Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Romaniszyn-Kania, Patrycja; Pollak, Anita; Bugdoł, Marcin; Bugdol, Monika; Kania, Damian; Mańka, Anna; Danch–Wierzchowska, Marta; Mitas, Andrzej W.

doi:10.3390/s21144853

Cited by 14 publications

(6 citation statements)

References 84 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Certain indices were calculated at a later stage using MATLAB software. Basic parameters and their statistics were calculated for each signal according to standard methods [29]. For the EDA signal, the following were determined: mean (M), minimum (min.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the relationship between emotion intensity and electrophysiology parameters during a voice examination of opera singers

Krasnodębska,

Szkiełkowska,

Pollak

et al. 2024

Int J Occup Med Environ Health.

View full text Add to dashboard Cite

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 SCM max 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Analysis of the relationship between emotion intensity and electrophysiology parameters during a voice examination of opera singers

Krasnodębska,

Szkiełkowska,

Pollak

et al. 2024

Int J Occup Med Environ Health.

View full text Add to dashboard Cite

show abstract

“…The presented acquisition setup is an integral part of a larger, original device, called Disc4Spine, which is equipped with various measuring systems for assessing the patient’s condition during diagnosis and therapeutic procedures of postural defects [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Real-Time Back Surface Landmark Determination Using a Time-of-Flight Camera

Ledwoń

Danch–Wierzchowska

Bugdoł

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…By taking appropriate measures to reduce job stress levels, management can boost employee morale and productivity. According to Izard's theory, a critical safety scenario might cause emotions, according to Kania et al [15]. This approach specifies four systems that activate emotions: the neural system, activated by brain electrical stimulation.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of stress based on multiple distinct modalities using machine learning techniques

Pradhan,

Shankar,

Barik

et al. 2024

IJPHS

View full text Add to dashboard Cite

Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.

show abstract

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Cited by 14 publications

References 84 publications

Analysis of the relationship between emotion intensity and electrophysiology parameters during a voice examination of opera singers

Analysis of the relationship between emotion intensity and electrophysiology parameters during a voice examination of opera singers

Real-Time Back Surface Landmark Determination Using a Time-of-Flight Camera

Evaluation of stress based on multiple distinct modalities using machine learning techniques

Contact Info

Product

Resources

About