Modeling the Multi-Dimensional Phenomenon of Fatiguing by Assessing the Perceived Whole Body Fatigue and Local Muscle Fatigue During Squat Lifting
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.
Abstract
Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.
참고문헌 (37)
- Ahmad, I., "Assessment of whole body and local muscle fatigue using electromyography and a perceived exertion scale for squat lifting", International Journal of Environmental Research and Public Health, 15(4), pp.784~ (2018)
- Al-Mulla, M. R., "A review of non-invasive techniques to detect and predict localised muscle fatigue", Sensors, 11(4), pp.3545~3594 (2011)
- Al-shair, K., "The Effect of Fatigue and Fatigue Intensity on Exercise Tolerance in Moderate COPD", Lung, 194, pp.889~895 (2016)
- Artiga Gonzalez, A., "Kinetic analysis of oxygen dynamics under a variable work rate", Human Movement Science (2017)
- Barron, P. J., "The effect of pitched and vertical ladder ergometer climbing on cardiorespiratory and psychophysical variables", Applied Ergonomics, 66, pp.172~176 (2018)
- Borg, G., "Psychophysical scaling with applications in physical work and the perception of exertion", Scandinavian Journal of Work, Environment and Health, 16, pp.55~58 (1990)
- Borg, G, "Physical performance and perceived exertion" (1962), Gleerup Lund
- De Jesus, I. R., "Principal components of frequency domain electromyograms for muscular fatigue analysis", Conf Proc IEEE Eng Med Biol Soc, 2010, pp.3519~3522 (2010)
- Ghofrani, M., "Reliability of SEMG measurements for trunk muscles during lifting variable loads in healthy subjects", Journal of Bodywork and Movement Therapies, 21(3), pp.711~718 (2017)
- González-Izal, M., "EMG spectral indices and muscle power fatigue during dynamic contractions", Journal of Electromyography and Kinesiology, 20(2), pp.233~240 (2010)
- Haddad, M., "Influence of fatigue, stress, muscle soreness and sleep on perceived exertion during submaximal effort", Physiology and Behavior, 119, pp.185~189 (2013)
- Hafeez, M., "Development of a Diagnostic Algorithm to Identify Psycho-Physiological Game Addiction Attributes Using Statistical Parameters", IEEE Access, 5, pp.22443~22452 (2017)
- Iodice, P., "Fatigue increases the perception of future effort during decision making", Psychology of Sport and Exercise, 33, pp.150~160 (2017)
- Karthick, P. A., "Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms", Computer Methods and Programs in Biomedicine, 154, pp.45~56 (2018)
- Kim, J.-Y., "The Measurement of Sensitivity of Numerical Parameters in Quantification of Local Muscle Fatigue", Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 42(12), pp.936~939 (1998)
- Kim, S., "Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks", Ergonomics, 56(2), pp.314~326 (2013)
- Kuthe, C. D., "Surface electromyography based method for computing muscle strength and fatigue of biceps brachii muscle and its clinical implementation", Informatics in Medicine Unlocked, 12, pp.34~43 (2018)
- Looft, J. M., "Modification of a three-compartment muscle fatigue model to predict peak torque decline during intermittent tasks", Journal of Biomechanics, 77, pp.16~25 (2018)
- Ma, L., "A new simple dynamic muscle fatigue model and its validation", International Journal of Industrial Ergonomics, 39(1), pp.211~220 (2009)
- Marri, K., "Classification of muscle fatigue using surface electromyography signals and multifractals", 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD, pp.669~674 (2015), FSKD
- Merletti, R., "Surface electromyography :physiology, engineering and applications" (2016), John Wiley & Sons
- 박성하, "목 자세에 따른 선 자세에서의 신체균형능력 평가", 한국산업경영시스템학회지, 41(2), pp.16~23 (2018), 한국산업경영시스템학회[DOI]
- 박성하, "전신 피로와 시야 제한이 자세균형 능력에 미치는 영향", 한국산업경영시스템학회지, 39(3), pp.39~46 (2016), 한국산업경영시스템학회
- Plews, D. J., "Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and Electrocardiogram Methods", Int J Sports Physiol Perform, 12(10), pp.1~17 (2017)
- Razanskas, P., "Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing", Biomedical Signal Processing and Control, 35, pp.19~29 (2017)
- Ribeiro, D. C., "The initial effects of a sustained glenohumeral postero-lateral glide during elevation on shoulder muscle activity : A repeated measures study on asymptomatic shoulders", Manual Therapy, 22(1), pp.101~108 (2016)
- OSH in Figures : Work-Related Musculoskeletal Disorders in the EU-Facts and Figures, "Safety, E. A. F.", European Risk Observatory, ~ (2010)
- Sedighi Maman, Z., "A data-driven approach to modeling physical fatigue in the workplace using wearable sensors", Appl. Ergon., 65, pp.515~529 (2017)
- "SENIAM" (2017)[URL]
- Shair, E., "EMG Processing Based Measures of Fatigue Assessment during Manual Lifting", BioMed research international, 2017 (2017)
- Shin, H.-J., "Measurement of trunk muscle fatigue during dynamic lifting and lowering as recovery time changes", International Journal of Industrial Ergonomics, 37, pp.545~551 (2007)
- Smets, E. M. A., "The multidimensional Fatigue Inventory (MFI)psychometric qualities of an instrument to assess fatigue", Journal of Psychosomatic Research, 39, pp.315~325 (1995)
- Son, C., "Detection of muscle activation through multi-electrode sensing using electrical stimulation", Sensors and Actuators A : Physical, 275, pp.19~28 (2018)
- Tetrault, J. M., "Reporting of multivariable methods in the medical literature", Journal of Investigative Medicine, 56, pp.954~957 (2008)
- Yavuz, H. U., "Kinematic and electromyographic activity changes during back squat with submaximal and maximal loading", Applied bionics and biomechanics (2017)
- Yung, M., "Fatigue at the Workplace : Measurement and Temporal Development", pp.2016
- Zhou, W., "Asymmetric Fuzzy Preference Relations Based on the Generalized Sigmoid Scale and Their Application in Decision Making Involving Risk Appetites", IEEE Transactions on Fuzzy Systems, 24(3), pp.741~756 (2016)