely detection provides. Studies have shown that speech-based features can be utilised to robustly predict several physiological markers of stress, including emotional state, continuous heart rate, and the stress hormone, cortisol. In this contribution, we extend previous works by the authors, utilising three German language corpora including more than 100 subjects undergoing a Trier Social Stress Test protocol. We present cross-corpus and transfer learning results which explore the efficacy of the speech signal to predict three physiological markers of stress—sequentially measured saliva-based cortisol, continuous heart rate as beats per minute (BPM), and continuous respiration. For this, we extract several features from audio as well as video and apply various machine learning architectures, including a temporal context-based Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). For the task of predicting cortisol levels from speech, deep learning improves on results obtained by conventional support vector regression—yielding a Spearman correlation coefficient (ρ) of 0.770 and 0.698 for cortisol measurements taken 10 and 20 min after the stress period for the two corpora applicable—showing that audio features alone are sufficient for predicting cortisol, with audiovisual fusion to an extent improving such results. We also obtain a Root Mean Square Error (RMSE) of 38 and 22 BPM for continuous heart rate prediction on the two corpora where this information is available, and a normalised RMSE (NRMSE) of 0.120 for respiration prediction (−10: 10). Both of these continuous physiological signals show to be highly effective markers of stress (based on cortisol grouping analysis), both when available as ground truth and when predicted using speech. This contribution opens up new avenues for future exploration of these signals as proxies for stress in naturalistic settings.

Implementasi Metode Naive Bayes untuk Mendeteksi Stres Siswa Berdasarkan Tweet pada Sistem Monitoring Stres

<p class="Abstrak">Gangguan mental saat ini masih menjadi permasalahan bagi bidang kesehatan di seluruh dunia. Salah satu jenis dari gangguan mental yang dapat diprediksi saat ini adalah stres. Stres memiliki dampak yang sangat besar bagi Kesehatan penderitanya, namun masih banyak masyarakat yang terlalu meremehkan perihal keberadaan penyakit stres ini. Hal ini salah satunya disebabkan oleh media yang dapat digunakan untuk melakukan pengecekan tingkat stres masih sangat sedikit. Sejauh ini, pengecekan kondisi kesehatan mental khususnya stres dapat dilakukan melalui konsultasi ke psikolog terdekat. Namun, tidak banyak masyarakat yang mengetahui hal itu. Ketika seseorang mengalami gangguan kecemasan khususnya stres, maka ia akan cenderung melakukan tindakan yang dapat mengekspresikan kecemasannya di media sosial. Kegiatan ini dinamakan Self Disclosure. Hal ini dianggap dapat mengurangi beban penderita gangguan mental tersebut. Mengenai hal itu, saat ini penggunaan media sosial menjadi hal yang sangat lumrah dimasyarakat khususnya remaja. Salah satu jenis sosial media yang banyak digunakan oleh masyarakat adalah Twitter. Salah satu keunggulan Twitter adalah dikarenakan twitter lebih mudah digunakan dan memiliki tampilan yang sederhana. Selain itu, penulisan tweet pada akun twitter memiliki pembatasan jumlah karakter sehingga tweet yang ditulis pengguna lebih jelas dan ringkas. Oleh karena itu,pada penelitian ini akan dibangun fitur untuk mendeteksi tingkat stres melalui tweet pada akun twitter dengan menggunakan metode <em>Naïve Bayes</em> yang mana akan dapat mengklasifikasikan tingkat stres siswa berdasarkan tweet siswa kedalam tiga kelas yaitu kelas stres ringan, stres sedang dan stres berat. Fitur ini nantinya akan diimplementasikan pada sistem monitoring stres siswa berbasis website sebagai bahan pertimbangan bagi siswa dan guru bimbingan konseling dalam proses konseling siswa. Berdasarkan pengujian akurasi yang dilakukan dengan 90 data latih dan 4 data uji, maka didapatkan tingkat akurasi fitur ini mencapai angka 75%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Mental disorders are currently still a problem for the health sector around the world. Stress is one type of mental disorder that can be predicted today. Stress has a huge impact on the health of sufferers, but there are still many people who underestimate the existence of the stress disease. This is partly due to the very few media that can be used to check stress levels. So far, checking mental health conditions, especially stress can be done through consultation with the nearest psychologist. However, not many people know about it. When a person experiences anxiety disorders, especially stress, he will tend to take actions that can express his anxiety on social media. This activity is called Self Disclosure. This is considered to reduce the burden on those with mental disorders. Regarding this, currently the use of social media is very common in society, especially teenagers. One type of social media that is widely used by the public is Twitter. One of the advantages of Twitter is that it is easier to use and has a simple interface. In addition, writing tweets on a Twitter account has a limit on the number of characters so that the tweet that the user writes is clearer and more concise. Therefore, this research will build a feature to detect stress levels via tweet on a twitter account using the Naïve Bayes method which will be able to classify students' stress levels based on student tweets into three classes, namely light stress, moderate stress and severe stress classes. This feature will later be implemented in a website-based student stress monitoring system as a consideration for students and counseling teachers in the student counseling process. Based on accuracy testing carried out with 90 training data and 4 test data, the accuracy rate of this feature is 75%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Analysis of Deformation Characteristics of Filled Subgrade Treated by Micro-Pile

Deformation control of fill subgrade is a difficult point in the field of highway engineering. This article relies on the treatment project of micro-piles for subgrade deformation of K555 + 070～K557 + 710 section of Qingdao-Yinchuan Expressway. Settlement and stress monitoring was carried out at the toe of the roadbed, the shoulder, and the center of the roadbed. We use Midas/GTS modeling trial calculation. A new method to check the simulation results of the deviation rate is proposed. A calculation model of the stress and deformation of the subgrade in the whole life cycle is established. The results of comparative analysis, monitoring, and simulation are as follows. ① The compression and consolidation of the fill account for most of the settlement and deformation of the roadbed in the whole life cycle. ② Subgrade center settlement is the sensitive part of subgrade deformation. ③ After piles are added, the deformation extremes at the toe of the slope, the shoulder, and the center of the roadbed are all reduced by more than 96%. The research results can provide theoretical guidance for the analysis of the stress and deformation characteristics of the subgrade before and after the micro-pile treatment.

A new approach for displacement and stress monitoring of tunnel based on iFEM methodology

Structural monitoring plays a key role for underground structures such as tunnels. Strain readings are expected to report structural conditions during construction and at the final delivery of the works. Furthermore, it is increasingly requested an extension to long-term monitoring from contractors with possible use of the same system in service during construction. A robust and efficient monitoring methodology from discrete strain measurements is the inverse Finite Element Method (iFEM), which allows to reconstruct the structural response without input data on the load pattern applied to the structure as well as material and inertial properties of the elements and therefore it is interesting for structural configurations affected by uncertain loading conditions, such as the tunnel. The formulation presented in this paper, based on the iFEM theory, is improved from the previous work available in literature for both the shape functions used and the computational procedure. Indeed, the approach allows to overcome inconsistencies related to structural loading conditions and a pseudo-inverse matrix preserve all the rigid body modes without imposing specific constraints which is typical for tunnels. Numerical validation of the iFEM procedure is performed by simulating the input data coming from a tunnel working in a heterogeneous soil under different loading conditions with direct FEM analysis.