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Yasar MN, Sica M, O'Flynn B, Tedesco S, Menolotto M. A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables. Sci Data 2024; 11:433. [PMID: 38678019 PMCID: PMC11055894 DOI: 10.1038/s41597-024-03254-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.
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Affiliation(s)
- Merve Nur Yasar
- Tyndall National Institute, University College Cork, Cork, T12 R5CP, Ireland.
| | - Marco Sica
- Tyndall National Institute, University College Cork, Cork, T12 R5CP, Ireland.
| | - Brendan O'Flynn
- Tyndall National Institute, University College Cork, Cork, T12 R5CP, Ireland
| | - Salvatore Tedesco
- Tyndall National Institute, University College Cork, Cork, T12 R5CP, Ireland
| | - Matteo Menolotto
- Tyndall National Institute, University College Cork, Cork, T12 R5CP, Ireland
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Aswar S, Yerrabandi V, Moncy MM, Boda SR, Jones J, Purkayastha S. Generalizability of Human Activity Recognition Machine Learning Models from non-Parkinson's to Parkinson's Disease Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082641 DOI: 10.1109/embc40787.2023.10340065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a personalized measure. Recent progress in automated Human Activity Recognition using machine learning (ML) models shows potential for better monitoring of PD patients. However, ML models must be calibrated to ignore tremors and accurately identify activity and its intensity. We report findings from a study where we trained ML models using data from medically validated triple synchronous sensors connected to 8 non-PD subjects performing 32 exercises. We then tested the models to identify exercises performed by 8 PD patients at different stages of the disease. Our analysis shows that better data preprocessing before modeling can provide some model generalizability. However, it is extremely challenging, as the models work with high accuracy on one group (Healthy or PD patients) (F1=0.88-0.94) but not on both groups.Clinical relevance-Patients with Parkinson's and other motor-generative diseases can now accurately measure physical activity with machine learning approaches. Clinicians, caregivers, and apps can make accurate, personalized exercise recommendations to augment medications that reduce tremors and stiffness.
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Bustos D, Cardoso R, Carvalho DD, Guedes J, Vaz M, Torres Costa J, Santos Baptista J, Fernandes RJ. Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115127. [PMID: 37299854 DOI: 10.3390/s23115127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.
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Affiliation(s)
- Denisse Bustos
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Ricardo Cardoso
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Diogo D Carvalho
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Joana Guedes
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Mário Vaz
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - José Torres Costa
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Santos Baptista
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Ricardo J Fernandes
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
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Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527765. [PMID: 36798422 PMCID: PMC9934625 DOI: 10.1101/2023.02.08.527765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Objective Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Method Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. Results The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. Conclusion and Significance This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.
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O’Keeffe R, Rathod V, Shirazi SY, Mehrdad S, Edwards A, Rao S, Atashzar SF. Linear versus Nonlinear Muscle Networks: A Case Study to Decode Hidden Synergistic Patterns During Dynamic Lower-limb Tasks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.15.524160. [PMID: 36711641 PMCID: PMC9882131 DOI: 10.1101/2023.01.15.524160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
This paper, for the first time, compares the behaviors of nonlinear versus linear muscle networks in decoding hidden peripheral synergistic neural patterns during dynamic functional tasks. In this paper, we report a case study during which one healthy subject conducts a series of four lower limb repetitive tasks. Specifically, the paper focuses on tasks that involve the right knee joint, including walking, sit-tostand, stepping, and drop-jump. Twelve muscles were recorded using the Delsys Trigno system. The linear muscle network was generated using coherence analysis, and the nonlinear network was generated using Spearman's correlation. The results show that the degree, clustering coefficient, and global efficiency of the muscle network have the highest value among tasks in the linear domain for the walking task, while a low linear synergistic network behavior for the sit-to-stand is observed. On the other hand, the results show that the nonlinear functional muscle network decodes high connectivity (degree) and clustering coefficient and efficiency for the sit-tostand when compared with other tasks. We have also developed a two-dimensional functional connectivity plane composed of linear and nonlinear features and shown that it can span the lower-limb dynamic task space. The results of this paper for the first time highlight the importance of observing both linear and nonlinear connectivity patterns, especially for complex dynamic tasks. It should also be noted that through a simultaneous EEG recording (using BrainVision System), we have shown that, indeed, cortical activity may indirectly explain highly-connected nonlinear muscle network for the sit-to-stand task, highlighting the importance of nonlinear muscle network as a neurophysiological window of observation beyond the periphery.
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A Pilot Study of Plantar Mechanics Distributions and Fatigue Profiles after Running on a Treadmill: Using a Support Vector Machine Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:7461729. [PMID: 36890878 PMCID: PMC9988392 DOI: 10.1155/2023/7461729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/12/2022] [Indexed: 02/25/2023]
Abstract
The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (T1) and second-fifth toes (T2-5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (M1). PP, PF, and PI at T1 and T2-5 were significantly higher in females than in males, and metatarsal 3-5 (M3-5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the T1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), T1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/T1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (T1 PP/HL PF, T1 PF/HL PF, and HL PF/T1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running.
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Bustos D, Cardoso F, Rios M, Vaz M, Guedes J, Torres Costa J, Santos Baptista J, Fernandes RJ. Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters. SENSORS (BASEL, SWITZERLAND) 2022; 23:194. [PMID: 36616791 PMCID: PMC9823590 DOI: 10.3390/s23010194] [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: 11/05/2022] [Revised: 12/13/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters' sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants' characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models' performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.
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Affiliation(s)
- Denisse Bustos
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Filipa Cardoso
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Manoel Rios
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Mário Vaz
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Joana Guedes
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - José Torres Costa
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Santos Baptista
- Associated Laboratory for Energy, Transports and Aeronautics, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Ricardo J. Fernandes
- Centre of Research, Education, Innovation and Intervention in Sport, CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
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De Lazzari N, Wichum F, Götte M, David C, Seid K, Tewes M. Entwicklung einer KI-gestützten Bewegungstherapie bei
onkologischen Palliativpatienten. B&G BEWEGUNGSTHERAPIE UND GESUNDHEITSSPORT 2022. [DOI: 10.1055/a-1909-5766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Zusammenfassung
HintergrundDie wechselnde Symptomlast ist eine große Hürde
in der Sporttherapie von onkologischen Palliativpatienten. Die täglich
variierende Symptomstärke erschwert die Einstellung einer optimalen
Trainingsbelastung und stellt neben der Motivation eine große Barriere
für die Teilnahme an bewegungstherapeutischen Interventionen dar. Ein
durch Künstliche Intelligenz (KI) gesteuertes Training könnte
helfen, die Trainingseinheiten individuell anzupassen und die Autonomie von
Palliativpatienten zu erhalten.
Methoden Fünf Patienten mit fortgeschrittener unheilbarer
Krebsdiagnose haben im Rahmen der Routineversorgung eine supervidierte
Bewegungstherapie absolviert. Dabei wurde ein Elektrokardiogramm über
einen Polar H10 Brustgurt aufgezeichnet und daraus kardiale und respiratorische
Vitalparameter extrahiert. Eine Klassifikation in drei Intensitätsstufen
über KI erfolgte anhand von neuronalen Netzen.
Ergebnisse Das KI-gesteuerte Training hat eine sehr hohe
Klassifikationsgüte (F1-Score: 0,95±0,05) durch die Vereinigung
von respiratorischen und kardialen Vitalparametern. Diese Kombination erzielt
genauere Klassifikationsergebnisse als die einzelnen Datensätze
für kardiale Parameter (0,93±0,06) und respiratorische Parameter
(0,72±0,06). Die Berücksichtigung einer Baselinemessung hat eine
positive Wirkung auf die Klassifikationsgenauigkeit.
Diskussion Diese Studie stellt die erste Untersuchung zum Einsatz von KI
zur Klassifizierung von trainingswissenschaftlichen Inhalten bei onkologischen
Palliativpatienten dar. Diese vulnerable Patientengruppe kann von einer
objektiven Erfassung des Belastungsniveaus anhand von Parametern des
kardiovaskulären Systems profitieren. Mit nur fünf Patienten
wird die Aussagekraft dieser explorativen Studie über Kreuzvalidierung
hergestellt. Zukünftig sollen weitere Parameter wie ein subjektives
Empfinden, Alter, Größe und Geschlecht die Klassifikation weiter
verbessern. In einem integrierten System ist eine individuelle
Trainingssteuerung in Echtzeit möglich.
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Affiliation(s)
- Nico De Lazzari
- Westdeutsches Tumorzentrum – Comprehensive Cancer Center,
Innere Klinik (Tumorforschung), Universitätsklinikum Essen, 45122 Essen,
Deutschland
| | - Felix Wichum
- Fraunhofer IMS, Universität Duisburg-Essen, 47057 Duisburg,
Deutschland
| | - Miriam Götte
- Westdeutsches Tumorzentrum – Comprehensive Cancer Center,
Klinik für Kinderheilkunde 3, Universitätsklinikum Essen, 45122
Essen, Deutschland
| | - Corinna David
- Fachhochschule Münster, Fachbereich
Physikingenieurwesen
| | - Karsten Seid
- Fraunhofer-Institut für Mikroelektronische Schaltungen und
Systeme (IMS), 47057 Duisburg und Fachgebiet Elektronische Bauelemente und
Schaltungen (EBS), Universität Duisburg-Essen
| | - Mitra Tewes
- Palliativmedizin der Universitätsmedizin Essen,
Universitätsklinikum Essen, 45122 Essen, Deutschland
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A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions. Artif Intell Med 2022; 130:102328. [DOI: 10.1016/j.artmed.2022.102328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 11/23/2022]
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Pinto-Bernal MJ, Cifuentes CA, Perdomo O, Rincón-Roncancio M, Múnera M. A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States. SENSORS (BASEL, SWITZERLAND) 2021; 21:6401. [PMID: 34640722 PMCID: PMC8513020 DOI: 10.3390/s21196401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/03/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023]
Abstract
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients' intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual's characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.
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Affiliation(s)
- Maria J. Pinto-Bernal
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
| | - Carlos A. Cifuentes
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
| | - Oscar Perdomo
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111711, Colombia;
| | | | - Marcela Múnera
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia; (M.J.P.-B.); (M.M.)
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