Elle Kaldırma Hareketinde Kutu Boyutunun L5/S1 Eklemine Etkisinin İncelenmesi

Öz

Günlük hayatta ve çalışma koşullarında elle nesne kaldırma eylemi yaygın olarak uygulanmaktadır. Elle nesne kaldırma eylemi insan vücut eklemlerinde çeşitli yüklenmeler oluşturmaktadır. Araştırmalara göre özellikle bel bölgesi kaldırma hareketinden en çok etkilenen bölgedir ve kaldırma hareketi sırasında bel bölgesindeki omurlar incelendiğinde, moment kolunun en yüksek olduğu L5/S1 eklemindeki yüklenme en yüksektir. Elle nesne kaldırma hareketinde L5/S1 eklemine etkisinin incelendiği araştırmalarda, kaldırma hareketi nesnenin iki yanındaki nesne tabanından belirli yükseklikteki tutamaçlardan tutularak gerçekleştirilmektedir. Bu çalışmada katılımcılar elle kaldırma hareketini önceki çalışmalardan farklı olarak kutuların altından kavrayarak gerçekleştirmişlerdir. Deneylerde katılımcıların 16 kg kütleli ve 3 farklı ebattaki kutuların 2 farklı kaldırma tekniği olan çömelerek ve eğilerek kaldırma hareketi sırasında L5/S1 ekleminde oluşan yüklerin değerlendirilmesi yapılmıştır. Kaldırma hareketleri Microsoft Kinect v2 kamerayla kaydedilmiştir. Kutuların elle kaldırma hareketinin L5/S1 eklemine etkisini incelemek amacıyla OpenSim biyomekanik model yazılımı kullanılmıştır. Kullanılan biyomekanik modele aktarılan insan vücut eklemleri konum verilerinin ters kinematik analiziyle eklem açıları, ters dinamik analiziyle ise eklem torkları elde edilmiştir. Daha sonra eklem reaksiyon analizi yapılarak L5/S1 eklemine binen yükler değerlendirilmiştir. Deneyler sonucunda küçük, orta ve büyük kutularda çömelerek kaldırma hareketinde L5/S1 eklemine etkiyen kompresyon kuvvetleri eğilerek kaldırmaya göre %8.8-9.1-9.6 oranında artış, eğilerek kaldırma hareketinde L5/S1 eklemine etkiyen kesme kuvvetleri çömelerek kaldırmaya göre %24.5-25.7-27.4 oranında artış göstermiştir.

Anahtar Kelimeler

• Kaldırma teknikleri
• Kinectv2
• Kas-iskelet sistemi
• OpenSim

Investigation of the Effect of Box Size on L5/S1 Joint in Manual Lifting

Abstract

Manual lifting is widely practiced in daily life and working conditions. The act of lifting objects by hand creates various loads on the human body joints. According to researches, especially the lumbar region is the region most affected by the lifting movement, and when the vertebrae in the lumbar region are examined during the lifting movement, the load on the L5/S1 joint, where the moment arm is the highest, is the highest. In studies examining the effect of manual object lifting on the L5/S1 joint, the lifting movement is carried out by holding the handles at a certain height from the object base on both sides of the object. In this study, the participants performed the manual lifting movement by grasping the bottom of the boxes, unlike previous studies. In the experiments, the loads formed on the L5/S1 joint were evaluated during the lifting movement of the participants by crouching and bending, which are 2 different lifting techniques of boxes of 16 kg mass and 3 different sizes. Lifting movements were recorded with the Microsoft Kinect v2 camera. OpenSim biomechanical model software was used to examine the effect of manual lifting of the boxes on the L5/S1 joint. Joint angles were obtained by inverse kinematic analysis of the position data of human body joints transferred to the used biomechanical model, and joint torques were obtained by inverse dynamic analysis. Afterwards, joint reaction analysis was performed and loads on the L5/S1 joint were evaluated. As a result of the experiments, the compression forces acting on the L5/S1 joint increased by 8.8-9.1-9.6% in the squat lifting movement in small, medium and large boxes compared to the stoop lift, and the shear forces acting on the L5/S1 joint in the stoop lifting movement increased by 24.5-25.7-27.4% compared to the squat lifting.

Keywords

• Lifting techniques
• Kinectv2
• Musculoskeletal system
• OpenSim

Kaynakça

1. Bazrgari B., Shirazi-Adl A., and Arjmand N., Analysis of squat and stoop dynamic liftings: Muscle forces and internal spinal loads, European Spine Journal 16(5), 687–699, 2007.
2. Beaucage-Gauvreau E., Robertson W.S.P., Brandon S.C.E., Fraser R., Freeman B.J.C., Graham R.B., Thewlis D., Jones C.F., Validation of an OpenSim full-body model with detailed lumbar spine for estimating lower lumbar spine loads during symmetric and asymmetric lifting tasks, Comput. Methods Biomech. Biomed. Engin. 22(5), 451–464, 2019.
3. Bruno A.G., Bouxsein M.L., Anderson D.E., Development and validation of a musculoskeletal model of the fully articulated thoracolumbar spine and rib cage, J. Biomech. Eng., 137(8), 1–10, 2015.
4. Bureau of Labor Statistics, Industry Injury and Illness Data, 2019.
5. Delp S.L., Anderson F.C., Arnold A.S., Loan P., Habib A., John C.T., Guendelman E., Thelen D. G., OpenSim: Open-source software to create and analyze dynamic simulations of movement, IEEE Trans. Biomed. Eng. 54(11), 1940–1950, 2007.
6. Dreischarf M., Rohlmann A., Graichen F., Bergmann G., Schmidt H., In vivo loads on a vertebral body replacement during different lifting techniques, J. Biomech. 49(6), 890– 895, 2016.
7. Faber G.S., Kingma I., Bakker A.J.M., van Dieën J.H., Low-back loading in lifting two loads beside the body compared to lifting one load in front of the body, J. Biomech. 42(1), 35–41, 2009.
8. Faber G.S., Kingma I., Chang C.C., Dennerlein J.T., van Dieën J.H., Validation of a wearable system for 3D ambulatory L5/S1 moment assessment during manual lifting using instrumented shoes and an inertial sensor suit, J. Biomech. 102, 2020.

9. Gholipour A., Arjmand N., Artificial neural networks to predict 3D spinal posture in reaching and lifting activities; Applications in biomechanical models, J. Biomech., 49(13), 2946–2952, 2016.
10. Hwang S., Kim Y., Kim Y., Lower extremity joint kinetics and lumbar curvature during squat and stoop lifting, BMC Musculoskelet. Disord. 10(1), 1–10, 2009.
11. Jia B., Kim S., and Nussbaum M. A., An EMG-based model to estimate lumbar muscle forces and spinal loads during complex, high-effort tasks: Development and application to residential construction using prefabricated walls, Int. J. Ind. Ergon. 41(5), 437–446, 2011.
12. Khoddam-Khorasani P., Arjmand N., Shirazi-Adl A., Effect of changes in the lumbar posture in lifting on trunk muscle and spinal loads: A combined in vivo, musculoskeletal, and finite element model study, J. Biomech. 104, 2020.
13. Kingma I., Faber G.S., van Dieën J.H., Supporting the upper body with the hand on the thigh reduces back loading during lifting, J. Biomech., 49(6), 881–889, 2016.
14. Koopman A.S., Kingma I., Faber G.S., Bornmann J., van Dieën J.H., Estimating the L5S1 flexion/extension moment in symmetrical lifting using a simplified ambulatory measurement system, J. Biomech. 70, 242–248, 2018.
15. Matthew R.P., Seko S., Bajcsy R., Lotz J., Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies, IEEE J. Biomed. Heal. informatics 23(4), 1784–1793, 2019.
16. McGill S.M., Marshall L., Andersen J., Low back loads while walking and carrying: Comparing the load carried in one hand or in both hands, Ergonomics 56(2), 293–302, 2013.
17. Plamondon A., Gagnon M., Gravel D., Moments at the L5/S1 joint during asymmetrical lifting: effects of different load trajectories and initial load positions, Clin. Biomech. 10(3), 128–136, 1995.
18. Rajaee M.A., Arjmand N., Shirazi-Adl A., Plamondon A., Schmidt H., Comparative evaluation of six quantitative lifting tools to estimate spine loads during static activities, Appl. Ergon. 48, 22–32, 2015.
19. Rajagopal A., Dembia C.L., DeMers M.S., Delp D.D., Hicks J.L., Delp S.L., Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait, IEEE Trans. Biomed. Eng. 63(10), 2068–2079, 2016.
20. Roozbahani H., Alizadeh M., Ustinov S., Handroos H., Development of a novel real-time simulation of human skeleton/muscles, J. Biomech. 114, 2021.
21. Seth A., Hicks J.L., Uchida T.K., Habib A., Dembia C.L., Dunne J.J., Ong C.F., DeMers M., Rajagopal A., Millard M., Hamner S.R., Arnold E.M., Yong J.R., Lakshmikanth S.K., Sherman M.A., Ku J.P., Delp S.L., OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement, PLoS Comput. Biol. 14(7), 2018.
22. Vilas-Boas M. C., Choupina H.M.P., Rocha A.P., Fernandes J.M., Cunha, J.P.S., Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait, J. Biomech. 87, 189–196, 2019.