| Standing balance is the process of maintaining the body’s center of gravity in the standing support surface to achieve dynamic stability,which is an important prerequisite for maintaining postural stability and achieving motor functions such as walking and running.The process of standing balance requires close cooperation between sensory and motor systems,in which the sensory input from vision,touch,proprioception and vestibular system balance helps the central nervous system to determine the body’s standing posture and orientation in time,and can adjust the motor commands in real time according to the environmental information and changes in the center of gravity;the motor systems of muscles,bones and joints under the innervation of nerves can generate reasonable muscle contraction,torque change and center of gravity shift to achieve dynamic stability of standing posture.This perceptual-motor fusion mechanism is the key to maintain standing balance,not only to ensure the stability of the body’s center of gravity migration within the support surface formed by the biped,but also to achieve timely and effective response to external disturbances.Therefore,the observation and analysis of standing balance performance can provide an important basis for the analysis of perceptual-motor function and the assessment of the nerve-musculo-skeletal system.Balance training is considered to be an effective way to improve posture control,enhance sensorimotor function and reduce the risk of falls.Introducing visual feedback in the process of standing balance training,establishing goal-oriented training based on visual feedback and realizing a training mechanism more in line with the principle of biofeedback are the key to effectively improve the effect of standing balance training.On the other hand,in order to achieve high-quality training effect,balance training requires trainees to establish a correct movement mode through repeated intensive training of their own center of gravity in a state of high concentration.How to improve the trainees’ attention level and reduce the physical and psychological fatigue caused by repeated training is one of the important factors to ensure the quality of standing balance training.Virtual reality(VR)technology is a computer simulation system that can create and experience the virtual world.Applying VR technology to rehabilitation training evaluation can provide real-time visual feedback based on virtual objects,establish training tasks and sensory stimulation in a variety of scenes,and greatly improve the participation and interest of users while obtaining immersive feeling.If VR technology is applied to standing balance training,it is expected to establish visual feedback conditions more in line with the life scene,and can effectively reduce the physiological and psychological fatigue caused by high-intensity balance training,which is of great significance to improve the training efficiency and improve the training quality.Based on the above considerations,this study establishes a target-oriented standing balance training and evaluation system based on visual feedback,and compares the effects of visual feedback conditions in 2D scenes and 3D Immersive Virtual Reality(IVR)scenes on multi-directional dynamic standing balance training.In this process,we deeply analyzed the migration pattern of the center of pressure(COP)in each direction and the central-peripheral coordination control mechanism based on electroencephalogram(EEG)and surface electromyogram(sEMG)analysis,in order to explore the learning mechanism of standing balance,clarify the directional pattern of center of gravity,and improve the standing balance.This paper will lay the foundation for exploring the learning mechanism of standing balance,clarifying the directional pattern of weight transfer,and improving the training and assessment of standing balance.The main work accomplished in this paper is as follows:(1)Development of multi-directional dynamic standing balance training evaluation system based on 2D visual feedback and 3D IVR visual feedback.In order to deeply analyze the learning mechanism and the central-periphery coordination mechanism of standing balance control,a multi-directional dynamic standing balance control training and evaluation system has been designed and developed by using sensor technology and signal processing technology.The COP signal of participants was projected as a visual feedback signal to different visual feedback scenarios.The system can provide dynamic standing balance training in eight directions based on 2D visual feedback and 3D IVR visual feedback,and synchronously collect the COP signal,sEMG signal and EEG signal of the trainees for evaluation.(2)The fluctuation analysis of COP signal before and after multi-directional dynamic standing balance training.The COP signal reflects the instantaneous average coordinate of the force between the body and the support surface,and its volatility is an effective indicator to reflect the stability of the standing balance.In this study,Detrended fluctuation analysis(DF A)was used to study the influence of multi-directional dynamic standing balance training on the fluctuation of COP signal.The results show that multi-directional training shortens the path and time of COP movement,indicating that subjects tend to migrate COP with lower energy consumption.After training,the DFA scale index of COP trajectory in each target direction was significantly reduced,indicating that the variability of dynamic structure in COP fluctuation was enhanced.(3)Analysis of dynamic changes in muscle network topology before and after multi-directional dynamic standing balance training.In order to deeply analyze the close association between muscle activity and standing balance control,a complex undirected weighted muscle network based on a multilayer horizontal viewable approach was constructed for 10 lower limb muscles in this study to evaluate the synergistic coupling ability of multiple muscles in the lower limb during dynamic standing balance control at the global level.The results showed that the clustering coefficients of the muscle network significantly increased(p<0.05)and the average feature path length significantly decreased(p<0.05)after training.This result indicates that increased muscle activity in the lower limbs,enhanced coordination of each muscle,and more efficient intermuscular information exchange enhanced the subjects’ ability to perform balance control tasks.(4)Analysis of dynamic changes in brain network topology before and after multi-directional dynamic standing balance training.The human cerebral cortex may regulate the excitability of subcortical postural centers to maintain the body’s balance and postural stability in response to environmental demands.It would be helpful to consider balance control ability in conjunction with the level of central nervous system regulation to obtain more comprehensive and valid balance assessment results.Therefore,this study analyzed the EEG signals of subjects during the experiment,established a multilayer undirected weighted functional brain network constructed based on multilayer horizontal viewable,tapped the connectivity characteristics of relevant nodes in functional brain regions,and dissected the cortical mechanisms supporting balance by analyzing the topology of brain networks during dynamic standing balance control.The results showed that EEG at each frequency band(θ:4～7 Hz,α:8～13 Hz,β:13～30 Hz,γ:30～50 Hz)observed a significant increase in the clustering coefficient(p<0.05)and a significant decrease in the mean feature path length(p<0.05)of the brain network after training,indicating that dynamic balance training improved the central nervous system during dynamic balance control information integration and processing capacity,enabling the motor system to receive top-down motor commands and adjust body posture to maintain balance in a more timely manner.(5)Comparison of the effects of multidirectional dynamic standing balance training with 2D visual feedback and 3D IVR visual feedback.In order to clarify the effects of 2D visual feedback condition and immersive 3D IVR visual feedback condition on the training effect in this study,the characteristics of COP,sEMG and EEG signals under the two visual feedback conditions were compared in depth,and the effects of the two visual feedback conditions on the key parameters of dynamic standing balance training were clarified.The results showed that there was no significant difference in the characteristics of COP,sEMG and EEG signals between 2D visual feedback condition and 3D IVR visual feedback condition,indicating that the effect of 3D IVR visual feedback was comparable to that of 2D visual feedback,and both played a positive role in goal-oriented training.Also,because 3D IVR training is more in line with life scenarios,subjects are more interesting and engaged,which is more advantageous for both interest development and fatigue suppression under long-term training.The standing balance training assessment system based on 2D visual feedback and 3D IVR visual feedback established in this study can realize a training mechanism more in line with the biofeedback principle,which can effectively enhance the effect of multi-directional dynamic standing balance and is effective and important for improving postural control,enhancing sensorimotor function and reducing the risk of falls.Research on the central-peripheral coordination control mechanism of standing balance in this study deeply advances the human-machine-environment interaction pattern under the stable control of standing posture,and provides a new way for the analysis of perceptual-motor function and assessment of neuromusculoskeletal system in healthy states and various disease conditions. |
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