Objectives: Emotions and stress affect voice production. There are only a few reports in the literature on how changes in the autonomic nervous system affect voice production. The aim of this study was to examine emotions and measure stress reactions during a voice examination procedure, particularly changes in the muscles surrounding the larynx. Material and Methods: The study material included 50 healthy volunteers (26 voice workers - opera singers, 24 control subjects), all without vocal complaints. All subjects had good voice quality in a perceptual assessment. The research procedure consisted of 4 parts: an ear, nose, and throat (ENT)-phoniatric examination, surface electromyography, recording physiological indicators (heart rate and skin resistance) using a wearable wristband, and a psychological profile based on questionnaires. Results: The results of the study demonstrated that there was a relationship between positive and negative emotions and stress reactions related to the voice examination procedure, as well as to the tone of the vocal tract muscles. There were significant correlations between measures describing the intensity of experienced emotions and vocal tract muscle maximum amplitude of the cricothyroid (CT) and sternocleidomastoid (SCM) muscles during phonation and non-phonation tasks. Subjects experiencing eustress (favorable stress response) had increased amplitude of submandibular and CT at rest and phonation. Subjects with high levels of negative emotions, revealed positive correlations with SCMmax during the glissando. The perception of positive and negative emotions caused different responses not only in the vocal tract but also in the vegetative system. Correlations were found between emotions and physiological parameters, most markedly in heart rate variability. A higher incidence of extreme emotions was observed in the professional group. Conclusions: The activity of the vocal tract muscles depends on the type and intensity of the emotions and stress reactions. The perception of positive and negative emotions causes different responses in the vegetative system and the vocal tract. Int J Occup Med Environ Health. 2024;37(1):84-97

BackgroundThe relation between the autonomic nervous system (ANS) and muscles of the vocal tract is of particular importance when considering the pathomechanism of a functional voice disorder.AimsThe aim of this study was to record electrophysiological indicators from the ANS as well as the tone of the external laryngeal muscle and test whether together they could point to an enhanced risk of primary functional voice disorder.Materials and methodsThe study material consisted of 81 people, 27 of whom were professional opera singers. None reported any voice complaints. The research comprised ENT and phoniatric examination, superficial electromyography (SEMG), and recording of physiological indicators (pulse rate, skin resistance).ResultsAll subjects had a clear voice with no sign of vocal disability. Endoscopy revealed laryngeal hyperfunction in 26 people. SEMG revealed that the 26 had increased external laryngeal muscle tone during phonation, and this finding correlated with a change in certain electrophysiological indicators HRV, BVP, EDA.ConclusionsWe conclude that anomalies in electrophysiological parameters in individuals with subclinical symptoms of functional voice disorder may be at risk of developing fully symptomatic hyperfunctional dysphonia in the future. Vocal training, which differentiates singers and non-singers, is known to have an effect on subclinical hyperfunctional dysphonia.SignificanceBy measuring indicators of hyperfunctional dysphonia, it may be possible to take remedial action before symptomatic dysphonia develops.

In the paper, convolutional neural network models were proposed that classify the patient’s EEG into one of the three groups of parkinsonism, i.e., no symptoms (PD-N), mild cognitive impairment(PD-MCI), and Parkinson’s Disease Dementia (PD-PDD). Three different architectures of Deep Convolution Neural Networks were proposed. As the input of the CNN, two approaches were employed: the raw EEG signal and its transformation to power spectral density (PSD). The classification process was performed as a three-class task (PD-N vs PD-MCI vs PDD) and a two-class problem (PD-N vs PD-MCI, PD-N vs PD-PDD, and PD-MCI vs PD-PDD). The obtained accuracy for three classes exceeded 50

This article presents method for recognizing activity from data acquired from the accelerator, magnetometer, gyroscope and motion sensors. The experiments providing data were conducted in July 2021 in Katowice, Poland, as a part of the System for Monitoring Activity and Training Rationalization (SMART) project, financed by the polish National Centre for Research and Development. A variety of classifiers were tested in two approaches – using all available variables and using features selected with the Joint Mutual Information method. Separate models were built for each activity as well as models for selecting one activity out of 8 possible. The best obtained results exceeded 98

Posture disorder affects more and more people in every age and social group, causing a huge drop in energy (tiredness, depression) or even nerve compression. The early assessment allows introducing therapy that minimizes the influence on everyday life. In the presented approach authors focused on the analysis of thermal images. The research group consists of 101 people, but only 69 were selected for further analysis (24 in the pathological group and 44 described as normative). Appropriate clothing for women turns out to be a very important element of the protocol. Each patient was photographed 3 times by thermovision camera (before, immediately after and 10 min after exercises). Using a proprietary algorithm the patient’s body (back) was divided into 10 ROI. The cross-validation kNN classifier was used to divide the subject into appropriate categories – normative or pathological. On the basis of the temperature data, the matched-pair t-test shows the significant statistical difference before and after exercise for all ROI in both normative and pathological group. Classification result based on after exercise data is 72.1% in contrary to 57.4% obtained from data before exercise.

Sleep is a physiological state in which quality and quantity should be kept accurate to maintain health in humans. Length of sleep is one of the indicators of sleep quality along with sleep latency, characteristics of awakenings during the night, and sleep architecture disturbance. Such parameters can be analyzed with subjective and objective methods. The most comprehensive objective method, known as the gold standard of sleep assessment, is polysomnography. It consists primarily of electroencephalography, electrooculography and electromyography, which enable evaluation of sleep stages occurring during the study. Apart from assessing sleep architecture, in polysomnography the sensors usually record the position of the patient’s body at night, limbs, and breathing movement. There are numerous risk factors of sleep disorders like smoking, abusing alcohol, or obesity. The aim of the study was to assess the sleep parameters of men by their BMI value. Sleep parameters from polysomnographic records of 94 men by the value of their BMI were tested with the Pearson correlation coefficient test. The higher BMI the men had, the shorter was their total sleep time (p = 0.017), the lower was their sleep efficiency (p = 0.013), the more sleep apnea (p = 0.003) and oxygen desaturations (p = 0.002) per hour of NREM sleep they had. The mean BMI of the sample indicates the men were obese, hence the accumulation of fat tissue around the throat. Such sleep disturbances often occur due to fat accumulation around the throat in people with high BMI. It is a cause of narrowing the throat, making it easier to collapse during sleep, preventing breathing. Such episodes of apnea cause oxygen desaturation and awakening

The paper presents a new approach to preventive examinations of employees in relation to the low reliability of the appraisals made due to experiencing stress that does not allow employees to fully present their potential. This approach is illustrated using the example of a case study of a voice professional. The study provides a detailed, quantitative view of the emotions experienced, revealed during the voice recording and audiometry procedure. Differences were found as a result of the study in terms of the intensity of emotions. EDA and HRV measurement values were highest when questionnaires were being completed concerning the emotions experienced during voice recording and audiometry. The discussion focuses on the possibilities of analysing emotions using psychophysiological measurements and on the benefits of combining research methods (physical examinations, psychological examinations, and psychophysiological measurements) in the context of employee appraisal to predict the effects of the impact of work on subjective well-being and health of individuals.

The temporomandibular joint (TMJ) is an even joint in the human head that allows for a movable connection between the skull and the mandible and performs complex movements. According to statistical data, it is estimated that disorders within the masticatory apparatus affect 60 to 80

The material describes the basic functional features of the diagnostic support system in the case of neurodegenerative diseases, which is the subject of the international research and implementation project Tele-BRAIN. The project’s main goal is to optimise the diagnostic process in the area of Parkinson’s disease based on automatic EEG analysis with the use of artificial intelligence methods. The main part of the project is a system of early, automatic or semi-automatic (with the participation of an expert) algorithmic early diagnosis of cognitive disorders in Parkinson’s disease. Identifying cognitive dysfunction in advance (in relation to the occurrence of easily recognisable symptoms) may allow for the implementation of a procedure that positive influence the extension of the patient’s independence period, a better quality of patient’s life as well for the caregiver, and in the future it may also allow for neuroprotective interventions slowing down the development of dementia. The presented algorithm is currently being implemented, and the aim of the article is to present the concept and functional evaluation of the pilot version of the system.

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

Postural disorders, their prevention, and therapies are still growing modern problems. The currently used diagnostic methods are questionable due to the exposure to side effects (radiological methods) as well as being time-consuming and subjective (manual methods). Although the computer-aided diagnosis of posture disorders is well developed, there is still the need to improve existing solutions, search for new measurement methods, and create new algorithms for data processing. Based on point clouds from a Time-of-Flight camera, the presented method allows a non-contact, real-time detection of anatomical landmarks on the subject's back and, thus, an objective determination of trunk surface metrics. Based on a comparison of the obtained results with the evaluation of three independent experts, the accuracy of the obtained results was confirmed. The average distance between the expert indications and method results for all landmarks was 27.73 mm. A direct comparison showed that the compared differences were statically significantly different; however, the effect was negligible. Compared with other automatic anatomical landmark detection methods, ours has a similar accuracy with the possibility of real-time analysis. The advantages of the presented method are non-invasiveness, non-contact, and the possibility of continuous observation, also during exercise. The proposed solution is another step in the general trend of objectivization in physiotherapeutic diagnostics.

Danch-Wierzchowska, Marta Bugdol, Marcin Mitas, Andrzej W.The analysis of the patient’s psychophysiological condition is one of the key elements of properly conducted therapy. In the therapeutic tasks special attention is paid to monitoring the patient’s condition. Our method proposed a robust for segmentation errors (based on median value) and an easily applied method for assessing the thermal profile of the face and its usage in psychophysiological state observation. The obtained results suggest that the central part of the face provides sufficient information to discriminate between an active and a non active individual, even during minor physical effort.

In this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year +/- 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

A study on voice fatigue evaluation is presented in this paper. It concerns the problem of vocal apparatus disorders with a general goal in the computer-assisted proper voice emission teaching support and possible disorder diagnosis. The singing and speech samples provided by 20 singers were recorded before and after a two-hour exhaustive performance. Nine features were extracted from each recording. Each feature was subjected to a statistical analysis using principal component analysis and Wilcoxon test producing feature importance ranking and individual p-values. Finally, a support vector classifier was employed for voice fatigue detection in separate singing and speech experiments, yielding assessment accuracies at 62.9% and 70.9%, respectively. The speech signal proved to reflect the voice fatigue more reliably than singing.

In this paper results of a research were presented, which started with statistical analysis, performed to extract parameters on the basis of which it would be possible to assess girls' sexual maturity. The parameters selected during the analysis were used in classification. Three classifiers (Random Forest, Linear Discriminant Analysis and Support Vector Machine) were used. Their effectiveness was evaluated using the following measures: sensitivity, specificity, precision, accuracy and F-score. The obtained results suggested that girls' sexual maturity assessment can be performed on the basis of anthropometric data including measurements in the area of the hips, fat folds and fat content in the body. The maturity status of a girl can be also estimated using parameters extracted from her voice combined with the information about her age. The best accuracy for these features was archived for Random Forest classifier (88.70% for voice and 89.13% for anthropometric data).

Mortality rate increases exponentially with people’s age. They gather age-related diseases and become ‘frail’, i.e. increasingly vulnerable to various stressors. The gold standard to evaluate the severity of upper limb motor symptoms is to use the UPDRS-part III (motor examination) yet it is proven that simple tests of manual dexterity can identify persons at high risk for neurodegenerative diseases. The aim of this study is to evaluate a novel, objective, observer-independent and assistance-independent tablet device-based method which could be helpful in the upper limb function diagnostics designed for telemedicine technology and frailty assessment. The test employs a tablet and a fixed-height obstacle. The patient is required to move his/her hand above the obstacle and touch the round fields displayed on the tablet. The test is performed for 30 s, as fast as possible, using pointing finger of the dominating hand. Several parameters are registered using the tablet. In the study, the test is evaluated in the group inpatients of geriatric hospital separated into Frail (14 patients) and Control (14 patients) groups. The patients in the Control group are featuring (Mann-Whitney U-test, p-value < 0.05) more correct touches ( 23.4± 8.2 vs. 17.1± 12.4 ) than the members of the Frail group. The reaction time and mean time between touches in Control group is shorter than in the Frail group (respectively Mann-Whitney U-test and Student’s t-test; p-value < 0.05).

Dysleksja jest przedmiotem wielu badań. Jedną z przyczyn tego zjawiska może być wzrastający w ostatnich latach odsetek społeczeństwa ze zdiagnozowanym problemem specyficznych trudności w nauce (obecnie zdiagnozowaną dysleksję ma ok. 10% uczniów; Brejnak, 2003). Pedagodzy szkolni w ramach posiadanych środków i czasu pracy często nie są w stanie zdiagnozować wszystkich uczniów z problemami w nauce. Zaproponowany uproszczony system o cechach user friendly został wdrożony w dwóch szkołach podstawowych, gdzie został pozytywnie oceniony przez pedagogów szkolnych. Dokładność poszczególnych modułów systemu oscylowała w okolicach 80%, swoistość przyjmowała wartości 90–100%, natomiast czułość wahała się między 60 a 70%.

The paper presents a system that recognizes the make, colour and type of the vehicle. The classification has been performed using low quality data from real-traffic measurement devices. For detecting vehicles' specific features three methods have been developed. They employ several image and signal recognition techniques, e.g. Mamdani Fuzzy Inference System for colour recognition or Scale Invariant Features Transform for make identification. The obtained results are very promising, especially because only on-site equipment, not dedicated for such application, has been employed. In case of car type, the proposed system has better performance than commonly used inductive loops. Extensive information about the vehicle can be used in many fields of Intelligent Transport Systems, especially for traffic supervision.

In this paper, a method for girls’ pubertal status evaluation is presented. The proposed algorithm uses voice features. Spectral analysis, Support Vector Machine and Random Forest Trees were employed. The obtained results are promising. Sensitivity reached 89.38%, when all features were included in the calculations (SVM). The highest specificity was achieved when only standard deviations were used (80.14% for the RF). Accuracy was greater than 80% for both classifiers when all features were used.

The paper presents the results of boys’ age modeling on the basis of the features of their voice. The research group has been divided according to age and the threshold has been 14 years. 98 boys have been examined (57 aged less than 14 years, 41 aged 14 years or more). Voice data has been acquired and processed. The obtained coefficients have been subjected to Principal Component Analysis and then linear models have been built, estimeting the boys’ age. The obtained results are promising and are especially good in case of the group of younger boys, where the median absolute error has been less than 6 months and the median relative error has been equal to 2.1%.