Abnormal deployment of the airbag during a frontal car collision can cause injuries to the upper extremity of drivers with non-standard driving postures.Finite element simulation offers an effective approach for evaluating such injury risks.In this study,a biomechanical finite element model of the upper limb of the 95th percentile human body with detailed anatomical structures was developed.The validity of the upper extremity-airbag collision model was confirmed by reconstructing the cadaveric forearm and airbag impact experiments.Based on the validated model,the influence of factors such as airbag mass rate parameters,upper limb grip angle,and grip force on upper limb injuries in frontal collisions was investigated.The results indicate that variations in these three parameters have a significant influence on upper extremity injury,and these factors should be considered in the assessment of upper extremity injuries during car collision.

Objective To investigate the risk of thoracoabdominal injuries in six-year-old child occupants in a reclined seating posture during frontal collisions,and provide a reference for developing child restraint systems(CRS).Methods Three validated biomechanical models of six-year-old child occupants in different seating postures with detailed anatomical structures were used.The acceleration curve from a sport utility vehicle crash test was applied to analyze the effects of seating posture on thoracic motion trajectory,chest acceleration,thoracoabdominal compression,viscous criterion(VC)of the chest and abdomen,internal organ strain,and spinal stress.Results Thoracic motion trajectories varied in the Z-direction under three seating postures.As the upper torso angle increased,thoracoabdominal kinematic injury parameters showed an upward trend.The thoracic and abdominal VC under 120° and 135° posture increased by 67％and 113％,10.7％and 25％compared with that under 105° standard sitting posture.The risk of thoracic internal organ injury was inversely related to the seating angle,while the risk of abdominal internal organ injury was positively related to the seating angle.The primary spinal injury mechanism was compression-flexion.Conclusions CRS protection evaluation should comprehensively consider thoracoabdominal kinematic parameters,internal organ biomechanics,and spinal injury risk.These findings have important implications for CRS development in intelligent driving systems and occupant protection strategy formulation.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Objective To investigate the risk of thoracoabdominal injuries in six-year-old child occupants in a reclined seating posture during frontal collisions,and provide a reference for developing child restraint systems(CRS).Methods Three validated biomechanical models of six-year-old child occupants in different seating postures with detailed anatomical structures were used.The acceleration curve from a sport utility vehicle crash test was applied to analyze the effects of seating posture on thoracic motion trajectory,chest acceleration,thoracoabdominal compression,viscous criterion(VC)of the chest and abdomen,internal organ strain,and spinal stress.Results Thoracic motion trajectories varied in the Z-direction under three seating postures.As the upper torso angle increased,thoracoabdominal kinematic injury parameters showed an upward trend.The thoracic and abdominal VC under 120° and 135° posture increased by 67％and 113％,10.7％and 25％compared with that under 105° standard sitting posture.The risk of thoracic internal organ injury was inversely related to the seating angle,while the risk of abdominal internal organ injury was positively related to the seating angle.The primary spinal injury mechanism was compression-flexion.Conclusions CRS protection evaluation should comprehensively consider thoracoabdominal kinematic parameters,internal organ biomechanics,and spinal injury risk.These findings have important implications for CRS development in intelligent driving systems and occupant protection strategy formulation.

Objective The finite element pelvis model with detailed anatomical structures which meets the Chinese human 95th percentile characteristics is developed,and the influence of cortical bone modeling method on the biomechanical response of the real pelvis is explored.Methods Based on the pelvic medical images of a 95th percentile male volunteer,two finite element pelvis models with real hip bone cortical bone thickness(REA-M)and 2 mm uniform cortical bone thickness(CON-M)dominated by hexahedral elements were constructed.Using the simulation method to reconstruct the loading conditions of cadaver experiments,the validation of models was verified by comparing the cadaver experimental results and simulation results,and biomechanical response differences of two models under different working conditions were discussed.Results The simulation data showed that there was a strong correlation between the overall biomechanical responses of two pelvic models and the cadaver experiment,and the mechanical response difference between two models was mostly within 8％,and the correlation score difference between two models was smaller than 2％.Conclusions The validation of two pelvic models established in this study is verified by rebuilding multiple simulation experiments.Although the biomechanical responses of CON-M and REA-M models were different,the difference was small.From the perspective of model simplification,the CON-M model can be used to study the biomechanical response of the pelvis.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Objective The finite element pelvis model with detailed anatomical structures which meets the Chinese human 95th percentile characteristics is developed,and the influence of cortical bone modeling method on the biomechanical response of the real pelvis is explored.Methods Based on the pelvic medical images of a 95th percentile male volunteer,two finite element pelvis models with real hip bone cortical bone thickness(REA-M)and 2 mm uniform cortical bone thickness(CON-M)dominated by hexahedral elements were constructed.Using the simulation method to reconstruct the loading conditions of cadaver experiments,the validation of models was verified by comparing the cadaver experimental results and simulation results,and biomechanical response differences of two models under different working conditions were discussed.Results The simulation data showed that there was a strong correlation between the overall biomechanical responses of two pelvic models and the cadaver experiment,and the mechanical response difference between two models was mostly within 8％,and the correlation score difference between two models was smaller than 2％.Conclusions The validation of two pelvic models established in this study is verified by rebuilding multiple simulation experiments.Although the biomechanical responses of CON-M and REA-M models were different,the difference was small.From the perspective of model simplification,the CON-M model can be used to study the biomechanical response of the pelvis.

Abnormal deployment of the airbag during a frontal car collision can cause injuries to the upper extremity of drivers with non-standard driving postures.Finite element simulation offers an effective approach for evaluating such injury risks.In this study,a biomechanical finite element model of the upper limb of the 95th percentile human body with detailed anatomical structures was developed.The validity of the upper extremity-airbag collision model was confirmed by reconstructing the cadaveric forearm and airbag impact experiments.Based on the validated model,the influence of factors such as airbag mass rate parameters,upper limb grip angle,and grip force on upper limb injuries in frontal collisions was investigated.The results indicate that variations in these three parameters have a significant influence on upper extremity injury,and these factors should be considered in the assessment of upper extremity injuries during car collision.

Objective To construct the theoretical framework of rehabilitation big data based on International Classification of Func-tioning,Disability and Health(ICF). Methods Drawing upon international rehabilitation policy documents,such as the World Health Organization's Rehabili-tation in health systems;Rehabilitation in health systems:guide for action;Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME);Template for Rehabilita-tion Information Collection(TRIC):a tool accompanying the Systematic Assessment of Rehabilitation Situation(STARS);and Framework and Standards for Country Health Information Systems;this study examined the com-position and function of rehabilitation big data.The content structure of the rehabilitation big data domain was an-alyzed using the World Health Organization Family of International Classifications(WHO-FICs).Furthermore,the generation patterns of rehabilitation big data was constructed drawing on the Health Metrics Network and big data hierarchical classification. Results Within the six primary elements of the health service system,the information system element was particularly significant,encompassing a substantial branch known as rehabilitation big data.There were three components of rehabilitation big data:health condition,health-related factors and health services.The content framework for this data was derived from the WHO-FICs framework,which covered three dimensions:health and function,dis-ease and function,and disease,function and intervention.A comprehensive model for generating and applying re-habilitation big data in rehabilitation services was developed in line with the requirements for constructing big da-ta architectures.The sources of this data included population censuses,social registration information,population surveys,resources,services and personal records.The result chain of rehabilitation big data encompassed five major processes:input,process,output,outcome and impact.The processing and utilization of this data involved collection,storage,management,analysis and application. Conclusion A theoretical framework for rehabilitation big data has been constructed based on the ICF theory.

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.