A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

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 reha...

Full description

Autores:: Pinto Bernal, Marìa J.
Cifuentes, Carlos A.
Perdomo, Oscar
Rincón Roncancio, Mónica
Múnera, Marcela

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: eng

id	ESCUELAIG2_8f7d91aa6029baa1839180bca8f03270
oai_identifier_str	oai:repositorio.escuelaing.edu.co:001/3229
network_acronym_str	ESCUELAIG2
network_name_str	Repositorio Institucional ECI
repository_id_str
dc.title.eng.fl_str_mv	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
title	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
spellingShingle	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States Análisis de datos fácticos Factual data analysis Fatiga Fatigue Programas de rehabilitación Rehabilitation programs Aprendizaje automático (Inteligencia artificial) Machine learning fatigue diagnosis classification models Inertial measurement units EMG Physical exercise Diagnóstico de fatiga Modelos de clasificación Unidades de medida inerciales Ejercicio fisico
title_short	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
title_full	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
title_fullStr	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
title_full_unstemmed	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
title_sort	A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
dc.creator.fl_str_mv	Pinto Bernal, Marìa J. Cifuentes, Carlos A. Perdomo, Oscar Rincón Roncancio, Mónica Múnera, Marcela
dc.contributor.author.none.fl_str_mv	Pinto Bernal, Marìa J. Cifuentes, Carlos A. Perdomo, Oscar Rincón Roncancio, Mónica Múnera, Marcela
dc.contributor.researchgroup.spa.fl_str_mv	GiBiome
dc.subject.armarc.none.fl_str_mv	Análisis de datos fácticos Factual data analysis Fatiga Fatigue Programas de rehabilitación Rehabilitation programs Aprendizaje automático (Inteligencia artificial) Machine learning
topic	Análisis de datos fácticos Factual data analysis Fatiga Fatigue Programas de rehabilitación Rehabilitation programs Aprendizaje automático (Inteligencia artificial) Machine learning fatigue diagnosis classification models Inertial measurement units EMG Physical exercise Diagnóstico de fatiga Modelos de clasificación Unidades de medida inerciales Ejercicio fisico
dc.subject.proposal.eng.fl_str_mv	fatigue diagnosis classification models Inertial measurement units EMG Physical exercise
dc.subject.proposal.spa.fl_str_mv	Diagnóstico de fatiga Modelos de clasificación Unidades de medida inerciales Ejercicio fisico
description	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%.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2024-08-28T20:49:05Z
dc.date.available.none.fl_str_mv	2024-08-28T20:49:05Z
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/article
format	http://purl.org/coar/resource_type/c_6501
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.escuelaing.edu.co/handle/001/3229
dc.identifier.eissn.spa.fl_str_mv	1424-8220
dc.identifier.instname.spa.fl_str_mv	Universidad Escuela Colombiana de Ingeniería
dc.identifier.reponame.spa.fl_str_mv	Repositorio Digital
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.escuelaing.edu.co/
url	https://repositorio.escuelaing.edu.co/handle/001/3229 https://repositorio.escuelaing.edu.co/
identifier_str_mv	1424-8220 Universidad Escuela Colombiana de Ingeniería Repositorio Digital
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.citationedition.spa.fl_str_mv	2021, 21, 6401
dc.relation.citationendpage.spa.fl_str_mv	25
dc.relation.citationissue.spa.fl_str_mv	6401
dc.relation.citationstartpage.spa.fl_str_mv	2
dc.relation.citationvolume.spa.fl_str_mv	21
dc.relation.ispartofjournal.eng.fl_str_mv	Sensors
dc.relation.references.spa.fl_str_mv	Salakari, M.R.; Surakka, T.; Nurminen, R.; Pylkkänen, L. Effects of rehabilitation among patients with advances cancer: A systematic review. Acta Oncol. 2015, 54, 618–628. [CrossRef] Zanuso, S.; Balducci, S.; Jimenez, A. Physical activity, a key factor to quality of life in type 2 diabetic patients. Diabetes/Metab. Res. Rev. 2009, 25, S24–S28. [CrossRef] Zanuso, S.; Jimenez, A.; Pugliese, G.; Corigliano, G.; Balducci, S. Exercise for the management of type 2 diabetes: A review of the evidence. Acta Diabetol. 2010, 47, 15–22. [CrossRef] [PubMed] Warburton, D.E.; Nicol, C.W.; Bredin, S.S. Health benefits of physical activity: The evidence. CMAJ 2006, 174, 801–809. [CrossRef] [PubMed] Bauman, A.E. Updating the evidence that physical activity is good for health: An epidemiological review 2000–2003. J. Sci. Med. Sport 2004, 7, 6–19. [CrossRef] Oguma, Y.; Shinoda-Tagawa, T. Physical activity decreases cardiovascular disease risk in women: review and meta-analysis. Am. J. Prev. Med. 2004, 26, 407–418. [CrossRef] [PubMed] Vuori, I. Physical inactivity is a cause and physical activity is a remedy for major public health problems. Kinesiology 2004, 36, 123–153 Haskell, W.L.; Lee, I.M.; Pate, R.R.; Powell, K.E.; Blair, S.N.; Franklin, B.A.; Macera, C.A.; Heath, G.W.; Thompson, P.D.; Bauman, A. Physical Activity and Public Health. Med. Sci. Sport. Exerc. 2007, 39, 1423–1434. [CrossRef] Pinto-Bernal, M.J.; Aguirre, A.; Cifuentes, C.A.; Munera, M. Wearable Sensors for Monitoring Exercise and Fatigue Estimation in Rehabilitation. In Internet of Medical Things; CRC Press: Boca Raton, FL, USA, 2021; pp. 83–110. Kristensen, T.; Kornitzer, M.; Alfredsson, L.; Marmot, M.; Logstrup, S.; Williams, C. Social Factors, Work, Stress and Cardiovascular Disease Prevention in the European Union; European Heart Network: Brussels, Belgium, 1998 Priest, N.; Armstrong, R.; Doyle, J.; Waters, E. Interventions implemented through sporting organisations for increasing participation in sport. Cochrane Database Syst. Rev. 2008, 18, CD004812. [CrossRef] Livingstone, M.; Robson, P.; Wallace, J.; McKinley, M. How active are we? Levels of routine physical activity in children and adults. Proc. Nutr. Soc. 2003, 62, 681–701. [CrossRef] Pollock, M.L.; Gaesser, G.A.; Butcher, J.D.; Després, J.P.; Dishman, R.K.; Franklin, B.A.; Garber, C.E. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Schweiz. Z. Sportmed. 1998, 41, 127–137. [CrossRef] [PubMed] Andersen, L.B.; Schnohr, P.; Schroll, M.; Hein, H.O. All-Cause Mortality Associated with Physical Activity during Leisure Time, Work, Sports, and Cycling to Work. Arch. Intern. Med. 2000, 160, 1621–1628. [CrossRef] [PubMed] Schnohr, P.; Marott, J.L.; Jensen, J.S.; Jensen, G.B. Intensity versus duration of cycling, impact on all-cause and coronary heart disease mortality: The Copenhagen City Heart Study. Eur. J. Prev. Cardiol. 2012, 19, 73–80. [CrossRef] [PubMed] Warburton, D.E. Prescribing exercise as preventive therapy. Can. Med. Assoc. J. 2006, 174, 961–974. [CrossRef] [PubMed] Cup, E.H.; Pieterse, A.J.; ten Broek-Pastoor, J.M.; Munneke, M.; van Engelen, B.G.; Hendricks, H.T.; van der Wilt, G.J.; Oostendorp, R.A. Exercise Therapy and Other Types of Physical Therapy for Patients With Neuromuscular Diseases: A Systematic Review. Arch. Phys. Med. Rehabil. 2007, 88, 1452–1464. [CrossRef] [PubMed] Manley, A.F. Physical Activity and Health: A Report of the Surgeon General; Diane Publishing: Darby, PA, USA, 1996 Lee, I.M.; Sesso, H.D.; Oguma, Y.; Paffenbarger, R.S. Relative intensity of physical activity and risk of coronary heart disease. Circulation 2003, 107, 1110–1116. [CrossRef] American College of Sports Medicine. ACSM’s Health-Related Physical Fitness Assessment Manual; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2013. Balducci, S.; Sacchetti, M.; Haxhi, J.; Orlando, G.; D’Errico, V.; Fallucca, S.; Menini, S.; Pugliese, G. Physical exercise as therapy for type 2 diabetes mellitus. Diabetes/Metab. Res. Rev. 2014, 30, 13–23. [CrossRef] Dun, Y.; Smith, J.R.; Liu, S.; Olson, T.P. High-Intensity Interval Training in Cardiac Rehabilitation. Sports Med. 2019, 42, 587–605. [CrossRef] Tanasescu, M.; Leitzmann, M.F.; Rimm, E.B.; Willett, W.C.; Stampfer, M.J.; Hu, F.B. Exercise type and intensity in relation to coronary heart disease in men. J. Am. Med. Assoc. 2002, 288, 1994–2000. [CrossRef] Oldervoll, L.; Kaasa, S.; Hjermstad, M.; Lund, J.; Loge, J. Physical exercise results in the improved subjective well-being of a few or is effective rehabilitation for all cancer patients? Eur. J. Cancer 2004, 40, 951–962. [CrossRef] Fleig, L.; Lippke, S.; Pomp, S.; Schwarzer, R. Exercise maintenance after rehabilitation: How experience can make a difference. Psychol. Sport Exerc. 2011, 12, 293–299. [CrossRef] Göhner, W.; Seelig, H.; Fuchs, R. Intervention effects on cognitive antecedents of physical exercise: A 1-year follow-up study. Appl. Psychol. Health Well-Being 2009, 1, 233–256. [CrossRef] Abd-Elfattah, H.M.; Abdelazeim, F.H.; Elshennawy, S. Physical and cognitive consequences of fatigue: A review. J. Adv. Res. 2015, 6, 351–358. [CrossRef] Baussard, L.; Carayol, M.; Porro, B.; Baguet, F.; Cousson-gelie, F. European Journal of Oncology Nursing Fatigue in cancer patients : Development and validation of a short form of the Multidimensional Fatigue Inventory ( MFI-10 ). Eur. J. Oncol. Nurs. 2018, 36, 62–67. [CrossRef] [PubMed] Alghannam, A.F.; Tsintzas, K.; Thompson, D.; Bilzon, J.; Betts, J.A. Exploring mechanisms of fatigue during repeated exercise and the dose dependent effects of carbohydrate and protein ingestion: Study protocol for a randomised controlled trial. Trials 2014, 15, 95. [CrossRef] Ozalp, O.; Inal-Ince, D.; Calik, E.; Vardar-Yagli, N.; Saglam, M.; Savci, S.; Arikan, H.; Bosnak-Guclu, M.; Coplu, L. Extrapulmonary features of bronchiectasis: Muscle function, exercise capacity, fatigue, and health status. Multidiscip. Respir. Med. 2012, 7, 3. [CrossRef] Lu, L.; Megahed, F.M.; Sesek, R.F.; Cavuoto, L.A. A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among US manufacturing workers. Appl. Ergon. 2017, 65, 139–151. [CrossRef] Zamunér, A.R.; Moreno, M.A.; Camargo, T.M.; Graetz, J.P.; Rebelo, A.C.; Tamburús, N.Y.; da Silva, E. Assessment of subjective perceived exertion at the anaerobic threshold with the Borg CR-10 scale. J. Sport. Sci. Med. 2011, 10, 130–136 Curt, G.A.; Breitbart, W.; Cella, D.; Groopman, J.E.; Horning, S.J.; Itri, L.M.; Johnson, D.H.; Miaskowski, C.; Scherr, S.L.; Portenoy, R.K.; et al. Impact of cancer-related fatigue on the lives of patients: New findings from the Fatigue Coalition. Oncologist 2000, 5, 353–360. [CrossRef] [PubMed] Annett, J. Subjective rating scales: Science or art? Ergonomics 2002, 45, 966–987. [CrossRef] [PubMed] Williams, N. The Borg rating of perceived exertion (RPE) scale. Occup. Med. 2017, 67, 404–405. [CrossRef] Borg, G. Borg’s range model and scales. Int. J. Sport Psychol. 2001, 32, 110-126. Sehle, A.; Vieten, M.; Sailer, S.; Mündermann, A.; Dettmers, C. Objective assessment of motor fatigue in multiple sclerosis: The Fatigue index Kliniken Schmieder (FKS). J. Neurol. 2014, 261, 1752–1762. [CrossRef] [PubMed] Maman, Z.S.; Chen, Y.J.; Baghdadi, A.; Lombardo, S.; Cavuoto, L.A.; Megahed, F.M. A data analytic framework for physical fatigue management using wearable sensors. Expert Syst. Appl. 2020, 155, 113405. [CrossRef] Qi, J.; Yang, P.; Waraich, A.; Deng, Z.; Zhao, Y.; Yang, Y. Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review. J. Biomed. Inform. 2018, 87, 138–153. [CrossRef] Zeni, A.I.; Hoffman, M.D.; Clifford, P.S. Relationships among heart rate, lactate concentration, and perceived effort for different types of rhythmic exercise in women. Arch. Phys. Med. Rehabil. 1996, 77, 237–241. [CrossRef] Poole, D.C.; Burnley, M.; Vanhatalo, A.; Rossiter, H.B.; Jones, A.M. Critical power: An important fatigue threshold in exercise physiology. Med. Sci. Sport. Exerc. 2016, 48, 2320–2334. [CrossRef] Pettersson, S.; Lundberg, I.; Liang, M.; Pouchot, J.; Welin Henriksson, E. Determination of the minimal clinically important difference for seven measures of fatigue in Swedish patients with systemic lupus erythematosus. Scand. J. Rheumatol. 2015, 44, 206–210. [CrossRef] Yu, F.; Bilberg, A.; Stenager, E.; Rabotti, C.; Zhang, B.; Mischi, M. A wireless body measurement system to study fatigue in multiple sclerosis. Physiol. Meas. 2012, 33, 2033–2048. [CrossRef] Möhler, F.; Ringhof, S.; Debertin, D.; Stein, T. Influence of fatigue on running coordination: A UCM analysis with a geometric 2D model and a subject-specific anthropometric 3D model. Hum. Mov. Sci. 2019, 66, 133–141. [CrossRef] Kang, S.R.; Min, J.Y.; Yu, C.; Kwon, T.K. Effect of whole body vibration on lactate level recovery and heart rate recovery in rest after intense exercise. Technol. Health Care 2017, 25, 115–123. [CrossRef] [PubMed] Glynn, A.J.; Fiddler, H. The Physiotherapist’s Pocket Guide to Exercise E-Book: Assessment, Prescription and Training; Elsevier Health Sciences: Amsterdam, The Netherlands, 2009. Aubert, A.E.; Seps, B.; Beckers, F. Heart rate variability in athletes. Sport. Med. 2003, 33, 889–919. [CrossRef] Achten, J.; Jeukendrup, A.E. Heart rate monitoring. Sport. Med. 2003, 33, 517–538. [CrossRef] da Cunha, F.A.; Farinatti, P.d.T.V.; Midgley, A.W. Methodological and practical application issues in exercise prescription using the heart rate reserve and oxygen uptake reserve methods. J. Sci. Med. Sport. 2011, 14, 46–57. [CrossRef] Goodwin, M.L.; Harris, J.E.; Hernández, A.; Gladden, L.B. Blood lactate measurements and analysis during exercise: A guide for clinicians. J. Diabetes Sci. Technol. 2007, 1, 558–569. [CrossRef] Jansen, T.C.; van Bommel, J.; Bakker, J. Blood lactate monitoring in critically ill patients: A systematic health technology assessment. Crit. Care Med. 2009, 37, 2827–2839 Saey, D.; Michaud, A.; Couillard, A.; Côté, C.H.; Mador, M.J.; LeBlanc, P.; Jobin, J.; Maltais, F. Contractile fatigue, muscle morphometry, and blood lactate in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2005, 171, 1109–1115. [CrossRef] Helbostad, J.L.; Sturnieks, D.L.; Menant, J.; Delbaere, K.; Lord, S.R.; Pijnappels, M. Consequences of lower extremity and trunk muscle fatigue on balance and functional tasks in older people: A systematic literature review. BMC Geriatr. 2010, 10, 56. [CrossRef] Wan, J.-J.; Qin, Z.; Wang, P.-Y.; Sun, Y.; Liu, X. Muscle fatigue: General understanding and treatment. Exp. Mol. Med. 2017, 49, e384. [CrossRef] Karthick, P.A.; Ghosh, D.M.; Ramakrishnan, S. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput. Methods Programs Biomed. 2018, 154, 45–56. [CrossRef] [PubMed] Subasi, A.; Kiymik, M.K. Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks. J. Med. Syst. 2010, 34, 777–785. [CrossRef] [PubMed] Al-Mulla, M.R.; Sepulveda, F.; Colley, M. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors 2011, 11, 3545–3594. [CrossRef] Camomilla, V.; Bergamini, E.; Fantozzi, S.; Vannozzi, G. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review. Sensors 2018, 18, 873. [CrossRef] [PubMed] Ejupi, A.; Gschwind, Y.J.; Valenzuela, T.; Lord, S.R.; Delbaere, K. A Kinect and Inertial Sensor-Based System for the Self-Assessment of Fall Risk: A Home-Based Study in Older People. Hum.-Comput. Interact. 2016, 31, 261–293. [CrossRef] Manchola, S.; Bernal, P.; Munera, M.; Cifuentes, C.A. Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals. Sensors 2019, 19, 2988. [CrossRef] [PubMed] Aguirre, A.; Casas, J.; Céspedes, N.; Múnera, M.; Rincon-Roncancio, M.; Cuesta-Vargas, A.; Cifuentes, C.A. Feasibility study: Towards Estimation of Fatigue Level in Robot-Assisted Exercise for Cardiac Rehabilitation. In Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada, 24–28 June 2019; pp. 911–916 Céspedes, N.; Múnera, M.; Gómez, C.; Cifuentes, C.A. Social Human-Robot Interaction for Gait Rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 1299–1307. [CrossRef] Segala, D.B.; Chelidze, D.; Adams, A.; Schiffman, J.M.; Hasselquist, L. Tracking Physiological Fatigue in Prolonged Load Carriage Walking Using Phase Space Warping and Smooth Orthogonal Decomposition. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Boston, MA, USA, 31 October–6 November 2008; Volume 48630, pp. 323–331. 64. Mugnosso, M.; Marini, F.; Holmes, M.; Morasso, P.; Zenzeri, J. Muscle fatigue assessment during robot-mediated movements. J. Neuroeng. Rehabil. 2018, 15, 1–14. [CrossRef] Chan, V.C.; Beaudette, S.M.; Smale, K.B.; Beange, K.H.; Graham, R.B. A subject-specific approach to detect fatigue-related changes in spine motion using wearable sensors. Sensors 2020, 20, 2646. [CrossRef] Ross, L.M.; Porter, R.R.; Durstine, J.L. High-intensity interval training (HIIT) for patients with chronic diseases. J. Sport Health Sci. 2016, 5, 139–144. [CrossRef] García-López, J.; Morante, J.C.; Ogueta-Alday, A.; Rodríguez-Marroyo, J.A. The Type Of Mat (Contact vs. Photocell) Affects Vertical Jump Height Estimated From Flight Time. J. Strength Cond. Res. 2013, 27, 1162–1167. [CrossRef] Aguirre, A.; Pinto, M.J.; Cifuentes, C.A.; Perdomo, O.; Díaz, C.A.; Múnera, M. Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise. Sensors 2021, 21, 5006. [CrossRef] Zhang, J.; Lockhart, T.E.; Soangra, R. Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Ann. Biomed. Eng. 2014, 42, 600–612. [CrossRef] [PubMed] Karg, M.; Venture, G.; Hoey, J.; Kuli´c, D. Human movement analysis as a measure for fatigue: A hidden Markov-based approach. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 470–481. [CrossRef] [PubMed] Karg, M.; Kühnlenz, K.; Buss, M.; Seiberl, W.; Tusker, F.; Schmeelk, M.; Schwirtz, A. Expression and automatic recognition of exhaustion in natural walking. In Proceedings of the IADIS Interfaces and Human Computer Interaction (IHCI), Amsterdam, The Netherlands, 25–27 July 2008; pp. 165–172. Kavanagh, J.J.; Morrison, S.; Barrett, R.S. Lumbar and cervical erector spinae fatigue elicit compensatory postural responses to assist in maintaining head stability during walking. J. Appl. Physiol. 2006, 101, 1118–1126. [CrossRef] Yoshino, K.; Motoshige, T.; Araki, T.; Matsuoka, K. Effect of prolonged free-walking fatigue on gait and physiological rhythm. J. Biomech. 2004, 37, 1271–1280. [CrossRef] [PubMed] Maman, Z.S.; Yazdi, M.A.A.; Cavuoto, L.A.; Megahed, F.M. A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. Appl. Ergon. 2017, 65, 515–529. [CrossRef] [PubMed] Lee, M.; Roan, M.; Smith, B.; Lockhart, T.E. Gait analysis to classify external load conditions using linear discriminant analysis. Hum. Mov. Sci. 2009, 28, 226–235. [CrossRef] Helbostad, J.L.; Leirfall, S.; Moe-Nilssen, R.; Sletvold, O. Physical fatigue affects gait characteristics in older persons. J. Gerontol. Ser. Biol. Sci. Med Sci. 2007, 62, 1010–1015. [CrossRef] Winter, D.A. Human balance and posture control during standing and walking. Gait Posture 1995, 3, 193–214. [CrossRef] Warburton, D.E.; Gledhill, N.; Quinney, A. Musculoskeletal fitness and health. Can. J. Appl. Physiol. 2001, 26, 217–237. [CrossRef] Swift-Spong, K.; Short, E.; Wade, E.; Matari´c, M.J. Effects of comparative feedback from a socially assistive robot on self-efficacy in post-stroke rehabilitation. In Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, 11–14 August 2015; pp. 764–769. Fasola, J.; Matari´c, M.J. A socially assistive robot exercise coach for the elderly. J. Hum.-Robot Interact. 2013, 2, 3–32. [CrossRef] Casas, J.; Senft, E.; Gutierrez, L.F.; Rincon-Rocancio, M.; Munera, M.; Belpaeme, T.; Cifuentes, C.A. Social assistive robots: Assessing the impact of a training assistant robot in cardiac rehabilitation. Int. J. Soc. Robot. 2020, 1–15. [CrossRef] Cifuentes, C.A.; Pinto, M.J.; Céspedes, N.; Múnera, M. Social robots in therapy and care. Curr. Robot. Rep. 2020, 1, 59–74. [CrossRef] Céspedes Gómez, N.; Irfan, B.; Senft, E.; Cifuentes, C.A.; Gutierrez, L.F.; Rincon-Roncancio, M.; Belpaeme, T.; Munera, M. A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World. Front. Neurorobot. 2021, 15, 21. Gockley, R.; Bruce, A.; Forlizzi, J.; Michalowski, M.; Mundell, A.; Rosenthal, S.; Sellner, B.; Simmons, R.; Snipes, K.; Schultz, A.C.; et al. Designing robots for long-term social interaction. In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, 2–6 August 2005; pp. 1338–1343. Gockley, R.; MatariC, M.J. Encouraging physical therapy compliance with a hands-off mobile robot. In Proceedings of the 1st ´ ACM SIGCHI/SIGART Conference on Human–Robot Interaction, Salt Lake City, UT, USA, 2–3 March 2006; pp. 150–155 Matari´c, M.J.; Eriksson, J.; Feil-Seifer, D.J.; Winstein, C.J. Socially assistive robotics for post-stroke rehabilitation. J. Neuroeng. Rehabil. 2007, 4, 1–9. [CrossRef] [PubMed] Smets, E.M.; Garssen, B.; Bonke, B.; De Haes, J.C. The multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J. Psychosom. Res. 1995, 39, 315–325. [CrossRef] Kakria, P.; Tripathi, N.; Kitipawang, P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int. J. Telemed. Appl. 2015, 2015, 373474. [CrossRef] Moohialdin, A.S.; Suhariadi, B.T.; Siddiqui, M.K. Practical validation measurements of a physiological status monitoring sensor in real construction activities. In Proceedings of the Streamlining Information Transfer between Construction and Structural Engineering, Brisbane, Australia, 3–5 December 2018. Swain, D.P.; Brawner, C.A.; American College of Sports Medicine. ACSM’s Resource Manual for Guidelines for Exercise Testing and Prescription; Wolters Kluwer Health/Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2014. Taborri, J.; Rossi, S.; Palermo, E.; Patanè, F.; Cappa, P. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network. Sensors 2014, 14, 16212–16234. [CrossRef] [PubMed] Sabatini, A.; Martelloni, C.; Scapellato, S.; Cavallo, F. Assessment of Walking Features From Foot Inertial Sensing. IEEE Trans. Biomed. Eng. 2005, 52, 486–494. [CrossRef] [PubMed] Kotiadis, D.; Hermens, H.; Veltink, P. Inertial Gait Phase Detection for control of a drop foot stimulator. Med. Eng. Phys. 2010, 32, 287–297. [CrossRef] Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Proceedings of the International Conference on Pervasive Computing, Vienna, Austria, 21–23 April 2004; Springer: Berlin/Heidelberg, Germany, 2004. Pirttikangas, S.; Fujinami, K.; Nakajima, T. Feature selection and activity recognition from wearable sensors. In International Symposium on Ubiquitious Computing Systems; Springer: Berlin/Heidelberg, Germany, 2006; pp. 516–527. Reaz, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. Techniques of EMG signal analysis: Detection, processing, classification and applications. Biol. Proced. Online 2006, 8, 11–35. [CrossRef] [PubMed] Wojtys, E.M.; Wylie, B.B.; Huston, L.J. The effects of muscle fatigue on neuromuscular function and anterior tibial translation in healthy knees. Am. J. Sport. Med. 1996, 24, 615–621. [CrossRef] [PubMed] Kern, N.; Schiele, B.; Schmidt, A. Multi-sensor activity context detection for wearable computing. In European Symposium on Ambient Intelligence; Springer: Berlin/Heidelberg, Germany, 2003; pp. 220–232. Marras, W.S.; Lavender, S.A.; Leurgans, S.E.; Rajulu, S.L.; Allread, S.W.G.; Fathallah, F.A.; Ferguson, S.A. The role of dynamic three-dimensional trunk motion in occupationally-related. Spine 1993, 18, 617–628. [CrossRef] [PubMed] Huynh, T.; Schiele, B. Analyzing features for activity recognition. In Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, Grenoble, France, 12–14 October 2005; pp. 159–163. Heinz, E.A.; Kunze, K.S.; Sulistyo, S.; Junker, H.; Lukowicz, P.; Tröster, G. Experimental evaluation of variations in primary features used for accelerometric context recognition In European Symposium on Ambient Intelligence; Springer: Berlin/Heidelberg, Germany, 2003; pp. 252–263. Krause, A.; Siewiorek, D.P.; Smailagic, A.; Farringdon, J. Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing. ISWC 2003, 3, 88. Lee, S.W.; Mase, K. Activity and location recognition using wearable sensors. IEEE Pervasive Comput. 2002, 1, 24–32. Lessley, D.; Crandall, J.; Shaw, G.; Kent, R.; Funk, J. A Normalization Technique for Developing Corridors from Individual Subject Responses; Technical Report; SAE Technical Paper: Detroit, MI, USA, 2004 Moorhouse, K. An improved normalization methodology for developing mean human response curves. In Proceedings of the International Technical Conference on the Enhanced Safety of Vehicles, Seoul, Korea, 27–30 May 2013. Yoganandan, N.; Arun, M.W.; Pintar, F.A. Normalizing and scaling of data to derive human response corridors from impact tests. J. Biomech. 2014, 47, 1749–1756. [CrossRef Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI, Montreal, QC, Canada, 20–25 August 1995; Volume 14, pp. 1137–1145 Jain, A.K.; Duin, R.P.W.; Mao, J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 4–37. [CrossRef] Liu, H.; Cocea, M. Semi-random partitioning of data into training and test sets in granular computing context. Granul. Comput. 2017, 2, 357–386. [CrossRef] Browne, M.W. Cross-validation methods. J. Math. Psychol. 2000, 44, 108–132. [CrossRef] Dag, A.; Topuz, K.; Oztekin, A.; Bulur, S.; Megahed, F.M. A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decis. Support Syst. 2016, 86, 1–12. [CrossRef] Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. Han, J.; Pei, J.; Kamber, M. Data Mining: Concepts and Techniques; Elsevier: Amsterdam, The Netherlands, 2011. James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning; Springer: Berlin/Heidelberg, Germany, 2013; Volume 112. Kuhn, M.; Johnson, K. Applied Predictive Modeling; Springer: Berlin/Heidelberg, Germany, 2013; Volume 26 Fernández, A.; García, S.; Galar, M.; Prati, R.C.; Krawczyk, B.; Herrera, F. Learning from Imbalanced Data Sets; Springer: Berlin/Heidelberg, Germany, 2018; Volume 10. Krawczyk, B. Learning from imbalanced data: Open challenges and future directions. Prog. Artif. Intell. 2016, 5, 221–232. [CrossRef] Skiena, S.S. The Data Science Design Manual; Springer: Berlin/Heidelberg, Germany, 2017. Khalid, S.; Khalil, T.; Nasreen, S. A survey of feature selection and feature extraction techniques in machine learning. In Proceedings of the 2014 Science and Information Conference, London, UK, 27–29 August 2014; pp. 372–378. Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M.L. Activity Recognition from Accelerometer Data; AAAI: Pittsburgh, PA, USA, 2005; Volume 5, pp. 1541–1546. Casas, J.; Irfan, B.; Senft, E.; Gutiérrez, L.; Rincon-Roncancio, M.; Munera, M.; Belpaeme, T.; Cifuentes, C.A. Social Assistive Robot for Cardiac Rehabilitation: A Pilot Study with Patients with Angioplasty. In Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI’18, Chicago, IL, USA, 5–8 March 2018; pp. 79–80.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_14cb
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/closedAccess
eu_rights_str_mv	closedAccess
rights_invalid_str_mv	http://purl.org/coar/access_right/c_14cb
dc.format.extent.spa.fl_str_mv	25 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	MDPI (Multidisciplinary Digital Publishing Institute)
dc.publisher.place.spa.fl_str_mv	Suiza
dc.source.spa.fl_str_mv	https://www.mdpi.com/
institution	Escuela Colombiana de Ingeniería Julio Garavito
bitstream.url.fl_str_mv	https://repositorio.escuelaing.edu.co/bitstream/001/3229/4/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf.txt https://repositorio.escuelaing.edu.co/bitstream/001/3229/3/Portada%20A%20data-driven%20approach%20to%20physical%20fatigue%20management.PNG https://repositorio.escuelaing.edu.co/bitstream/001/3229/5/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf.jpg https://repositorio.escuelaing.edu.co/bitstream/001/3229/2/license.txt https://repositorio.escuelaing.edu.co/bitstream/001/3229/1/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf
bitstream.checksum.fl_str_mv	425fb4dd7699218476f9018924e83a85 62f9593f531f626d007a3c2b1e1b80f6 647ab2e00d221c775dba4f9512ad4fd6 5a7ca94c2e5326ee169f979d71d0f06e 3e357ec5b436277213c51afc75e917eb
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Escuela Colombiana de Ingeniería Julio Garavito
repository.mail.fl_str_mv	repositorio.eci@escuelaing.edu.co
_version_	1814355635942195200
spelling	Pinto Bernal, Marìa J.7036c7a55946d354b3bca3063bdab1fbCifuentes, Carlos A.0b885a45437175ae12e5d0a6f598afc4Perdomo, Oscarff88a6a3395dc44ade411d38bf28c565Rincón Roncancio, Mónica0c0bbc94eb026b9dd7d325de466d7d8aMúnera, Marcela8047a30ff2499f8ae5a4e903871b8f95GiBiome2024-08-28T20:49:05Z2024-08-28T20:49:05Z2021https://repositorio.escuelaing.edu.co/handle/001/32291424-8220Universidad Escuela Colombiana de IngenieríaRepositorio Digitalhttps://repositorio.escuelaing.edu.co/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%.El ejercicio físico contribuye al éxito de los programas de rehabilitación y rehabilitación Procesos asistidos a través de robots sociales. Sin embargo, la cantidad e intensidad del ejercicio necesario para obtener resultados positivos se desconocen. Se deben tener en cuenta varias consideraciones para su implementación en rehabilitación, como el seguimiento de la intensidad de los pacientes, fundamental para evitar situaciones extremas. Las condiciones de fatiga pueden causar complicaciones físicas y fisiológicas. El uso del aprendizaje automático Se han implementado modelos en la gestión de la fatiga, pero en la práctica están limitados debido a la falta de comprender cómo el desempeño de un individuo se deteriora con la fatiga; esto puede variar dependiendo de ejercicio físico, entorno y características del individuo. Como primer paso, este documento establece la base para un enfoque analítico de datos para gestionar la fatiga en las tareas de caminar. la propuesta El marco establece los criterios para la selección de una característica y un algoritmo de aprendizaje automático para la fatiga. manejo, clasificando cuatro estados diagnósticos de fatiga. A partir del marco propuesto y de la clasificador implementado, el modelo de bosque aleatorio presentó el mejor desempeño con un promedio precisión de ≥98% y puntuación F de ≥93%. Este modelo estaba compuesto por ≤16 características. Además, el El rendimiento de la predicción se analizó limitando los sensores utilizados de cuatro IMU a dos o incluso una IMU con una eficiencia general de ≥88%.25 páginasapplication/pdfengMDPI (Multidisciplinary Digital Publishing Institute)Suizahttps://www.mdpi.com/A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue StatesArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a852021, 21, 6401256401221SensorsSalakari, M.R.; Surakka, T.; Nurminen, R.; Pylkkänen, L. Effects of rehabilitation among patients with advances cancer: A systematic review. Acta Oncol. 2015, 54, 618–628. [CrossRef]Zanuso, S.; Balducci, S.; Jimenez, A. Physical activity, a key factor to quality of life in type 2 diabetic patients. Diabetes/Metab. Res. Rev. 2009, 25, S24–S28. [CrossRef]Zanuso, S.; Jimenez, A.; Pugliese, G.; Corigliano, G.; Balducci, S. Exercise for the management of type 2 diabetes: A review of the evidence. Acta Diabetol. 2010, 47, 15–22. [CrossRef] [PubMed]Warburton, D.E.; Nicol, C.W.; Bredin, S.S. Health benefits of physical activity: The evidence. CMAJ 2006, 174, 801–809. [CrossRef] [PubMed]Bauman, A.E. Updating the evidence that physical activity is good for health: An epidemiological review 2000–2003. J. Sci. Med. Sport 2004, 7, 6–19. [CrossRef]Oguma, Y.; Shinoda-Tagawa, T. Physical activity decreases cardiovascular disease risk in women: review and meta-analysis. Am. J. Prev. Med. 2004, 26, 407–418. [CrossRef] [PubMed]Vuori, I. Physical inactivity is a cause and physical activity is a remedy for major public health problems. Kinesiology 2004, 36, 123–153Haskell, W.L.; Lee, I.M.; Pate, R.R.; Powell, K.E.; Blair, S.N.; Franklin, B.A.; Macera, C.A.; Heath, G.W.; Thompson, P.D.; Bauman, A. Physical Activity and Public Health. Med. Sci. Sport. Exerc. 2007, 39, 1423–1434. [CrossRef]Pinto-Bernal, M.J.; Aguirre, A.; Cifuentes, C.A.; Munera, M. Wearable Sensors for Monitoring Exercise and Fatigue Estimation in Rehabilitation. In Internet of Medical Things; CRC Press: Boca Raton, FL, USA, 2021; pp. 83–110.Kristensen, T.; Kornitzer, M.; Alfredsson, L.; Marmot, M.; Logstrup, S.; Williams, C. Social Factors, Work, Stress and Cardiovascular Disease Prevention in the European Union; European Heart Network: Brussels, Belgium, 1998Priest, N.; Armstrong, R.; Doyle, J.; Waters, E. Interventions implemented through sporting organisations for increasing participation in sport. Cochrane Database Syst. Rev. 2008, 18, CD004812. [CrossRef]Livingstone, M.; Robson, P.; Wallace, J.; McKinley, M. How active are we? Levels of routine physical activity in children and adults. Proc. Nutr. Soc. 2003, 62, 681–701. [CrossRef]Pollock, M.L.; Gaesser, G.A.; Butcher, J.D.; Després, J.P.; Dishman, R.K.; Franklin, B.A.; Garber, C.E. The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Schweiz. Z. Sportmed. 1998, 41, 127–137. [CrossRef] [PubMed]Andersen, L.B.; Schnohr, P.; Schroll, M.; Hein, H.O. All-Cause Mortality Associated with Physical Activity during Leisure Time, Work, Sports, and Cycling to Work. Arch. Intern. Med. 2000, 160, 1621–1628. [CrossRef] [PubMed]Schnohr, P.; Marott, J.L.; Jensen, J.S.; Jensen, G.B. Intensity versus duration of cycling, impact on all-cause and coronary heart disease mortality: The Copenhagen City Heart Study. Eur. J. Prev. Cardiol. 2012, 19, 73–80. [CrossRef] [PubMed]Warburton, D.E. Prescribing exercise as preventive therapy. Can. Med. Assoc. J. 2006, 174, 961–974. [CrossRef] [PubMed]Cup, E.H.; Pieterse, A.J.; ten Broek-Pastoor, J.M.; Munneke, M.; van Engelen, B.G.; Hendricks, H.T.; van der Wilt, G.J.; Oostendorp, R.A. Exercise Therapy and Other Types of Physical Therapy for Patients With Neuromuscular Diseases: A Systematic Review. Arch. Phys. Med. Rehabil. 2007, 88, 1452–1464. [CrossRef] [PubMed]Manley, A.F. Physical Activity and Health: A Report of the Surgeon General; Diane Publishing: Darby, PA, USA, 1996Lee, I.M.; Sesso, H.D.; Oguma, Y.; Paffenbarger, R.S. Relative intensity of physical activity and risk of coronary heart disease. Circulation 2003, 107, 1110–1116. [CrossRef]American College of Sports Medicine. ACSM’s Health-Related Physical Fitness Assessment Manual; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2013.Balducci, S.; Sacchetti, M.; Haxhi, J.; Orlando, G.; D’Errico, V.; Fallucca, S.; Menini, S.; Pugliese, G. Physical exercise as therapy for type 2 diabetes mellitus. Diabetes/Metab. Res. Rev. 2014, 30, 13–23. [CrossRef]Dun, Y.; Smith, J.R.; Liu, S.; Olson, T.P. High-Intensity Interval Training in Cardiac Rehabilitation. Sports Med. 2019, 42, 587–605. [CrossRef]Tanasescu, M.; Leitzmann, M.F.; Rimm, E.B.; Willett, W.C.; Stampfer, M.J.; Hu, F.B. Exercise type and intensity in relation to coronary heart disease in men. J. Am. Med. Assoc. 2002, 288, 1994–2000. [CrossRef]Oldervoll, L.; Kaasa, S.; Hjermstad, M.; Lund, J.; Loge, J. Physical exercise results in the improved subjective well-being of a few or is effective rehabilitation for all cancer patients? Eur. J. Cancer 2004, 40, 951–962. [CrossRef]Fleig, L.; Lippke, S.; Pomp, S.; Schwarzer, R. Exercise maintenance after rehabilitation: How experience can make a difference. Psychol. Sport Exerc. 2011, 12, 293–299. [CrossRef]Göhner, W.; Seelig, H.; Fuchs, R. Intervention effects on cognitive antecedents of physical exercise: A 1-year follow-up study. Appl. Psychol. Health Well-Being 2009, 1, 233–256. [CrossRef]Abd-Elfattah, H.M.; Abdelazeim, F.H.; Elshennawy, S. Physical and cognitive consequences of fatigue: A review. J. Adv. Res. 2015, 6, 351–358. [CrossRef]Baussard, L.; Carayol, M.; Porro, B.; Baguet, F.; Cousson-gelie, F. European Journal of Oncology Nursing Fatigue in cancer patients : Development and validation of a short form of the Multidimensional Fatigue Inventory ( MFI-10 ). Eur. J. Oncol. Nurs. 2018, 36, 62–67. [CrossRef] [PubMed]Alghannam, A.F.; Tsintzas, K.; Thompson, D.; Bilzon, J.; Betts, J.A. Exploring mechanisms of fatigue during repeated exercise and the dose dependent effects of carbohydrate and protein ingestion: Study protocol for a randomised controlled trial. Trials 2014, 15, 95. [CrossRef]Ozalp, O.; Inal-Ince, D.; Calik, E.; Vardar-Yagli, N.; Saglam, M.; Savci, S.; Arikan, H.; Bosnak-Guclu, M.; Coplu, L. Extrapulmonary features of bronchiectasis: Muscle function, exercise capacity, fatigue, and health status. Multidiscip. Respir. Med. 2012, 7, 3. [CrossRef]Lu, L.; Megahed, F.M.; Sesek, R.F.; Cavuoto, L.A. A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among US manufacturing workers. Appl. Ergon. 2017, 65, 139–151. [CrossRef]Zamunér, A.R.; Moreno, M.A.; Camargo, T.M.; Graetz, J.P.; Rebelo, A.C.; Tamburús, N.Y.; da Silva, E. Assessment of subjective perceived exertion at the anaerobic threshold with the Borg CR-10 scale. J. Sport. Sci. Med. 2011, 10, 130–136Curt, G.A.; Breitbart, W.; Cella, D.; Groopman, J.E.; Horning, S.J.; Itri, L.M.; Johnson, D.H.; Miaskowski, C.; Scherr, S.L.; Portenoy, R.K.; et al. Impact of cancer-related fatigue on the lives of patients: New findings from the Fatigue Coalition. Oncologist 2000, 5, 353–360. [CrossRef] [PubMed]Annett, J. Subjective rating scales: Science or art? Ergonomics 2002, 45, 966–987. [CrossRef] [PubMed]Williams, N. The Borg rating of perceived exertion (RPE) scale. Occup. Med. 2017, 67, 404–405. [CrossRef]Borg, G. Borg’s range model and scales. Int. J. Sport Psychol. 2001, 32, 110-126.Sehle, A.; Vieten, M.; Sailer, S.; Mündermann, A.; Dettmers, C. Objective assessment of motor fatigue in multiple sclerosis: The Fatigue index Kliniken Schmieder (FKS). J. Neurol. 2014, 261, 1752–1762. [CrossRef] [PubMed]Maman, Z.S.; Chen, Y.J.; Baghdadi, A.; Lombardo, S.; Cavuoto, L.A.; Megahed, F.M. A data analytic framework for physical fatigue management using wearable sensors. Expert Syst. Appl. 2020, 155, 113405. [CrossRef]Qi, J.; Yang, P.; Waraich, A.; Deng, Z.; Zhao, Y.; Yang, Y. Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review. J. Biomed. Inform. 2018, 87, 138–153. [CrossRef]Zeni, A.I.; Hoffman, M.D.; Clifford, P.S. Relationships among heart rate, lactate concentration, and perceived effort for different types of rhythmic exercise in women. Arch. Phys. Med. Rehabil. 1996, 77, 237–241. [CrossRef]Poole, D.C.; Burnley, M.; Vanhatalo, A.; Rossiter, H.B.; Jones, A.M. Critical power: An important fatigue threshold in exercise physiology. Med. Sci. Sport. Exerc. 2016, 48, 2320–2334. [CrossRef]Pettersson, S.; Lundberg, I.; Liang, M.; Pouchot, J.; Welin Henriksson, E. Determination of the minimal clinically important difference for seven measures of fatigue in Swedish patients with systemic lupus erythematosus. Scand. J. Rheumatol. 2015, 44, 206–210. [CrossRef]Yu, F.; Bilberg, A.; Stenager, E.; Rabotti, C.; Zhang, B.; Mischi, M. A wireless body measurement system to study fatigue in multiple sclerosis. Physiol. Meas. 2012, 33, 2033–2048. [CrossRef]Möhler, F.; Ringhof, S.; Debertin, D.; Stein, T. Influence of fatigue on running coordination: A UCM analysis with a geometric 2D model and a subject-specific anthropometric 3D model. Hum. Mov. Sci. 2019, 66, 133–141. [CrossRef]Kang, S.R.; Min, J.Y.; Yu, C.; Kwon, T.K. Effect of whole body vibration on lactate level recovery and heart rate recovery in rest after intense exercise. Technol. Health Care 2017, 25, 115–123. [CrossRef] [PubMed]Glynn, A.J.; Fiddler, H. The Physiotherapist’s Pocket Guide to Exercise E-Book: Assessment, Prescription and Training; Elsevier Health Sciences: Amsterdam, The Netherlands, 2009.Aubert, A.E.; Seps, B.; Beckers, F. Heart rate variability in athletes. Sport. Med. 2003, 33, 889–919. [CrossRef]Achten, J.; Jeukendrup, A.E. Heart rate monitoring. Sport. Med. 2003, 33, 517–538. [CrossRef]da Cunha, F.A.; Farinatti, P.d.T.V.; Midgley, A.W. Methodological and practical application issues in exercise prescription using the heart rate reserve and oxygen uptake reserve methods. J. Sci. Med. Sport. 2011, 14, 46–57. [CrossRef]Goodwin, M.L.; Harris, J.E.; Hernández, A.; Gladden, L.B. Blood lactate measurements and analysis during exercise: A guide for clinicians. J. Diabetes Sci. Technol. 2007, 1, 558–569. [CrossRef]Jansen, T.C.; van Bommel, J.; Bakker, J. Blood lactate monitoring in critically ill patients: A systematic health technology assessment. Crit. Care Med. 2009, 37, 2827–2839Saey, D.; Michaud, A.; Couillard, A.; Côté, C.H.; Mador, M.J.; LeBlanc, P.; Jobin, J.; Maltais, F. Contractile fatigue, muscle morphometry, and blood lactate in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2005, 171, 1109–1115. [CrossRef]Helbostad, J.L.; Sturnieks, D.L.; Menant, J.; Delbaere, K.; Lord, S.R.; Pijnappels, M. Consequences of lower extremity and trunk muscle fatigue on balance and functional tasks in older people: A systematic literature review. BMC Geriatr. 2010, 10, 56. [CrossRef]Wan, J.-J.; Qin, Z.; Wang, P.-Y.; Sun, Y.; Liu, X. Muscle fatigue: General understanding and treatment. Exp. Mol. Med. 2017, 49, e384. [CrossRef]Karthick, P.A.; Ghosh, D.M.; Ramakrishnan, S. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Comput. Methods Programs Biomed. 2018, 154, 45–56. [CrossRef] [PubMed]Subasi, A.; Kiymik, M.K. Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks. J. Med. Syst. 2010, 34, 777–785. [CrossRef] [PubMed]Al-Mulla, M.R.; Sepulveda, F.; Colley, M. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors 2011, 11, 3545–3594. [CrossRef]Camomilla, V.; Bergamini, E.; Fantozzi, S.; Vannozzi, G. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review. Sensors 2018, 18, 873. [CrossRef] [PubMed]Ejupi, A.; Gschwind, Y.J.; Valenzuela, T.; Lord, S.R.; Delbaere, K. A Kinect and Inertial Sensor-Based System for the Self-Assessment of Fall Risk: A Home-Based Study in Older People. Hum.-Comput. Interact. 2016, 31, 261–293. [CrossRef]Manchola, S.; Bernal, P.; Munera, M.; Cifuentes, C.A. Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals. Sensors 2019, 19, 2988. [CrossRef] [PubMed]Aguirre, A.; Casas, J.; Céspedes, N.; Múnera, M.; Rincon-Roncancio, M.; Cuesta-Vargas, A.; Cifuentes, C.A. Feasibility study: Towards Estimation of Fatigue Level in Robot-Assisted Exercise for Cardiac Rehabilitation. In Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada, 24–28 June 2019; pp. 911–916Céspedes, N.; Múnera, M.; Gómez, C.; Cifuentes, C.A. Social Human-Robot Interaction for Gait Rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 1299–1307. [CrossRef]Segala, D.B.; Chelidze, D.; Adams, A.; Schiffman, J.M.; Hasselquist, L. Tracking Physiological Fatigue in Prolonged Load Carriage Walking Using Phase Space Warping and Smooth Orthogonal Decomposition. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Boston, MA, USA, 31 October–6 November 2008; Volume 48630, pp. 323–331. 64. Mugnosso, M.; Marini, F.; Holmes, M.; Morasso, P.; Zenzeri, J. Muscle fatigue assessment during robot-mediated movements. J. Neuroeng. Rehabil. 2018, 15, 1–14. [CrossRef]Chan, V.C.; Beaudette, S.M.; Smale, K.B.; Beange, K.H.; Graham, R.B. A subject-specific approach to detect fatigue-related changes in spine motion using wearable sensors. Sensors 2020, 20, 2646. [CrossRef]Ross, L.M.; Porter, R.R.; Durstine, J.L. High-intensity interval training (HIIT) for patients with chronic diseases. J. Sport Health Sci. 2016, 5, 139–144. [CrossRef]García-López, J.; Morante, J.C.; Ogueta-Alday, A.; Rodríguez-Marroyo, J.A. The Type Of Mat (Contact vs. Photocell) Affects Vertical Jump Height Estimated From Flight Time. J. Strength Cond. Res. 2013, 27, 1162–1167. [CrossRef]Aguirre, A.; Pinto, M.J.; Cifuentes, C.A.; Perdomo, O.; Díaz, C.A.; Múnera, M. Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise. Sensors 2021, 21, 5006. [CrossRef]Zhang, J.; Lockhart, T.E.; Soangra, R. Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Ann. Biomed. Eng. 2014, 42, 600–612. [CrossRef] [PubMed]Karg, M.; Venture, G.; Hoey, J.; Kuli´c, D. Human movement analysis as a measure for fatigue: A hidden Markov-based approach. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 470–481. [CrossRef] [PubMed]Karg, M.; Kühnlenz, K.; Buss, M.; Seiberl, W.; Tusker, F.; Schmeelk, M.; Schwirtz, A. Expression and automatic recognition of exhaustion in natural walking. In Proceedings of the IADIS Interfaces and Human Computer Interaction (IHCI), Amsterdam, The Netherlands, 25–27 July 2008; pp. 165–172.Kavanagh, J.J.; Morrison, S.; Barrett, R.S. Lumbar and cervical erector spinae fatigue elicit compensatory postural responses to assist in maintaining head stability during walking. J. Appl. Physiol. 2006, 101, 1118–1126. [CrossRef]Yoshino, K.; Motoshige, T.; Araki, T.; Matsuoka, K. Effect of prolonged free-walking fatigue on gait and physiological rhythm. J. Biomech. 2004, 37, 1271–1280. [CrossRef] [PubMed]Maman, Z.S.; Yazdi, M.A.A.; Cavuoto, L.A.; Megahed, F.M. A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. Appl. Ergon. 2017, 65, 515–529. [CrossRef] [PubMed]Lee, M.; Roan, M.; Smith, B.; Lockhart, T.E. Gait analysis to classify external load conditions using linear discriminant analysis. Hum. Mov. Sci. 2009, 28, 226–235. [CrossRef]Helbostad, J.L.; Leirfall, S.; Moe-Nilssen, R.; Sletvold, O. Physical fatigue affects gait characteristics in older persons. J. Gerontol. Ser. Biol. Sci. Med Sci. 2007, 62, 1010–1015. [CrossRef]Winter, D.A. Human balance and posture control during standing and walking. Gait Posture 1995, 3, 193–214. [CrossRef]Warburton, D.E.; Gledhill, N.; Quinney, A. Musculoskeletal fitness and health. Can. J. Appl. Physiol. 2001, 26, 217–237. [CrossRef]Swift-Spong, K.; Short, E.; Wade, E.; Matari´c, M.J. Effects of comparative feedback from a socially assistive robot on self-efficacy in post-stroke rehabilitation. In Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, 11–14 August 2015; pp. 764–769.Fasola, J.; Matari´c, M.J. A socially assistive robot exercise coach for the elderly. J. Hum.-Robot Interact. 2013, 2, 3–32. [CrossRef]Casas, J.; Senft, E.; Gutierrez, L.F.; Rincon-Rocancio, M.; Munera, M.; Belpaeme, T.; Cifuentes, C.A. Social assistive robots: Assessing the impact of a training assistant robot in cardiac rehabilitation. Int. J. Soc. Robot. 2020, 1–15. [CrossRef]Cifuentes, C.A.; Pinto, M.J.; Céspedes, N.; Múnera, M. Social robots in therapy and care. Curr. Robot. Rep. 2020, 1, 59–74. [CrossRef]Céspedes Gómez, N.; Irfan, B.; Senft, E.; Cifuentes, C.A.; Gutierrez, L.F.; Rincon-Roncancio, M.; Belpaeme, T.; Munera, M. A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World. Front. Neurorobot. 2021, 15, 21.Gockley, R.; Bruce, A.; Forlizzi, J.; Michalowski, M.; Mundell, A.; Rosenthal, S.; Sellner, B.; Simmons, R.; Snipes, K.; Schultz, A.C.; et al. Designing robots for long-term social interaction. In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, 2–6 August 2005; pp. 1338–1343.Gockley, R.; MatariC, M.J. Encouraging physical therapy compliance with a hands-off mobile robot. In Proceedings of the 1st ´ ACM SIGCHI/SIGART Conference on Human–Robot Interaction, Salt Lake City, UT, USA, 2–3 March 2006; pp. 150–155Matari´c, M.J.; Eriksson, J.; Feil-Seifer, D.J.; Winstein, C.J. Socially assistive robotics for post-stroke rehabilitation. J. Neuroeng. Rehabil. 2007, 4, 1–9. [CrossRef] [PubMed]Smets, E.M.; Garssen, B.; Bonke, B.; De Haes, J.C. The multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J. Psychosom. Res. 1995, 39, 315–325. [CrossRef]Kakria, P.; Tripathi, N.; Kitipawang, P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int. J. Telemed. Appl. 2015, 2015, 373474. [CrossRef]Moohialdin, A.S.; Suhariadi, B.T.; Siddiqui, M.K. Practical validation measurements of a physiological status monitoring sensor in real construction activities. In Proceedings of the Streamlining Information Transfer between Construction and Structural Engineering, Brisbane, Australia, 3–5 December 2018.Swain, D.P.; Brawner, C.A.; American College of Sports Medicine. ACSM’s Resource Manual for Guidelines for Exercise Testing and Prescription; Wolters Kluwer Health/Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2014.Taborri, J.; Rossi, S.; Palermo, E.; Patanè, F.; Cappa, P. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network. Sensors 2014, 14, 16212–16234. [CrossRef] [PubMed]Sabatini, A.; Martelloni, C.; Scapellato, S.; Cavallo, F. Assessment of Walking Features From Foot Inertial Sensing. IEEE Trans. Biomed. Eng. 2005, 52, 486–494. [CrossRef] [PubMed]Kotiadis, D.; Hermens, H.; Veltink, P. Inertial Gait Phase Detection for control of a drop foot stimulator. Med. Eng. Phys. 2010, 32, 287–297. [CrossRef]Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Proceedings of the International Conference on Pervasive Computing, Vienna, Austria, 21–23 April 2004; Springer: Berlin/Heidelberg, Germany, 2004.Pirttikangas, S.; Fujinami, K.; Nakajima, T. Feature selection and activity recognition from wearable sensors. In International Symposium on Ubiquitious Computing Systems; Springer: Berlin/Heidelberg, Germany, 2006; pp. 516–527.Reaz, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. Techniques of EMG signal analysis: Detection, processing, classification and applications. Biol. Proced. Online 2006, 8, 11–35. [CrossRef] [PubMed]Wojtys, E.M.; Wylie, B.B.; Huston, L.J. The effects of muscle fatigue on neuromuscular function and anterior tibial translation in healthy knees. Am. J. Sport. Med. 1996, 24, 615–621. [CrossRef] [PubMed]Kern, N.; Schiele, B.; Schmidt, A. Multi-sensor activity context detection for wearable computing. In European Symposium on Ambient Intelligence; Springer: Berlin/Heidelberg, Germany, 2003; pp. 220–232.Marras, W.S.; Lavender, S.A.; Leurgans, S.E.; Rajulu, S.L.; Allread, S.W.G.; Fathallah, F.A.; Ferguson, S.A. The role of dynamic three-dimensional trunk motion in occupationally-related. Spine 1993, 18, 617–628. [CrossRef] [PubMed]Huynh, T.; Schiele, B. Analyzing features for activity recognition. In Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, Grenoble, France, 12–14 October 2005; pp. 159–163.Heinz, E.A.; Kunze, K.S.; Sulistyo, S.; Junker, H.; Lukowicz, P.; Tröster, G. Experimental evaluation of variations in primary features used for accelerometric context recognition In European Symposium on Ambient Intelligence; Springer: Berlin/Heidelberg, Germany, 2003; pp. 252–263.Krause, A.; Siewiorek, D.P.; Smailagic, A.; Farringdon, J. Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing. ISWC 2003, 3, 88.Lee, S.W.; Mase, K. Activity and location recognition using wearable sensors. IEEE Pervasive Comput. 2002, 1, 24–32.Lessley, D.; Crandall, J.; Shaw, G.; Kent, R.; Funk, J. A Normalization Technique for Developing Corridors from Individual Subject Responses; Technical Report; SAE Technical Paper: Detroit, MI, USA, 2004Moorhouse, K. An improved normalization methodology for developing mean human response curves. In Proceedings of the International Technical Conference on the Enhanced Safety of Vehicles, Seoul, Korea, 27–30 May 2013.Yoganandan, N.; Arun, M.W.; Pintar, F.A. Normalizing and scaling of data to derive human response corridors from impact tests. J. Biomech. 2014, 47, 1749–1756. [CrossRefKohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI, Montreal, QC, Canada, 20–25 August 1995; Volume 14, pp. 1137–1145Jain, A.K.; Duin, R.P.W.; Mao, J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 4–37. [CrossRef]Liu, H.; Cocea, M. Semi-random partitioning of data into training and test sets in granular computing context. Granul. Comput. 2017, 2, 357–386. [CrossRef]Browne, M.W. Cross-validation methods. J. Math. Psychol. 2000, 44, 108–132. [CrossRef]Dag, A.; Topuz, K.; Oztekin, A.; Bulur, S.; Megahed, F.M. A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decis. Support Syst. 2016, 86, 1–12. [CrossRef]Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.Han, J.; Pei, J.; Kamber, M. Data Mining: Concepts and Techniques; Elsevier: Amsterdam, The Netherlands, 2011.James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning; Springer: Berlin/Heidelberg, Germany, 2013; Volume 112.Kuhn, M.; Johnson, K. Applied Predictive Modeling; Springer: Berlin/Heidelberg, Germany, 2013; Volume 26Fernández, A.; García, S.; Galar, M.; Prati, R.C.; Krawczyk, B.; Herrera, F. Learning from Imbalanced Data Sets; Springer: Berlin/Heidelberg, Germany, 2018; Volume 10.Krawczyk, B. Learning from imbalanced data: Open challenges and future directions. Prog. Artif. Intell. 2016, 5, 221–232. [CrossRef]Skiena, S.S. The Data Science Design Manual; Springer: Berlin/Heidelberg, Germany, 2017.Khalid, S.; Khalil, T.; Nasreen, S. A survey of feature selection and feature extraction techniques in machine learning. In Proceedings of the 2014 Science and Information Conference, London, UK, 27–29 August 2014; pp. 372–378.Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M.L. Activity Recognition from Accelerometer Data; AAAI: Pittsburgh, PA, USA, 2005; Volume 5, pp. 1541–1546.Casas, J.; Irfan, B.; Senft, E.; Gutiérrez, L.; Rincon-Roncancio, M.; Munera, M.; Belpaeme, T.; Cifuentes, C.A. Social Assistive Robot for Cardiac Rehabilitation: A Pilot Study with Patients with Angioplasty. In Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI’18, Chicago, IL, USA, 5–8 March 2018; pp. 79–80.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAnálisis de datos fácticosFactual data analysisFatigaFatigueProgramas de rehabilitaciónRehabilitation programsAprendizaje automático (Inteligencia artificial)Machine learningfatigue diagnosisclassification modelsInertial measurement unitsEMGPhysical exerciseDiagnóstico de fatigaModelos de clasificaciónUnidades de medida inercialesEjercicio fisicoTEXTA Data-Driven Approach to Physical Fatigue Management using.pdf.txtA Data-Driven Approach to Physical Fatigue Management using.pdf.txtExtracted texttext/plain96558https://repositorio.escuelaing.edu.co/bitstream/001/3229/4/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf.txt425fb4dd7699218476f9018924e83a85MD54metadata only accessTHUMBNAILPortada A data-driven approach to physical fatigue management.PNGPortada A data-driven approach to physical fatigue management.PNGimage/png224200https://repositorio.escuelaing.edu.co/bitstream/001/3229/3/Portada%20A%20data-driven%20approach%20to%20physical%20fatigue%20management.PNG62f9593f531f626d007a3c2b1e1b80f6MD53open accessA Data-Driven Approach to Physical Fatigue Management using.pdf.jpgA Data-Driven Approach to Physical Fatigue Management using.pdf.jpgGenerated Thumbnailimage/jpeg16039https://repositorio.escuelaing.edu.co/bitstream/001/3229/5/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf.jpg647ab2e00d221c775dba4f9512ad4fd6MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3229/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALA Data-Driven Approach to Physical Fatigue Management using.pdfA Data-Driven Approach to Physical Fatigue Management using.pdfapplication/pdf1907207https://repositorio.escuelaing.edu.co/bitstream/001/3229/1/A%20Data-Driven%20Approach%20to%20Physical%20Fatigue%20Management%20using.pdf3e357ec5b436277213c51afc75e917ebMD51metadata only access001/3229oai:repositorio.escuelaing.edu.co:001/32292024-08-29 03:04:40.085metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.coU0kgVVNURUQgSEFDRSBQQVJURSBERUwgR1JVUE8gREUgUEFSRVMgRVZBTFVBRE9SRVMgREUgTEEgQ09MRUNDScOTTiAiUEVFUiBSRVZJRVciLCBPTUlUQSBFU1RBIExJQ0VOQ0lBLgoKQXV0b3Jpem8gYSBsYSBFc2N1ZWxhIENvbG9tYmlhbmEgZGUgSW5nZW5pZXLDrWEgSnVsaW8gR2FyYXZpdG8gcGFyYSBwdWJsaWNhciBlbCB0cmFiYWpvIGRlIGdyYWRvLCBhcnTDrWN1bG8sIHZpZGVvLCAKY29uZmVyZW5jaWEsIGxpYnJvLCBpbWFnZW4sIGZvdG9ncmFmw61hLCBhdWRpbywgcHJlc2VudGFjacOzbiB1IG90cm8gKGVuICAgIGFkZWxhbnRlIGRvY3VtZW50bykgcXVlIGVuIGxhIGZlY2hhIAplbnRyZWdvIGVuIGZvcm1hdG8gZGlnaXRhbCwgeSBsZSBwZXJtaXRvIGRlIGZvcm1hIGluZGVmaW5pZGEgcXVlIGxvIHB1YmxpcXVlIGVuIGVsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwsIAplbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIHkgZGVtw6FzIGxleWVzIHkganVyaXNwcnVkZW5jaWEgdmlnZW50ZQphbCByZXNwZWN0bywgcGFyYSBmaW5lcyBlZHVjYXRpdm9zIHkgbm8gbHVjcmF0aXZvcy4gRXN0YSBhdXRvcml6YWNpw7NuIGVzIHbDoWxpZGEgcGFyYSBsYXMgZmFjdWx0YWRlcyB5IGRlcmVjaG9zIGRlIAp1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbDsgeSBwYXJhIHVzb3MgZW4gcmVkZXMsIGludGVybmV0LCBleHRyYW5ldCwgeSBjdWFscXVpZXIgCmZvcm1hdG8gbyBtZWRpbyBjb25vY2lkbyBvIHBvciBjb25vY2VyLgpFbiBtaSBjYWxpZGFkIGRlIGF1dG9yLCBleHByZXNvIHF1ZSBlbCBkb2N1bWVudG8gb2JqZXRvIGRlIGxhIHByZXNlbnRlIGF1dG9yaXphY2nDs24gZXMgb3JpZ2luYWwgeSBsbyBlbGFib3LDqSBzaW4gCnF1ZWJyYW50YXIgbmkgc3VwbGFudGFyIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcy4gUG9yIGxvIHRhbnRvLCBlcyBkZSBtaSBleGNsdXNpdmEgYXV0b3LDrWEgeSwgZW4gY29uc2VjdWVuY2lhLCAKdGVuZ28gbGEgdGl0dWxhcmlkYWQgc29icmUgw6lsLiBFbiBjYXNvIGRlIHF1ZWphIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybyByZWZlcmVudGUgYSBsb3MgZGVyZWNob3MgZGUgYXV0b3Igc29icmUgCmVsIGRvY3VtZW50byBlbiBjdWVzdGnDs24sIGFzdW1pcsOpIGxhIHJlc3BvbnNhYmlsaWRhZCB0b3RhbCB5IHNhbGRyw6kgZW4gZGVmZW5zYSBkZSBsb3MgZGVyZWNob3MgYXF1w60gYXV0b3JpemFkb3MuIEVzdG8gCnNpZ25pZmljYSBxdWUsIHBhcmEgdG9kb3MgbG9zIGVmZWN0b3MsIGxhIEVzY3VlbGEgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4KVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsIGRlIGxhIEVzY3VlbGEsIGVsIENhdMOhbG9nbyBlbiBsw61uZWEgdSBvdHJvIG1lZGlvIGVsZWN0csOzbmljbywgCnBvZHLDoSBjb3BpYXIgYXBhcnRlcyBkZWwgdGV4dG8sIGNvbiBlbCBjb21wcm9taXNvIGRlIGNpdGFyIHNpZW1wcmUgbGEgZnVlbnRlLCBsYSBjdWFsIGluY2x1eWUgZWwgdMOtdHVsbyBkZWwgdHJhYmFqbyB5IGVsIAphdXRvci5Fc3RhIGF1dG9yaXphY2nDs24gbm8gaW1wbGljYSByZW51bmNpYSBhIGxhIGZhY3VsdGFkIHF1ZSB0ZW5nbyBkZSBwdWJsaWNhciB0b3RhbCBvIHBhcmNpYWxtZW50ZSBsYSBvYnJhIGVuIG90cm9zIAptZWRpb3MuRXN0YSBhdXRvcml6YWNpw7NuIGVzdMOhIHJlc3BhbGRhZGEgcG9yIGxhcyBmaXJtYXMgZGVsIChsb3MpIGF1dG9yKGVzKSBkZWwgZG9jdW1lbnRvLiAKU8OtIGF1dG9yaXpvIChhbWJvcykK

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Publicaciones similares