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Hillen B, Andrés López D, Marzano-Felisatti JM, Sanchez-Jimenez JL, Cibrián Ortiz de Anda RM, Nägele M, Salvador-Palmer MR, Pérez-Soriano P, Schömer E, Simon P, Priego-Quesada JI. Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas. J Therm Biol 2023; 115:103605. [PMID: 37329763 DOI: 10.1016/j.jtherbio.2023.103605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/08/2023] [Accepted: 05/27/2023] [Indexed: 06/19/2023]
Abstract
This study aimed to examine the skin temperature (Tsk) variations in five regions of interest (ROI) to assess whether possible disparities between the ROI's Tsk could be associated with specific acute physiological responses during cycling. Seventeen participants performed a pyramidal load protocol on a cycling ergometer. We synchronously measured Tsk in five ROI with three infrared cameras. We assessed internal load, sweat rate, and core temperature. Reported perceived exertion and calves' Tsk showed the highest correlation (r = -0.588; p < 0.01). Mixed regression models revealed that the heart rate and reported perceived exertion were inversely related to calves' Tsk. The exercise duration was directly associated with the nose tip and calf Tsk but inversely related to the forehead and forearm Tsk. The sweat rate was directly related to forehead and forearm Tsk. The association of Tsk with thermoregulatory or exercise load parameters depends on the ROI. The parallel observation of the face and calf Tsk could indicate simultaneously the observation of acute thermoregulatory needs and individual internal load. The separate Tsk analyses of individual ROI appear more suitable to examine specific physiological response than a mean Tsk of several ROI during cycling.
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Affiliation(s)
- Barlo Hillen
- Department of Sports Medicine, Disease Prevention and Rehabilitation, Institute of Sports Science, Johannes Gutenberg University of Mainz, Germany.
| | - Daniel Andrés López
- Research Group of Computational Geometry, Institute of Computer Science, Johannes Gutenberg University of Mainz, Germany
| | - Joaquín Martín Marzano-Felisatti
- Department of Physical Education and Sports, Faculty of Physical Activity and Sports Sciences, GIBD (Research Group in Sports Biomechanics), University of Valencia, Spain
| | - José Luis Sanchez-Jimenez
- Department of Physical Education and Sports, Faculty of Physical Activity and Sports Sciences, GIBD (Research Group in Sports Biomechanics), University of Valencia, Spain
| | - Rosa Maria Cibrián Ortiz de Anda
- Department of Physiology. Faculty of Medicine and Odontology, GIFIME (Biophysics and Medical Physics Group), University of Valencia, Spain
| | | | - Maria Rosario Salvador-Palmer
- Department of Physiology. Faculty of Medicine and Odontology, GIFIME (Biophysics and Medical Physics Group), University of Valencia, Spain
| | - Pedro Pérez-Soriano
- Department of Physical Education and Sports, Faculty of Physical Activity and Sports Sciences, GIBD (Research Group in Sports Biomechanics), University of Valencia, Spain
| | - Elmar Schömer
- Research Group of Computational Geometry, Institute of Computer Science, Johannes Gutenberg University of Mainz, Germany
| | - Perikles Simon
- Department of Sports Medicine, Disease Prevention and Rehabilitation, Institute of Sports Science, Johannes Gutenberg University of Mainz, Germany
| | - Jose Ignacio Priego-Quesada
- Department of Physical Education and Sports, Faculty of Physical Activity and Sports Sciences, GIBD (Research Group in Sports Biomechanics), University of Valencia, Spain; Department of Physiology. Faculty of Medicine and Odontology, GIFIME (Biophysics and Medical Physics Group), University of Valencia, Spain.
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Perpetuini D, Formenti D, Priego-Quesada JI, Merla A. Editorial: Effect of neurophysiological conditions and mental workload on physical and cognitive performances: a multidimensional perspective. FRONTIERS IN NEUROERGONOMICS 2023; 4:1215376. [PMID: 38234485 PMCID: PMC10790942 DOI: 10.3389/fnrgo.2023.1215376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/05/2023] [Indexed: 01/19/2024]
Affiliation(s)
- David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, University of Studies G. d'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - Damiano Formenti
- Department of Biotechnology and Life Sciences, University of Insubria, Varese, Italy
| | - Jose Ignacio Priego-Quesada
- Department of Physical Education and Sports, Faculty of Physical Activity and Sport Sciences, Universitat de València, Valencia, Spain
| | - Arcangelo Merla
- Department of Engineering and Geology, G. d'Annunzio University of Chieti and Pescara, Pescara, Italy
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Perpetuini D, Formenti D, Cardone D, Trecroci A, Rossi A, Di Credico A, Merati G, Alberti G, Di Baldassarre A, Merla A. Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue? SENSORS (BASEL, SWITZERLAND) 2023; 23:832. [PMID: 36679631 PMCID: PMC9863897 DOI: 10.3390/s23020832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 for ARV, and r = 0.661, p < 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.
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Affiliation(s)
- David Perpetuini
- Department of Neurosciences, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Damiano Formenti
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy
| | - Daniela Cardone
- Department of Engineering and Geology, University “G. d’Annunzio” of Chieti-Pescara, 65127 Pescara, Italy
| | - Athos Trecroci
- Department of Biomedical Sciences for Health, University of Milan, 20129 Milan, Italy
| | - Alessio Rossi
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Andrea Di Credico
- Department of Medicine and Aging Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Giampiero Merati
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milano, Italy
| | | | - Angela Di Baldassarre
- Department of Medicine and Aging Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University “G. d’Annunzio” of Chieti-Pescara, 65127 Pescara, Italy
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Perpetuini D, Filippini C, Zito M, Cardone D, Merla A. Altered Microcirculation in Alzheimer’s Disease Assessed by Machine Learning Applied to Functional Thermal Imaging Data. Bioengineering (Basel) 2022; 9:bioengineering9100492. [PMID: 36290459 PMCID: PMC9598647 DOI: 10.3390/bioengineering9100492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer’s disease (AD) is characterized by progressive memory failures accompanied by microcirculation alterations. Particularly, impaired endothelial microvascular responsiveness and altered flow motion patterns have been observed in AD patients. Of note, the endothelium influences the vascular tone and also the small superficial blood vessels, which can be evaluated through infrared thermography (IRT). The advantage of IRT with respect to other techniques relies on its contactless features and its capability to preserve spatial information of the peripheral microcirculation. The aim of the study is to investigate peripheral microcirculation impairments in AD patients with respect to age-matched healthy controls (HCs) at resting state, through IRT and machine learning (ML) approaches. Particularly, several classifiers were tested, employing as regressors the power of the nose tip temperature time course in different physiological frequency bands. Among the ML classifiers tested, the Decision Tree Classifier (DTC) delivered the best cross-validated accuracy (accuracy = 82%) when discriminating between AD and HCs. The results further demonstrate the alteration of microvascular patterns in AD in the early stages of the pathology, and the capability of IRT to assess vascular impairments. These findings could be exploited in clinical practice, fostering the employment of IRT as a support for the early diagnosis of AD.
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Affiliation(s)
- David Perpetuini
- Department of Neuroscience and Imaging, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
- Correspondence: ; Tel.: +39-0871-3556954
| | - Chiara Filippini
- Department of Neuroscience and Imaging, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Michele Zito
- Department of Medicine and Science of Ageing, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Daniela Cardone
- Department of Engineering and Geology, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
- Next2U s.r.l., 65127 Pescara, Italy
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