Publication:
Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Thumbnail Image
Files

Files

Machine Learning Approach for Fatigue Estimation in.pdf (1.34 MB)
View Statistics
Reference Managers
Mendeley
Indexers
Google Scholar CORE
QR Code
QR Code
Authors

Authors

Aguirre, Andrés
Pinto, Maria J.
Cifuentes, Carlos A.
Perdomo, Oscar
Díaz, Camilo A
Múnera, Marcela
Abstract (Spanish)
El ejercicio físico (EF) se ha convertido en una herramienta imprescindible para diferentes programas de rehabilitación. Se ha demostrado que los ejercicios de alta intensidad (HIE) proporcionan mejores resultados en condiciones generales de salud, en comparación con los ejercicios de intensidad baja y moderada. En este contexto, el seguimiento del estado del paciente es fundamental para evitar condiciones extremas de fatiga, que pueden provocar complicaciones físicas y fisiológicas. Se han propuesto diferentes métodos para la estimación de la fatiga, tales como: monitoreo de parámetros fisiológicos del sujeto y escalas subjetivas. Sin embargo, todavía se necesitan procedimientos prácticos que proporcionen una estimación objetiva, especialmente para las HIE. En este trabajo, considerando que el ejercicio sit-to-stand (STS) es uno de los más implementados en rehabilitación física, se propone un modelo computacional para estimar la fatiga durante este ejercicio. Se llevó a cabo un estudio con 60 voluntarios sanos para obtener un conjunto de datos para desarrollar y evaluar el modelo propuesto. Según la literatura, este modelo estima tres condiciones de fatiga (baja, moderada y alta) mediante el monitoreo de 32 características cinemáticas STS y la frecuencia cardíaca de un conjunto de sensores ambulatorios (sensores Kinect y Zephyr). Los resultados muestran que un modelo de bosque aleatorio compuesto por 60 subclasificadores presentó una precisión del 82,5% en la tarea de clasificación. Además, los resultados sugieren que el movimiento de la parte superior del cuerpo es la característica más relevante para la estimación de la fatiga. Los movimientos de la parte inferior del cuerpo y la frecuencia cardíaca también aportan información esencial para identificar el estado de fatiga. Este trabajo presenta una herramienta prometedora para la rehabilitación física.
Abstract (English)
Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.
Extent
31 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3242
Collections

Collections

AA. Gibiome
References

Thompson, P. Exercise and Physical Activity in the Prevention and Treatment of Atherosclerotic Cardiovascular Disease: A Statement From the Council on Clinical Cardiology. Arterioscler. Thromb. Vasc. Biol. 2003, 23, 42e–49e. [CrossRef]

World Health Organization. Global Status Report on Noncommunicable Diseases 2014; Number WHO/NMH/NVI/15.1; World Health Organization: Geneva, Switzerland, 2014.

Warburton, D.E.R.; Nicol, C.W.; Bredin, S.S.D. Prescribing exercise as preventive therapy. CMAJ 2006, 174, 961–974

Pedersen, B.K. Physical Exercise in Chronic Diseases. In Nutrition and Skeletal Muscle; Elsevier: Amsterdam, The Netherlands, 2019; pp. 217–266. [CrossRef]

Ignarro, L.J.; Balestrieri, M.L.; Napoli, C. Nutrition, physical activity, and cardiovascular disease: An update. Cardiovasc. Res. 2007, 73, 326–340. [CrossRef] [PubMed]

Price, K.J.; Gordon, B.A.; Bird, S.R.; Benson, A.C. A review of guidelines for cardiac rehabilitation exercise programmes: Is there an international consensus? Eur. J. Prev. Cardiol. 2016, 23, 1715–1733. [CrossRef]

Dibben, G.O.; Dalal, H.M.; Taylor, R.S.; Doherty, P.; Tang, L.H.; Hillsdon, M. Cardiac rehabilitation and physical activity: Systematic review and meta-analysis. Heart 2018, 104, 1394–1402. [CrossRef] [PubMed]

Gloeckl, R.; Schneeberger, T.; Jarosch, I.; Kenn, K. Pulmonary rehabilitation and exercise training in chronic obstructive pulmonary disease. Dtsch. ÄRzteblatt Int. 2018, 115, 117. [CrossRef] [PubMed]

Spruit, M.A.; Pitta, F.; McAuley, E.; ZuWallack, R.L.; Nici, L. Pulmonary rehabilitation and physical activity in patients with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2015, 192, 924–933. [CrossRef] [PubMed]

Dalzell, M.; Smirnow, N.; Sateren, W.; Sintharaphone, A.; Ibrahim, M.; Mastroianni, L.; Zambrano, L.V.; O’Brien, S. Rehabilitation and exercise oncology program: Translating research into a model of care. Curr. Oncol. 2017, 24, e191. [CrossRef] [PubMed]

Spence, R.R.; Heesch, K.C.; Brown, W.J. Exercise and cancer rehabilitation: A systematic review. Cancer Treat. Rev. 2010, 36, 185–194. [CrossRef]

Morrow, G.R.; Shelke, A.R.; Roscoe, J.A.; Hickok, J.T.; Mustian, K. Management of cancer-related fatigue. Clin. J. Oncol. Nurs. 2005, 23, 229–239. [CrossRef]

Dörr, W.; Engenhart-Cabillic, R.; Zimmermann, J.S. Normal Tissue Reactions in Radiotherapy and Oncology; Karger Medical and Scientific Publishers: Basel, Switzerland, 2002; Volume 37.

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]

Lee, Y.; Ahn, S. The Effects of Kinesio Taping and Neuromuscular Rehabilitation Exercise for Patients with Acute WhiplashAssociated Disorder. J. Korean Acad. Orthop. Man. Phys. Ther. 2016, 22, 41–49.

Voorn, E.L.; Koopman, F.; Nollet, F.; Brehm, M.A. Aerobic exercise in adult neuromuscular rehabilitation: A survey of healthcare professionals. J. Rehabil. Med. 2019, 51, 518–524. [CrossRef]

Frontera, W.R. Exercise and Musculoskeletal Rehabilitation: Restoring Optimal Form and Function. Physician Sportsmed. 2003, 31, 39–45. [CrossRef]

Escalante, Y.; Saavedra, J.M.; García-Hermoso, A.; Silva, A.J.; Barbosa, T.M. Physical exercise and reduction of pain in adults with lower limb osteoarthritis: A systematic review. J. Back Musculoskelet. Rehabil. 2010, 23, 175–186. [CrossRef] [PubMed]

American College of Sports Medicine. ACSM’s Health-Related Physical Fitness Assessment Manual; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2013.

Warburton, D.E.; Gledhill, N.; Quinney, A. Musculoskeletal Fitness and Health. Can. J. Appl. Physiol. 2001, 26, 217–237. [CrossRef] [PubMed]

Warburton, D.E.; Gledhill, N.; Quinney, A. The effects of changes in musculoskeletal fitness on health. Can. J. Appl. Physiol. 2001, 26, 161–216. [CrossRef]

Warburton, D.E.; McKenzie, D.C.; Haykowsky, M.J.; Taylor, A.; Shoemaker, P.; Ignaszewski, A.P.; Chan, S.Y. Effectiveness of high-intensity interval training for the rehabilitation of patients with coronary artery disease. Am. J. Cardiol. 2005, 95, 1080–1084. [CrossRef]

Dun, Y.; Thomas, R.J.; Smith, J.R.; Medina-Inojosa, J.R.; Squires, R.W.; Bonikowske, A.R.; Huang, H.; Liu, S.; Olson, T.P. Highintensity interval training improves metabolic syndrome and body composition in outpatient cardiac rehabilitation patients with myocardial infarction. Cardiovasc. Diabetol. 2019, 18, 104. [CrossRef]

Manley, A. Physical Activity and Health: A Report of the Surgeon General; U.S. Department of Health & Human Services: Atlanta, GA, USA, 1997.

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. Med. Sci. Sport. Exerc. 1998, 30, 975–991. [CrossRef]

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]

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]

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] [PubMed]

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] [PubMed]

Fox, E.L.; Bartels, R.L.; Billings, C.E.; Mathews, D.K.; Bason, R.; Webb, W.M. Intensity and distance of interval training programs and changes in aerobic power. Med. Sci. Sport. 1973, 5, 18–22.

Myers, J.; Prakash, M.; Froelicher, V.; Do, D.; Partington, S.; Atwood, J.E. Exercise Capacity and Mortality among Men Referred for Exercise Testing. N. Engl. J. Med. 2002, 346, 793–801. [CrossRef]

Keteyian, S.J.; Brawner, C.A.; Savage, P.D.; Ehrman, J.K.; Schairer, J.; Divine, G.; Aldred, H.; Ophaug, K.; Ades, P.A. Peak aerobic capacity predicts prognosis in patients with coronary heart disease. Am. Heart J. 2008, 156, 292–300. [CrossRef]

Rognmo, Ø.; Hetland, E.; Helgerud, J.; Hoff, J.; Slørdahl, S.A. High intensity aerobic interval exercise is superior to moderate intensity exercise for increasing aerobic capacity in patients with coronary artery disease. Eur. J. Cardiovasc. Prev. Rehabil. 2004, 11, 216–222. [CrossRef]

Moholdt, T.T.; Amundsen, B.H.; Rustad, L.A.; Wahba, A.; Løvø, K.T.; Gullikstad, L.R.; Bye, A.; Skogvoll, E.; Wisløff, U.; Slørdahl, S.A. Aerobic interval training versus continuous moderate exercise after coronary artery bypass surgery: A randomized study of cardiovascular effects and quality of life. Am. Heart J. 2009, 158, 1031–1037. [CrossRef] [PubMed]

Kemi, O.J.; Wisløff, U. High-Intensity Aerobic Exercise Training Improves the Heart in Health and Disease. J. Cardiopulm. Rehabil. Prev. 2010, 30, 2–11. [CrossRef]

O’Connor, C.M.; Whellan, D.J.; Lee, K.L.; Keteyian, S.J.; Cooper, L.S.; Ellis, S.J.; Leifer, E.S.; Kraus, W.E.; Kitzman, D.W.; Blumenthal, J.A.; et al. Efficacy and safety of exercise training in patients with chronic heart failure HF-ACTION randomized controlled trial. JAMA—J. Am. Med. Assoc. 2009, 301, 1439–1450. [CrossRef]

Cornish, A.K.; Broadbent, S.; Cheema, B.S. Interval training for patients with coronary artery disease: A systematic review Eur. J. Appl. Physiol. 2011, 111, 579–589. [CrossRef]

Balady, G.J.; Williams, M.A.; Ades, P.A.; Bittner, V.; Comoss, P.; Foody, J.A.M.; Franklin, B.; Sanderson, B.; Southard, D. Core components of cardiac rehabilitation/secondary prevention programs: 2007 update—A sci. statement from the Am. Heart Assoc. exercise, cardiac rehabilitation, and prevention comm., the council on clinical cardiology; the councils on cardiovascular nu. Circulation 2007, 115, 2675–2682. [CrossRef]

Kobashigawa, J.A.; Leaf, D.A.; Lee, N.; Gleeson, M.P.; Liu, H.; Hamilton, M.A.; Moriguchi, J.D.; Kawata, N.; Einhorn, K.; Herlihy, E.; et al. A controlled trial of exercise rehabilitation after heart transplantation. N. Engl. J. Med. 1999, 340, 272–277. [CrossRef] [PubMed]

Bohannon, R.W. Sit-to-stand test for measuring performance of lower extremity muscles. Percept. Mot. Ski. 1995, 80, 163–166. [CrossRef] [PubMed]

Bohanno, R.W. Test-retest reliability of the five-repetition sit-to-stand test: A systematic review of the literature involving adults J. Strength Cond. Res. 2011, 25, 3205–3207. [CrossRef]

Jiménez, C.R.; Bennett, P.; García, A.O.; Cuesta Vargas, A.I. Fatigue detection during sit-to-stand test based on surface electromyography and acceleration: A case study. Sensors 2019, 19, 4202. [CrossRef] [PubMed]

Shephard, R. Absolute versus relative intensity of physical activity in a dose-response context. Med. Sci. Sport. 2001, 33 (Suppl. S6), S400–S418. [CrossRef]

Ainsworth, B.; Haskell, W.L.; Leon, A.S.; Jacobs, D.R., Jr.; Montoye, H.J.; Sallis, J.F.; Paffenbarger, R.S., Jr. Compendium of physical activities: Classification of energy costs of human physical activities. Med. Sci. Sport. Exerc. 1993, 25, 71–80. [CrossRef] [PubMed]

Schutz, Y.; Weinsier, R.L.; Hunter, G.R. Assessment of free-living physical activity in humans: An overview of currently available and proposed new measures. Obes. Res. 2001, 9, 368–379. [CrossRef]

Ainsworth, B.E.; Haskell, W.L.; Whitt, M.C.; Irwin, M.L.; Swartz, A.M.; Strath, S.J.; O’brien, W.L.; Bassett, D.R.; Schmitz, K.H.; Emplaincourt, P.; et al. Compendium of Physical Activities: An update of activity codes and MET intensities. Med. Sci. Sport. Exerc. 2000, 32, S498–S504. [CrossRef] [PubMed]

Savage, P.D.; Toth, M.J.; Ades, P.A. A re-examination of the metabolic equivalent concept in individuals with coronary heart disease. J. Cardiopulm. Rehabil. Prev. 2007, 27, 143–148. [CrossRef] [PubMed]

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] [PubMed]

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]

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]

Reybrouck, T.; Mertens, L.; Brusselle, S.; Weymans, M.; Eyskens, B.; Defoor, J.; Gewillig, M. Oxygen uptake versus exercise intensity: A new concept in assessing cardiovascular exercise function in patients with congenital heart disease. Heart 2000, 84, 46–52. [CrossRef]

Jette, M.; Sidney, K.; Blümchen, G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin. Cardiol. 1990, 13, 555–565. [CrossRef]

Fukuda, K.; Straus, S.E.; Hickie, I.; Sharpe, M.C.; Dobbins, J.G.; Komaroff, A. The chronic fatigue syndrome: A comprehensive approach to its definition and study. Ann. Intern. Med. 1994, 121, 953–959. [CrossRef]

Dittner, A.J.; Wessely, S.C.; Brown, R.G. The assessment of fatigue: A practical guide for clinicians and researchers. J. Psychosom. Res. 2004, 56, 157–170. [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]

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]

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]

Stoykov, N.S.; Lowery, M.M.; Kuiken, T.A. A finite-element analysis of the effect of muscle insulation and shielding on the surface EMG signal. IEEE Trans. Biomed. Eng. 2005, 52, 117–121. [CrossRef]

Annett, J. Subjective rating scales: Science or art? Ergonomics 2002, 45, 966–987. [CrossRef]

Borg, G. Borg’s range model and scales. Int. J. Sport Psychol. 2001, 32, 110–126.

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.

Paillard, T. Effects of general and local fatigue on postural control: A review. Neurosci. Biobehav. Rev. 2012, 36, 162–176. [CrossRef] [PubMed]

Roldán-Jiménez, C.; Bennett, P.; Cuesta-Vargas, A.I. Muscular activity and fatigue in lower-limb and trunk muscles during different sit-to-stand tests. PLoS ONE 2015, 10, e0141675. [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

Mokaya, F.; Lucas, R.; Noh, H.Y.; Zhang, P. Burnout: A wearable system for unobtrusive skeletal muscle fatigue estimation. In Proceedings of the 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Vienna, Austria, 11–14 April 2016; pp. 1–12.

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]

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]

McGinnis, R.S.; Cain, S.M.; Davidson, S.P.; Vitali, R.V.; Perkins, N.C.; McLean, S.G. Quantifying the effects of load carriage and fatigue under load on sacral kinematics during countermovement vertical jump with IMU-based method. Sport. Eng. 2016, 19, 21–34. [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]

Hollander, J.E.; Carr, B.G. Virtually perfect? Telemedicine for COVID-19. N. Engl. J. Med. 2020, 382, 1679–1681. [CrossRef]

Jakicic, J.; Otto, A.D. Physical activity considerations for the treatment and prevention of obesity–. Am. J. Clin. Nutr. 2005, 82, 226S–229S. [CrossRef]

Hsiao, M.Y.; Li, C.M.; Lu, I.S.; Lin, Y.H.; Wang, T.G.; Han, D.S. An investigation of the use of the Kinect system as a measure of dynamic balance and forward reach in the elderly. Clin. Rehabil. 2018, 32, 473–482. [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, QLD, Australia, 3–5 December 2018.

Kim, J.H.; Roberge, R.; Powell, J.; Shafer, A.; Williams, W.J. Measurement accuracy of heart rate and respiratory rate during graded exercise and sustained exercise in the heat using the Zephyr BioHarness™. Int. J. Sport. Med. 2013, 34, 497. [CrossRef]

American College of Sports Medicine. ACSM’s Resource Manual for Guidelines for Exercise Testing and Prescription; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2012.

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.

Arney, B.; Glover, R.; Fusco, A.; Cortis, C.; de Koning, J.; Erp, T.; Jaime, S.; Mikat, R.; Porcari, J.; Foster, C. Comparison of rating of perceived exertion scales during incremental and interval exercise. Kinesiology 2019, 51, 150–157. [CrossRef]

Colado, J.C.; Brasil, R.M. Concurrent and Construct Validation of a Scale for Rating Perceived Exertion in Aquatic Cycling for Young Men. J. Sport. Sci. Med. 2019, 18, 695–707.

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; SAE International: Warrendale, PA, 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]

Skiena, S.S. The Data Science Design Manual; Springer: Berlin/Heidelberg, Germany, 2017.

Berrar, D. Cross-validation. Encycl. Bioinform. Comput. Biol. 2019, 1, 542–545.

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.

Algamal, Z.Y.; Lee, M.H. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst. Appl. 2015, 42, 9326–9332. [CrossRef]

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]

Afsar, P.; Cortez, P.; Santos, H. Automatic visual detection of human behavior: A review from 2000 to 2014. Expert Syst. Appl. 2015, 42, 6935–6956. [CrossRef]

Ghaderyan, P.; Abbasi, A.; Saber, S. A new algorithm for kinematic analysis of handwriting data; towards a reliable handwritingbased tool for early detection of alzheimer’s disease. Expert Syst. Appl. 2018, 114, 428–440. [CrossRef]

Rescio, G.; Leone, A.; Siciliano, P. Supervised machine learning scheme for electromyography-based pre-fall detection system. Expert Syst. Appl. 2018, 100, 95–105. [CrossRef]

Ryu, J.; Kim, D.H. Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals. Expert Syst. Appl. 2017, 85, 357–365. [CrossRef]

Yigit, H. A weighting approach for KNN classifier. In Proceedings of the 2013 International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Turkey, 7–9 November 2013; pp. 228–231.

Erickson, B.J.; Korfiatis, P.; Akkus, Z.; Kline, T.L. Machine learning for medical imaging. Radiographics 2017, 37, 505–515. [CrossRef] [PubMed]

Madzarov, G.; Gjorgjevikj, D.; Chorbev, I. A multi-class SVM classifier utilizing binary decision tree. Informatica 2009, 33, 225–233.

Mahmon, N.A.; Ya’acob, N. A review on classification of satellite image using Artificial Neural Network (ANN). In Proceedings of the 2014 IEEE 5th Control and System Graduate Research Colloquium, Shah Alam, Malaysia, 11–12 August 2014; pp. 153–157. 97. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [CrossRef]

Dietterich, T.G. Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems; Springer: Berlin/Heidelberg, Germany, 2000; pp. 1–15

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]

McInnes, L.; Healy, J.; Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv 2018, arXiv:1802.03426.

Strassmann, A.; Steurer-Stey, C.; Dalla Lana, K.; Zoller, M.; Turk, A.J.; Suter, P.; Puhan, M.A. Population-based reference values for the 1-min sit-to-stand test. Int. J. Public Health 2013, 58, 949–953. [CrossRef] [PubMed]

Parkinson, S.; Campbell, A.; Dankaerts, W.; Burnett, A.; O’Sullivan, P. Upper and lower lumbar segments move differently during sit-to-stand. Man. Ther. 2013, 18, 390–394. [CrossRef]