Biomechanical Research Of Poor Posture And Spinal Orthosis

Health is an inevitable requirement for the promotion of all-round human development.With the development of socio-economic level and the improvement of living standard,it has become an essential trend for people to desire and pursue health.A study from the World Health Organization shows that daily behavior and lifestyle are the main factors that affect human health.Long-term ambulatory work and sedentary are common phenomena in modern society,in addition,the awareness of normal posture is still insufficient,thus leading to an increasing incidence of poor posture.If poor posture is not corrected in time,it will impair the growth and development of adolescents,muscle activity,and normal function of the organs.Spinal orthoses are used to improve poor posture and increase spinal stability.However,there are still relatively few studies on the biomechanical effects of poor posture and spinal orthoses on the human.The study of poor posture and spinal orthosis will have important practical significance.In this paper,kinematic data was collected through a gait experiment.Kinematic and ground reaction force(GRF)data as input for multi-body dynamics musculoskeletal simulation.Finite element method was used to analyze the effect of different loading positions and magnitudes;and the role of the spinal orthosis in trunk movement was investigated.The surface electromyography(sEMG)experiment was performed to collect the sEMG signals from trunk and lower extremity muscles during movements without and with a spinal orthosis.Different machine learning algorithms were constructed to implement movement classification based on sEMG signals.The main research works are as follows:(1)The Vicon motion capture system was employed to collect kinematic data from subjects walking in normal posture,poor posture and spinal orthosis.The effects of body posture on spatio-temporal parameters(step length,step stride,step width,and step speed),center of pressure(COP),and center of mass(COM)were analyzed,and the joint angles(spine,thorax,hip,knee and ankle)during the complete gait cycle were studied.The results showed that body posture had no significant effect on step length,step width,and step speed.There was no significant difference in the COP-COM difference in the coronal and sagittal planes(toe off)when walking in normal posture,poor posture,and spinal orthosis.However,the COP-COM difference in the sagittal plane(hell strike)when walking in poor posture was lower than that of walking in normal posture and spinal orthosis,which means that walking in poor posture will bring the COM closer to the COP in the sagittal plane.The results showed that spine and thorax angles in the sagittal plane were significantly higher when walking in poor posture compared to walking in normal posture,while there was no significant difference in the kinematic data of the trunk and lower limbs when walking in normal posture and spinal orthosis.Moreover,there was no significant difference in lower limbs(hip,knee,and ankle)angles in the sagittal plane among three gait patterns.(2)The subject-specific full-body musculoskeletal model in OpenSim was used to explore joint moments,joint impulse moments,and joint compression forces during three gait patterns.The results showed that the lumbar moment in the sagittal plane,lumbar impulse moment,and back compression force when walking in poor posture were significantly higher than those in normal posture and spinal orthosis.There was no significant difference in impulse moments at the lumbar,hip,knee,and ankle joints when walking in normal posture and spinal orthosis.Moreover,there were no significant differences in hip abduction and adduction moments,knee flexion and extension moments,hip compressive force,knee compressive force,and ankle compressive force when walking in normal posture,poor posture,and with a spinal orthosis.The results indicated that poor posture has little effect on the lower limbs,but can cause abnormal weight bearing on the lumbar and back.The use of spinal orthosis significantly reduced the load on the lumbar and back,and demonstrated that spinal orthosis is an effective way to maintain normal physiological function.(3)The above full-body musculoskeletal model only explored the relationship between human movement and the forces under different gait patterns,while finite element analsysis can obtain the stress and displacements of the components.The effects of different positions and magnitudes of orthopedic forces on poor posture were investigated by the finite element method.The results showed that load position and magnitude can have an impact on the orthopedic effect of the poor posture spine model.It was inferred that the position of the shoulder belt,the tightening force of the shoulder belt and abdominal lumbar belt may affect the orthopedic outcome.To further obtain the stress and displacement of vertebrae,ribs and soft tissue of the trunk during daily movements(flexion,lateral bending and axial rotation)while wearing the spinal orthosis.In this study,a normal posture trunk model was established to evaluate the role of spinal orthosis during trunk movement.The results showed that the presence of a spinal orthosis reduces the stress and displacement of the vertebrae,ribs,and soft tissue.This suggests that wearing a spinal orthosis can restrict trunk movements and reduce the risk of injury.(4)It is clear that a spinal orthosis can effectively limit trunk movement from the above analysis,but it will always restrict trunk movements,making daily activities(such as bending over to pick something up)difficult,and thus greatly affecting compliance.For optimal design consideration,the orthosis should have greater stiffness to restrict movement and correct posture when the body in poor posture,and less stiffness to allow free movement when preforming daily activities.Therefore,this paper investigates the feasibility of movement recognition based on sEMG signals,and provides theoretical support for subsequent orthotic improvements.Ten participants were tested for different movements with two different modalities:motion without the spinal orthosis(Normal)and with the spinal orthosis(Spinal orthosis).The sEMG signals were collected from eight muscles using Noraxon Ultium wirelessEMG device during four movements[flexion-extension,lateral bending,axial rotation,and stand to sit to stand].Four feature values of the sEMG signal were analyzed and extracted,which are Integrated Electromyography(IEMG),Root Mean Square(RMS),Mean Absolute Value(MAV),and Input Contribution Rate(ICR_i).Three machine learning algorithms,Random Forest(RF),k-Nearest Neighbor(k-NN),and Support Vector Machine(SVM),were constructed for identification and classification.The results showed that machine learning algorithms could not only identify Normal and Spinal orthosis movement modalities,but also distinguish four daily movements in the Normal and Spinal orthosis modalities.The machine learning methods are able to accurately identify movement patterns by considering sEMG signals,which may provide instructions for adjusting the stiffness of the spinal orthosis.Moreover,the classification performance of RF,k-NN,and SVM algorithms were also compared.The results showed that all three machine learning algorithms have high classification accuracy.