Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment

Musculoskeletal injury is a common cause in manual material handling activities, where workers are exposed to repetitive picking and placing of materials, that therefore may lead to dangerous injuries if incorrect postures are made. It is the duty of factories to take care of the health conditions o...

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Main Author:	Albawab,, Tarek Mhd Moataz
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/24681/1/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf http://eprints.utem.edu.my/id/eprint/24681/2/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf
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id	my-utem-ep.24681
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Ahmad, Nadiah
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Albawab,, Tarek Mhd Moataz Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
description	Musculoskeletal injury is a common cause in manual material handling activities, where workers are exposed to repetitive picking and placing of materials, that therefore may lead to dangerous injuries if incorrect postures are made. It is the duty of factories to take care of the health conditions of their employees, and ensure the workplace is ergonomically designed. However, it is a difficult task to assess the work postures in a large number of employees all the time due to cost, lack of equipment, and lack of experience. The aim of this study is to formulate an ergonomic model to identify and classify body part motion angle ranges for upper limb postural analysis, to develop an automated real-time upper limb postural angle and classification system, and to evaluate the developed postural angle classification system using 30 participants in a lab setting and five ergonomic experts opinions. The chosen experts are individuals with experiences in ergonomics field working as academic researchers, consultancy agents, and industry management positions in Malaysia. Formulating the postural classification model applied the concepts of traffic light to categorise the work postures, where upper limb postures were classified into three classifications with mathematical models to count the number and percentage of each classification occurrence for each posture. The postural classification model was then integrated with a developed C# based software and a Microsoft Kinect sensor using heuristic approaches to do an automated real-time upper limb postural angle classification system. The developed postural classification was validated for 12 static postures, and 4 dynamic postures among 30 participants in a lab setting using Jamar goniometer (Sammons Preston Roylan, USA), a computerised protractor tool in ErgoFellow v3.0, and the statistical analysis used the root mean square error (RMSE). The evaluation was further explored by taking the ergonomic experts’ opinions through semi-structured interviews to note the needful, usefulness, applicability, effectiveness, and the details provided for the workplace. The results of validation revealed that the static postures was 7.52 RMSE, dynamic postures was 21.93 RMSE, and combined static and dynamic results was 14.48 RMSE. The study shows better mean RMSE results than Plantard et al. (2017) study by 15.6% in static phase analysis, but larger mean RMSE in dynamic analysis which might be due to the method of capturing the reference angles. The study concluded that despite the acceptable RMSE results presented by the developed system, the software architecture and detection techniques require further improvement and development for better angle measurement accuracy with added parameters for ergonomics assessment.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Albawab,, Tarek Mhd Moataz
author_facet	Albawab,, Tarek Mhd Moataz
author_sort	Albawab,, Tarek Mhd Moataz
title	Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
title_short	Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
title_full	Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
title_fullStr	Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
title_full_unstemmed	Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment
title_sort	automated postural angle classification using microsoft kinect for ergonomics assessment
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24681/1/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf http://eprints.utem.edu.my/id/eprint/24681/2/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf
_version_	1747834087086751744
spelling	my-utem-ep.246812021-10-05T11:47:35Z Automated Postural Angle Classification Using Microsoft Kinect For Ergonomics Assessment 2019 Albawab,, Tarek Mhd Moataz TA Engineering (General). Civil engineering (General) Musculoskeletal injury is a common cause in manual material handling activities, where workers are exposed to repetitive picking and placing of materials, that therefore may lead to dangerous injuries if incorrect postures are made. It is the duty of factories to take care of the health conditions of their employees, and ensure the workplace is ergonomically designed. However, it is a difficult task to assess the work postures in a large number of employees all the time due to cost, lack of equipment, and lack of experience. The aim of this study is to formulate an ergonomic model to identify and classify body part motion angle ranges for upper limb postural analysis, to develop an automated real-time upper limb postural angle and classification system, and to evaluate the developed postural angle classification system using 30 participants in a lab setting and five ergonomic experts opinions. The chosen experts are individuals with experiences in ergonomics field working as academic researchers, consultancy agents, and industry management positions in Malaysia. Formulating the postural classification model applied the concepts of traffic light to categorise the work postures, where upper limb postures were classified into three classifications with mathematical models to count the number and percentage of each classification occurrence for each posture. The postural classification model was then integrated with a developed C# based software and a Microsoft Kinect sensor using heuristic approaches to do an automated real-time upper limb postural angle classification system. The developed postural classification was validated for 12 static postures, and 4 dynamic postures among 30 participants in a lab setting using Jamar goniometer (Sammons Preston Roylan, USA), a computerised protractor tool in ErgoFellow v3.0, and the statistical analysis used the root mean square error (RMSE). The evaluation was further explored by taking the ergonomic experts’ opinions through semi-structured interviews to note the needful, usefulness, applicability, effectiveness, and the details provided for the workplace. The results of validation revealed that the static postures was 7.52 RMSE, dynamic postures was 21.93 RMSE, and combined static and dynamic results was 14.48 RMSE. The study shows better mean RMSE results than Plantard et al. (2017) study by 15.6% in static phase analysis, but larger mean RMSE in dynamic analysis which might be due to the method of capturing the reference angles. The study concluded that despite the acceptable RMSE results presented by the developed system, the software architecture and detection techniques require further improvement and development for better angle measurement accuracy with added parameters for ergonomics assessment. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24681/ http://eprints.utem.edu.my/id/eprint/24681/1/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf text en public http://eprints.utem.edu.my/id/eprint/24681/2/Automated%20Postural%20Angle%20Classification%20Using%20Microsoft%20Kinect%20For%20Ergonomics%20Assessment.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117641 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Ahmad, Nadiah 1. Abd Rahman, M. N. asrull, Abdul Rani, M. R. ebi and Rohani, J. M., 2011, WERA: an observational tool develop to investigate the physical risk factor associated with WMSDs, Journal of human ergology, 40(1–2), pp. 19–36. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-84873402164&partnerID=tZOtx3y1. 2. Abobakr, A., Nahvandi, D., Iskander, J., Hossny, M., Nahavandi, S., Smets, M., 2017a, A kinect-based workplace postural analysis system using deep residual networks, 2017 IEEE International Symposium on Systems Engineering, ISSE 2017 - Proceedings. doi: 10.1109/SysEng.2017.8088272. 3. Abobakr, A., Nahvandi, D., Iskander, J., Hossny, M., Nahavandi, S., Smets, M., 2017b, RGB-D human posture analysis for ergonomic studies using deep convolutional neural network, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2885–2890. doi: 10.1109/SMC.2017.8123065. 4. Albawab, T. M. M., Halim, I., Ahmad, N., Umar, R.Z.R., Mohamed, M.S.S., Abdullais, F., Basari, A.S.H., Bakar, M., Saptari, A., 2019, Upper Limb Joints and Motions Sampling System Using Kinect Camera, Journal of Advanced Manufacturing Technology, 12(2), pp. 147–158. 5. Alleblas, C. C. J., de Man, L., Van den Haak, L., E. Vierhout, M., Willem Jansen, F., E. Nieboer, T., 2017, Prevalence of Musculoskeletal Disorders Among Surgeons Performing Minimally Invasive Surgery, Annals of Surgery, 266(6):905–920, doi: 10.1097/SLA.0000000000002223. 6. Armstrong, T., Foulke, J., Joseph, B., 1982, Investigation of cumulative trauma disorders in a poultry processing plant, American Industrial Hygiene Association Journal, 43, pp. 103–116. 7. Asaeda, M.,.Kuwahara, W., Fujita, N., Yamasaki, T., Adachi, N., 2018, Validity of motion analysis using the Kinect system to evaluate single leg stance in patients with hip disorders, Gait and Posture. Elsevier, 62(April), pp. 458–462. doi: 10.1016/j.gaitpost.2018.04.010. 8. Bao, S., Howard, N., Spielholz, P., Silverstein, B., Polissar, N., 2009, Interrater reliability of posture observations, Human Factors, 51(3), pp. 292–309. doi: 10.1177/0018720809340273. 9. Beacon, J. F., Comeau, G., Payeur, P., Russell, D., 2017, Assessing the suitability of Kinect for measuring the impact of a week-long Feldenkrais method workshop on pianists’ posture and movement, Journal of Music, Technology and Education, 10(1), pp. 51–72. doi: 10.1386/jmte.10.1.51_1. 10. Bonnechère, B., Jansen, B., Salvia, P., Bouzahouene, H., Omelina, L., Moiseev, F., Sholukha, V., Cornelis, J., Rooze, M., Van Sint Jan, S., 2014, Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry, Gait and Posture, 39(1), pp. 593–598. doi: 10.1016/j.gaitpost.2013.09.018. 11. Brandl, C., Mertens, A. and Schlick, C. M., 2017, Effect of sampling interval on the reliability of ergonomic analysis using the Ovako working posture analysing system (OWAS), International Journal of Industrial Ergonomics. Elsevier B.V, 57, pp. 68–73. doi: 10.1016/j.ergon.2016.11.013. 12. Buchholz, B., Paquet, V., Punnett, L., Lee, D., Moir, S., 1996, PATH: A work sampling-based approach to ergonomic job analysis for construction and other non-repetitive work, Applied Ergonomics, 27(3), pp. 177–187. doi: 10.1016/0003-6870(95)00078-X. 13. Bhatia, V., Kalra, P. and Randhawa, J. S., 2019, Upper Body Postural Analysis in Sitting Workplace Environment Using Microsoft Kinect V2 Sensor, Research into Design for a Connected World. Smart Innovation, Systems and Technologies, vol 135. Springer, Singapore, doi: 10.1007/978-981-13-5974-3. 14. Chander, D. S. and Cavatorta, M. P., 2017, An observational method for Postural Ergonomic Risk Assessment (PERA), International Journal of Industrial Ergonomics. Elsevier B.V, 57, pp. 32–41. doi: 10.1016/j.ergon.2016.11.007. 15. Chang, R., Guan, L. and Burneoe, J. A., 2000, An automated form of video image analysis applied to classification of movement disorders, Journal of Disability and Rehabilitation, vol. 22, no. 1/2, pp. 97–108. 16. Choppin, S., Lane, B. and Wheat, J., 2014, The accuracy of the Microsoft Kinect in joint angle measurement, Sports Technology, 7(1–2), pp. 98–105. doi: 10.1080/19346182.2014.968165. 17. Clark, R. A., Pua, Y. H., Fortin, K., Ritchie, C., Webster, K. E., 2012, Validity of the Microsoft Kinect for assessment of postural control, Gait and Posture. Elsevier B.V., 36(3), pp. 372–377. doi: 10.1016/j.gaitpost.2012.03.033. 18. Collins, J. D. and Sullivan, L. W. O., 2015, Musculoskeletal disorder prevalence and psychosocial risk exposures by age and gender in a cohort of office based employees in two academic institutions, International Journal of Industrial Ergonomics, Elsevier, 46, 85-97, doi: 10.1016/j.ergon.2014.12.013. 19. Cristani, M., Raghavendra, R., Bue, A. D., Murino, V., 2013, Human behavior analysis in video surveillance: A Social Signal Processing perspective, Neurocomputing. Elsevier, 100, pp. 86–97. doi: 10.1016/j.neucom.2011.12.038. 20. Cruz, L., Lucio, D. and Velho, L., 2012, Kinect and RGBD images: Challenges and applications, Proceedings: 25th SIBGRAPI - Conference on Graphics, Patterns and Images Tutorials, SIBGRAPI-T 2012, pp. 36–49. doi: 10.1109/SIBGRAPI-T.2012.13. 21. Dul, J., Bruder, R., Buckle, P., Carayon, P., Falzon, P., S. Marras, W., R. Wilson, J., van der Doelen, B., 2012, A strategy for human factors/ergonomics: developing the discipline and profession, Ergonomics, 55(4), pp. 377–395. doi: 10.1080/00140139.2012.661087. 22. Diego-Mas, J. A. and Alcaide-Marzal, J., 2014a, Using Kinect sensor in observational methods for assessing postures at work, Applied Ergonomics. Elsevier Ltd, 45(4), pp. 976–985. doi: 10.1016/j.apergo.2013.12.001. 23. Diego-Mas, J. A. and Alcaide-Marzal, J., 2014b, Using KinectTM sensor in observational methods for assessing postures at work, Applied Ergonomics. Elsevier Ltd, 45(4), pp. 976–985. doi: 10.1016/j.apergo.2013.12.001. 24. Du, Y. C., Shih, C. B., Fan, S. C., Lin, H. T., Chen, P. J., 2018, An IMU-compensated skeletal tracking system using Kinect for the upper limb, Microsystem Technologies. Springer Berlin Heidelberg, 24(10), pp. 4317–4327. doi: 10.1007/s00542-018-3769-6. 25. Dzeng, R. J., Hsueh, H. H. and Ho, C. W., 2017, Automated Posture Assessment for construction workers, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings, pp. 1027–1031. doi: 10.23919/MIPRO.2017.7973575. 26. Elmadany, N. E. D., He, Y. and Guan, L., 2018, Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis, IEEE Transactions on Image Processing. IEEE, 27(11), pp. 5275–5287. doi: 10.1109/TIP.2018.2855438. 27. Epstein, S., Sparer, H., Tran, B. N., Ruan, Q. Z., Dennerlein, J. T., Singhal, D., Lee, B. T., 2018, Prevalence of work-related musculoskeletal disorders among surgeons and interventionalists: A systematic review and meta-analysis, JAMA Surgery, 153(2), pp. 1–11. doi: 10.1001/jamasurg.2017.4947. 28. Etemad, S. A. and Wainer, G. A., 2010 DEVS-based Modeling of a Human Motion Data Synthesis and Control System, Proceedings of the 2010 Summer Computer Simulation Conference, pp. 469–474. Available at: http://dl.acm.org/citation.cfm?id=1999416.1999477. 29. Fazi, H. M., Mohamed, N. M. Z. N., Rashid, M. F. F. A., & Rose, A. N. M., 2016, Ergonomics study for workers at food production industry, MATEC Web of Conferences, 90, 0–6. https://doi.org/10.1051/matecconf/20179001003 30. Fernández-Baena, A., Susín, A. and Lligadas, X., 2012, Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments, Proceedings of the 2012 4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012, pp. 656–661. doi: 10.1109/iNCoS.2012.66. 31. Fýðlalý, N., Cihan, A., Esen, H., Figlali, Alpaslan, Cecmeci, D., Gullu, M. K., Yilmaz, M. K., 2015, Image processing-aided working posture analysis: I-OWAS, Computers and Industrial Engineering, 85, pp. 384–394. doi: 10.1016/j.cie.2015.03.011. 32. Fransson-Hall, C., Gloria, R., Kilbom, A., Winkel, J., 1995, A portable ergonomic observation method (PEO) for computerized on-line recording of postures and manual handling, Applied Ergonomics, 26(2), pp. 93–100. doi: 10.1016/0003-6870(95)00003-U. 33. Geerse, D. J., Coolen, B. H. and Roerdink, M., 2015, Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments, PloS one, 10(10), p. e0139913. doi: 10.1371/journal.pone.0139913. 34. Genaidy, A. M., Al-Shedi, A. A. and Karwowski, W., 1994, Postural stress analysis in industry, Applied Ergonomics, 25(2), pp. 77–87. doi: 10.1016/0003-6870(94)90068-X. 35. González, A., Hayashibe, M., Bonnet, V., Fraisse, P., 2014, Whole body center of mass estimation with portable sensors: Using the statically equivalent serial chain and a kinect, Sensors (Switzerland), 14(9), pp. 16955–16971. doi: 10.3390/s140916955. 36. Han, J., Shao, L., Xu, D., Shotton, J., 2013, Enhanced computer vision with Microsoft Kinect sensor: A review, IEEE Transactions on Cybernetics, 43(5), pp. 1318–1334. doi: 10.1109/TCYB.2013.2265378. 37. Hogg, R. V., Tanis, E., Zimmerman, D., 2003, Probability and Statistical Inference (Ninth Edition), United States of America: Pearson. 38. Heberger, J. R., Nazarwanji, M. F., Paquet, V., Pollard, J. P., Dempsey, P. G., 2012, Inter-rater reliability of video-based ergonomic job analysis for maintenance work in mineral processing and coal preparation plants, Proceedings of the Human Factors and Ergonomics Society, (December 2014), pp. 2368–2372. doi: 10.1177/1071181312561512. 39. Hecklau, F., Galeitzke, M., Flachs, S., Kohl, H., 2016, Holistic Approach for Human Resource Management in Industry 4.0, Procedia CIRP. The Author(s), 54, pp. 1–6. doi: 10.1016/j.procir.2016.05.102. 40. Hignett, S., McAtamney, L., 2000, Rapid Entire Body Assessment (REBA), Applied Ergonomics, 31(2), pp. 201–205. doi: 10.1016/S0003-6870(99)00039-3. 41. Huber, M., Leeser, M., Sternad, D., 2015, ‘Accuracy of kinect for measuring shoulder joint angles in multiple planes of motion’, International Conference on Virtual Rehabilitation, ICVR, pp. 170–171. doi: 10.1109/ICVR.2015.7358612. 42. Huber, M. E., Leeser, M., Sternad, D., 2014, Validity and reliability of kinect for measuring shoulder joint angles, Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC, 2014-Decem, pp. 5–6. doi: 10.1109/NEBEC.2014.6972818. 43. Jiang, S., Liu, P., Fu, D., Xue, Y., Luo, W., Wang, M., 2017, A low-cost rapid upper limb assessment method in manual assembly line based on somatosensory interaction technology, in AIP Conference Proceedings. doi: 10.1063/1.4981575. 44. Joseph Hamill, Kathleen M. Knutzen, T. R. D., 2015, Biomechanical Basis of Human Movement, Climate Change 2013 - The Physical Science Basis. doi: 10.1017/CBO9781107415324.004. 45. Juul-Kristensen, B., Fallentin, N. and Ekdahl, C., 1997, Criteria for classification of posture in repetitive work by observation methods: A review, International Journal of Industrial Ergonomics, 19(5), pp. 397–411. doi: 10.1016/S0169-8141(96)00013-3. 46. Karhu, O., Kansi, P. and Kuorinka, I., 1977, Correcting working postures in industry: A practical method for analysis, Applied Ergonomics, 8(4), pp. 199–201. doi: 10.1016/0003-6870(77)90164-8. 47. Kennedy, C. A., Amick, B. C., Dennerlein, J. T., Brewer, S., Catli, S., Williams, R., Serra, C., Gerr, F., Irvin, E., Fanzblau, Q. M., Eerd, D. V., Evanoff, B., Rempel, D., 2009, Systematic review of the role of occupational health and safety interventions in the prevention of upper extremity musculoskeletal symptoms, signs, disorders, injuries, claims and lost time, Journal of Occupational Rehabilitation, pp. 127–162. doi: 10.1007/s10926-009-9211-2. 48. Keyserling, M. W., 1986, Postural analysis of the trunk and shoulders in simulated real time, Ergonomics, 29(4), pp. 569–583. doi: 10.1080/00140138608968292. 49. Keyserling, W. M., Stetson, D. S., Silverstein, B. A., Brouwer, M. L., 1993, A checklist for evaluating ergonomic risk factors associated with upper extremity cumulative trauma disorders, Ergonomics, 36(7), pp. 807–831. doi: 10.1080/00140139308967945. 50. Kilbom, A., 1994, Assessment of physical exposure in relation to work-related musculoskeletal disorders--what information can be obtained from systematic observations?, Scandinavian Journal of Work, Environment and Health, vol. 20. 51. Kramer, J., Parker, M., Castro, D., Burrus, N., Echtler, F., 2012, Hacking the Kinect, Apress, San Diego, CA, USA, doi: 10.1007/978-1-4302-3868-3. 52. Lop, N. S., Kamar, I. F. M., Aziz, M. N. A., Abdullah, L., Akhir, N. M., 2017, Work-related to musculoskeletal disorder amongst Malaysian construction trade workers: Bricklayers, AIP Conference Proceedings, 1891, https://doi.org/10.1063/1.5005420 53. Lehrmann, A. M., Gehler, P. V and Nowozin, S., 2014, Efficient Nonlinear Markov Models for Human Motion, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (1), pp. 1314–1321. doi: 10.1109/CVPR.2014.171. 54. Mahmood, S., Syed Abdul Aziz, S. A. H., Marsi, N., Zulkifli, M. Z., 2019,. Posture assement for workers repititive tasks at poultry feed manufacturing industry, Science Proceedings Series, 1(2), pp. 28–31. 55. Mokhtar, M. M., Md Deros, B., Sukadarin, E. H., 2013, Evaluation of Musculoskeletal Disorders Prevalence during Oil Palm Fresh Fruit Bunches Harvesting Using RULA, Advanced Engineering Forum 10, pp. 110–115, doi: 10.4028/www.scientific.net/aef.10.110 56. Magenheim, J., Nelles, W., Rhode, T., Schaper, N., Schubert, S., Stechert, P., 2010,. Competencies for informatics systems and modeling: Results of qualitative content analysis of expert interviews, IEEE EDUCON 2010 Conference, doi: 10.1109/educon.2010.5492535. 57. Manghisi, V. M., Uva, A. E., Fiorentino, M., Bevliacqua, V., Trotta, G. F., Monno, G., 2017, Real time RULA assessment using Kinect v2 sensor, Applied Ergonomics, Elsevier Ltd, 65, pp. 481–491. doi: 10.1016/j.apergo.2017.02.015. 58. Marcum, J., Adams, D., 2017, Work-related musculoskeletal disorder surveillance using the Washington state workers’ compensation system: Recent declines and patterns by industry, 1999-2013, American Journal of Industrial Medicine, 60(5), pp. 457–471. doi: 10.1002/ajim.22708. 59. Mariño, C., Santana, R., Vargas, J., Morales, L., Cisneros, L., 2019, Reliability and Validity of Postural Evaluations with Kinect v2 Sensor Ergonomic Evaluation System, Conference Information and Communication Technologies of Ecuador (TIC.EC), 60. Riobamba, 2018, pp. 86-99. 61. Mathiassen, S. E., Wahlström, J. and Forsman, M., 2012, Bias and imprecision in posture percentile variables estimated from short exposure samples, BMC Medical Research Methodology, 12. doi: 10.1186/1471-2288-12-36. 62. Mcatamney, L. and Corlett, E. N., 1993, RULA: a survey method for the investigation of world-related upper limb disorders, Applied Ergonomics, 24(2), pp. 91–99. doi: 10.1016/0003-6870(93)90080-S. 63. Mentiplay, B. F., Hasanki, K., Perraton, L. G., Pua, Y. H., Charlton, P. C., Clark, R. A., 2018, Three-dimensional assessment of squats and drop jumps using the Microsoft Xbox One Kinect: Reliability and validity, Journal of Sports Sciences. Routledge, 36(19), pp. 2202–2209. doi: 10.1080/02640414.2018.1445439. 64. Mgbemena, C. E., Oyekan, J., Hutabarat, W., Xu, Yuchun, Tiwari, A., 2017, Design and implementation of ergonomic risk assessment feedback system for improved work posture assessment, Theoretical Issues in Ergonomics Science. Taylor & Francis, (October), pp. 1–25. doi: 10.1080/1463922X.2017.1381196. 65. Mohan, V., Justine, M., Jagannathan, M., Bt Aminudin, S., Bt Johari, S. H., 2015, Preliminary study of the patterns and physical risk factors of work-related musculoskeletal disorders among academicians in a higher learning institute, Journal of Orthopaedic Science, 20(2), pp. 410–417, doi: 10.1007/s00776-014-0682-4 66. Mohamed, A. N., 2015, A Novice Guide towards Human Motion Analysis and Understanding, CoRR, abs/1509.0, pp. 1–35., Available at: http://arxiv.org/abs/1509.01074. 67. Mortazavi, F. and Nadian-Ghomsheh, A., 2018, Stability of Kinect for range of motion analysis in static stretching exercises, PLoS ONE, 13(7). doi: 10.1371/journal.pone.0200992. 68. Nur, N. M., Dawal, S. Z., Dahari, M., 2014, The Prevalence of Work Related 69. Musculoskeletal Disorders Among Workers Performing Industrial Repetitive Tasks in the Automotive Manufacturing Companies., Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management, Bali, Indonesia, pp. 1–8. 70. Neale, I. M., 1988, First generation expert systems: A review of knowledge acquisition methodologies, The Knowledge Engineering Review, 3(2), pp. 105–145, doi: 10.1017/S0269888900004288 71. Negin, F., Rodriguez, P., Koperski, M., Kerboua, A., Gonzalez, J., Bourgeois, J., Chapoulie, E., Robert, P., Bremond, F., 2018, PRAXIS: Towards automatic cognitive assessment using gesture recognition, Expert Systems with Applications, 106, pp. 21–35. doi: 10.1016/j.eswa.2018.03.063. 72. Neumann, W. P., Wells, R.P., Norman, R. W., Frank, J., Shannon, H., Kerr, M.S., 2001, A posture and load sampling approach to determining low-back pain risk in occupational settings, International Journal of Industrial Ergonomics, 27(2), pp. 65–77. doi: 10.1016/S0169-8141(00)00038-X. 73. Occhipinti, E., 1998, OCRA: A concise index for the assessment of exposure to repetitive movements of the upper limbs, Ergonomics, 41(9), pp. 1290–1311. doi: 10.1080/001401398186315. 74. Palma, C., Salazar, A. and Vargas, F., 2016, HMM and DTW for evaluation of therapeutical gestures using kinect, Available at: http://arxiv.org/abs/1602.03742. 75. Persson, J. and Kilbom, A., 1983, VIRA - en enkel videofilmningsteknik för registrering och analys av arbetsställningar och rörelser (VIRA - a simple video-film technique for recording and analysis of work postures and work movements), National Board of Occupational Safety and Health, Vol. 10, Stockholm Sweden. 76. Pfeiffer, S., 2016, Robots, Industry 4.0 and Humans, or Why Assembly Work Is More than Routine Work, Societies, 6(2), p. 16. doi: 10.3390/soc6020016. 77. Plantard, P., Muller, A., Pontonnier, C., Dumont, G., Shum, H. P.H., Multon, F., 2017, 78. Inverse dynamics based on occlusion-resistant Kinect data: Is it usable for ergonomics?, International Journal of Industrial Ergonomics. Elsevier B.V, 61, pp. 71–80. doi: 10.1016/j.ergon.2017.05.010. 79. Plantard, P., Auvinet, E., Pierres, A.S. L., Multon, F., 2017, Validation of an ergonomic assessment method using Kinect data in real workplace conditions, Applied Ergonomics. Elsevier Ltd, 65, pp. 562–569. doi: 10.1016/j.apergo.2016.10.015. 80. Plantard, P., Auvinet, E., Pierres, A.S. L., Multon, F., 2015, Pose estimation with a kinect for ergonomic studies: Evaluation of the accuracy using a virtual mannequin, Sensors (Switzerland), 15(1), pp. 1785–1803, doi: 10.3390/s150101785. 81. Palma, C., Salazar, A. and Vargas, F., 2016, HMM and DTW for evaluation of therapeutical gestures using kinect, Available at: http://arxiv.org/abs/1602.03742. 82. Radwin, R. G., Lee, S., Li, K., Lieblich, M., Park, B.K. D., 2016, Discussion panel on computer vision and occupational ergonomics, Proceedings of the Human Factors and Ergonomics Society, pp. 957–959, doi: 10.1177/1541931213601220. 83. Roman-Liu, D., 2014, Comparison of concepts in easy-to-use methods for MSD risk assessment, Applied Ergonomics. Elsevier Ltd, 45(3), pp. 420–427. doi: 10.1016/j.apergo.2013.05.010. 84. Singh, J., Lal, H., Kocher, G., 2012, Musculoskeletal Disorder Risk Assessment in small scale forging Industry by using RULA Method, International Journal of Engineering and Advanced Technology (IJEAT), 1(5), pp. 513–518. 85. Schaub, K., Caragnano, G., Britzke, B., Bruder, R., 2013, The European Assembly Worksheet, Theoretical Issues in Ergonomics Science, 14(6), pp. 616–639. doi: 10.1080/1463922X.2012.678283. 86. Schlagenhauf, F., Sreeram, S. and Singhose, W., 2018, Comparison of Kinect and Vicon Motion Capture of Upper-Body Joint Angle Tracking, IEEE International Conference on Control and Automation (ICCA), pp. 674–679. doi: 10.1109/ICCA.2018.8444349. 87. Sedighi Maman, Z., Yazdi, M. A. A., Cavuoto, L. A., Megahed, F. M., 2017, A data-driven approach to modeling physical fatigue in the workplace using wearable sensors, Applied Ergonomics. Elsevier Ltd, 65, pp. 515–529, doi: 10.1016/j.apergo.2017.02.001. 88. Shakir, A., Shafii, N. Z., Kamarudin, M. K., Amin, N. A., 2018, An ergonomic assessment of musculoskeletal disorders among airport bag handlers: A case study in Malaysia, International Journal of Research in Pharmaceutical Sciences, (January 2019), pp. 83–87. doi: 10.26452/ijrps.v9iSPL2.1746. 89. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., 2013, Real-time human pose recognition in parts from single depth images, Studies in Computational Intelligence, 411, pp. 119–135. doi: 10.1007/978-3-642-28661-2_5. 90. Souza, A. de M. e. and Stemmer, M. R., 2018, Extraction and Classification of Human Body Parameters for Gait Analysis, Journal of Control, Automation and Electrical Systems, Springer US, 29(5), pp. 586–604. doi: 10.1007/s40313-018-0401-z. 91. Stack, T., Ostrom, L. T. and Wilhelmsen, C. A., 2016, Elements of Ergonomics Programs, Occupational Ergonomics, pp. 121–162. doi: 10.1002/9781118814239.ch6. 92. Stanson, N., Hedge, A., Brookhuis, K., Salas, E., Hendrick, H., 2005, Handbook of Human Factors and Ergonomics Methods, CRC Press., Boca Raton, USA, pp. 42-1–42-8. 93. Takala, E. P., Pehkonen, I., Forsman, M., Hansson, G. Å., Mathiassen, S. E., Neumann, W. P., Winkel, J., 2010, Systematic evaluation of observational methods assessing biomechanical exposures at work, Scandinavian Journal of Work, Environment and Health, 36(1), pp. 3–24. https://doi.org/10.5271/sjweh.2876 94. Tarabini, M., Marinoni, M., Mascetti, M., Marzaroli, P., Corti, F., Giberti, H., Mascagni, P., Villa, A., Eger, T., 2019, Real-Time Monitoring of the Posture at the Workplace Using Low Cost Sensors, Springer International Publishing, 827, pp. 678–688. doi: 10.1007/978-3-319-96098-2. 95. Teeravarunyou, S., 2014, Development of Computer Aided Posture Analysis for Rapid Upper Limb Assessment with Ranged Camera, 3rd South East Asian Network of Ergonomics Societies International Conference, 1, pp. 1–6. 96. Timmi, A., Coates, G., Fortin, K., Ackland, D., Bryant, A. L., Gordon, I., Pivonka, P., 2018, Accuracy of a novel marker tracking approach based on the low-cost Microsoft Kinect v2 sensor, Medical Engineering and Physics. Elsevier Ltd, 59, pp. 63–69. doi: 10.1016/j.medengphy.2018.04.020. 97. UK Department of Health and Safety Executive, 2018, The Health and Safety Executive Annual Report and Accounts 2017/18 in Great Britain, Available at: https://www.gov.uk/government/publications/. 98. UK Department of Health and Safety Executive, 2017, Work-related Musculoskeletal Disorders (WRMSDs) Statistics in Great Britain, Available at: www.hse.gov.uk/statistics/. 99. Valdivia, S., Blanco, R., Uribe-Quevedo, A., Penuela, L., Rojas, D., Kaparlos, B., 2018, Development and evaluation of two posture-tracking user interfaces for occupational health care, Advances in Mechanical Engineering, 10(6), pp. 1–12. doi: 10.1177/1687814018769489. 100. Vignais, N., Miezal, M., Bleser, G., Mura, K., Gorecky, D., Marin, F., 2013, Innovative system for real-time ergonomic feedback in industrial manufacturing, Applied Ergonomics. Elsevier Ltd, 44(4), pp. 566–574. doi: 10.1016/j.apergo.2012.11.008. 101. Village, J., Trask, C., Luong, N., Chow, Y., Johnson, P., Koehoorn, M., Teschke, K., 2009, Development and evaluation of an observational Back-Exposure Sampling Tool (Back-EST) for work-related back injury risk factors, Applied Ergonomics. Elsevier Ltd, 40(3), pp. 538–544. doi: 10.1016/j.apergo.2008.09.001. 102. van der Beek, A. J., van Gaalen, L. C. and Frings-Dresen, M. H. W., 1992, Working postures and activities of lorry drivers: a reliability study of on-site observation and recording on a pocket computer, Applied Ergonomics, 23(5), pp. 331–336. 103. Wang, X., Dong, X. S., Choi, S. D., Dement, J., 2016, Work-related musculoskeletal disorders among construction workers in the United States from 1992 to 2014, pp. 1–7. doi: 10.1136/oemed-2016-103943. 104. Weerasinghe, I. P. T., Ruwanpura, J. Y., Boyd, J. E., Habib, A. F., 2012, Application of Microsoft Kinect Sensor for Tracking Construction Workers, Construction Research Congress 2012 © ASCE 2012, pp. 858–867. doi: 10.1061/9780784412329.087. 105. Whysall, Z., Haslam, C. and Haslam, R., 2006, A stage of change approach to reducing occupational ill health, Preventive Medicine, 43(5), pp. 422–428. doi: 10.1016/j.ypmed.2006.07.004. 106. Wilson, J. R., 2000, Fundamentals of ergonomics in theory and practice, Applied Ergonomics, 31(6), pp. 557–567. doi: 10.1016/S0003-6870(00)00034-X. 107. Wolf, S., Loose, T., Schablowski, M., Doderlein, L., Rupp, R., Gerner, H. J., Bretthauer, G., Mikut, R., 2006, Automated feature assessment in instrumented gait analysis, Gait and Posture, 23(3), pp. 331–338. doi: 10.1016/j.gaitpost.2005.04.004. 108. Zare, M., Sagot, J.-C. and Roquelaure, Y., 2018, Within and between Individual Variability of Exposure to Work-Related Musculoskeletal Disorder Risk Factors, International Journal of Environmental Research and Public Health, 15(5), p. 1003. doi: 10.3390/ijerph15051003. 109. Zhu, X., Yurteri-Kapalan L. A., Cavuoto, L. A., Sokol, A. I., Iglesia, C. B., Gutman, R. E., Park, A. J., Paquet, V., 2017, ErgoPART: A Computerized Observational Tool to Quantify Postural Loading in Real-Time During Surgery, IISE Transactions on Occupational Ergonomics and Human Factors, 5(1), pp. 23–38. doi: 10.1080/24725838.2016.1276032.

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