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Correspondence to Daniela Ohlendorf. Reprints and Permissions. Ohlendorf, D. Standard values of the upper body posture and postural control: a study protocol. J Occup Med Toxicol 11, 34 Download citation. Received : 30 May Accepted : 27 June Published : 16 July Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Decisions on orthopedic interventions on upper body posture and its control have usually resulted from comparisons with the healthy state. Results For standard value determination tolerance range and confidence intervals will be calculated.
Discussion This project aims at improving classifications in adaptations of upper body posture and postural control. Background Postural control has to be regarded as a complex feedback-dependent system using various sensory inputs from visual, vestibular and somatosensory receptors [ 1 — 3 ]. Aims In this project non-invasive measurement techniques shall be used to determine upper body posture synchronously to static postural control. Therefore, the following parameters will be analyzed in terms of age, gender, social strata and profession: Determination of a general range of tolerance and confidence intervals for upper body posture and postural control.
Correlations between hours of work and upper body posture and postural control. Correlations between physical activity, age and upper body posture and postural control. Recruitment Participants will be recruited at different places in Germany.
In this study, subjects underwent ten second trials of quiet standing balance with and without skin stretch feedback. Visual and vestibular sensory deficits were simulated by having each subject close their eyes and tilt their head back.
We found that sensory augmentation by velocity-based skin stretch feedback at the fingertip reduced the entropy of the standing postural sway of the people with simulated sensory deficits. This result aligns with the framework of the free energy principle which states that a self-organizing biological system at its equilibrium state tries to minimize its free energy either by updating the internal state or by correcting body movement with appropriate actions. The velocity-based skin stretch feedback at the fingertip may increase the signal-to-noise ratio of the sensory signals, which in turn enhances the accuracy of the internal states in the central nervous system.
With more accurate internal states, the human postural control system can further adjust the standing posture to minimize the entropy, and thus the free energy. Upright standing is one of the most important motor tasks that enable the use of the upper limbs and hands for tool use and dexterity.
Even though this motor task seems trivial, it involves the complicated interplay between neurophysiology and biomechanics of sensory, neuromuscular, and neural processing subsystems 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8. Specifically, upright standing involves the integration and transformation of various sensory inputs e.
Degradation in the sensory systems e. One of the key elements for the successful balance rehabilitation is to quantitatively assess the balance. Traditionally, descriptive parameters have been used to characterize the postural sway of the standing balance Several mathematical models using statistical mechanics have also been introduced to capture the stochastic behaviors of the postural sway 20 , 21 , However, these measures provide only the statistical description of the system, but not the dynamics itself Instead, several researchers tried to model the sensorimotor system from the control-theoretic perspectives to understand the human motor control system.
Wolpert et al. They argued that the CNS internally integrates sensory signals such that predicted sensory errors temporally propagate in the framework of a Kalman filter. In addition, motor learning was suggested to be the process of updating the prior knowledge of the system by evidences from the error feedback in the Bayesian-optimal framework However, even though the control-theoretic framework seems fascinating, it requires for the biological systems to have the complicated inverse model and the heavy computation, which are not likely to be realizable neurobiologically Recently, a unifying theory, called free-energy principle, was proposed to account for the action, perception and learning of sensorimotor systems The free-energy principle argues that a self-organizing biological system at its equilibrium point tries to minimize its free energy.
In other words, a self-organizing biological system tries to resist a tendency toward disorder. According to the free-energy principle, a self-organizing biological system minimizes the free energy either by i updating the internal state equivalently, internal model or prior 25 or ii correcting body movement with appropriate actions. Free energy principle does not require any complicated inverse models and is neurobiologically realizable Therefore, the idea of the free-energy principle may also be applied to human postural control system to understand how the human CNS maintains the standing balance under external perturbations.
In other words, this framework can be used to quantify the human postural control system 7 , In the Method section and Appendix see Supplementary Information , we will examine the use of the free-energy principle to derive metrics that quantify or assess the human standing balance.
Rehabilitation is a process of a motor adaptation in response to external changes or stimuli to restore the normative motor functions.
These external changes or stimuli mostly result from errors or deviations from the reference or desired trajectories for the given tasks. However, how the motor adaptation takes place is still not well known and is one of the important research topics in human motor control and rehabilitation.
One widely-accepted idea is that human sensorimotor systems utilize the error feedbacks for motor adaptation Several studies have reported that amplifying the movement errors could enhance the rehabilitation outcomes, which is sometimes referred to as an error augmentation 13 , 30 , Although rehabilitation may include internal sources of adaptation such as motor imagery, these are not the focus of this study.
Cutaneous feedback has been of a great interest as a way to sensory augmentation for the balance enhancement 13 , 32 , 33 , Random vibrotactile feedback has been used to strengthen the weak signal, known as stochastic resonance, and thus improve the sensorimotor functions 35 , 36 , 37 , It was shown that the random vibrotactile signal at various body parts e.
Similarly, Jeka et al. They found that subjects who are lightly touching a fixed surface with their index fingertip with the normal force less than 1N exhibited the reduced postural sway in tandem Romberg posture.
Clapp and Wing 33 also showed a reduction in postural sway in the sagittal plane when subjects were making a light contact with their fingertips on a fixed surface during normal bipedal stance. Pan et al. These suggest that additional cutaneous sensory inputs at the fingertip can provide a robust regulation of the postural sway. It has been shown, by partially blocking sensory afferents during human standing, that the enhanced balance due to light touch was not due to the mechanical support on the fingertip but due to the tactile feedback Krishnamoorthy et al.
The objective of this study is twofold: i to formulate the human standing postural control system in the framework of the free-energy principle, and thus to introduce measures that quantify the human postural control system and ii to investigate the efficacy of the skin stretch feedback in enhancing the human standing balance.
We hypothesized that skin stretch feedback would both reduce the entropy of the postural sway and improve balance with respect to other measures in accordance with the free energy principle.
The formulation of the human postural control system in the framework of the free-energy principle is given in the Method section. We also provide a proof that the long-term average of the free energy can be approximated by an entropy of the postural sway in the Appendix see Supplementary Information. In the Experiment section, the details of experimental protocol to test the efficacy of the skin stretch feedback are described.
In the Result and Discussion sections, we show that the skin stretch feedback at the fingertip reduces Entropy of the postural sway. In this section, we introduce the framework of free-energy principle and provide an algorithm to estimate the entropy of the human postural sway. To test the feasibility of the framework, we conduct an experiment of quiet standing with and without skin stretch feedback. The free-energy principle states that a self-organizing biological system at its equilibrium point tries to minimize its free energy Similar to the idea of the conventional optimal control theory based on forward-inverse models 24 , 42 , 43 , free energy principle explains how a biological system makes movements, perceives the environments and infers the internal states.
However, unlike the conventional optimal control theory, free energy principle does not require the complicated inversion process of the forward models via heavy computation and optimization That is, free energy principle uses a forward model only and does not need to solve the complicated Bellman equation from dynamic programming with cost functions. Instead, it uses prior beliefs about the given tasks learned via previous sensorimotor behaviors, which is generally considered to be Bayes-optimal 44 , With these benefits, we frame the human postural control system with the free energy principle.
In essence, free energy principle, or equivalently active inference, claims to minimize the prediction error of the forward model i. The prediction error is called self-information, or surprise. In other words, minimizing the surprise is equivalently maximizing the accuracy of prediction about the sensory states by the agent i.
A careful mathematical formulation see Supplementary Information can prove that minimizing the free energy is implicitly equivalent to minimizing surprise.
To minimize the free energy, the agent either i makes a movement of the body to alter the sensory input or ii updates the internal states of the brain to refine the approximation of the posterior beliefs i.
When this principle is applied to the human standing balance, CNS is understood to minimize the free energy either by correcting body posture or by updating the internal model of the postural control system i.
In other words, the human postural control system with the goal or intention of stable standing balance always tries to be confident about the the current state of its posture. The long-term average of surprise can be proven to be Entropy see Supplementary Information. Therefore, the free energy principle is called a minimum entropy principle , suggesting that CNS keeps the average surprise of the body posture sampled from a probability distribution or density to be low.
A density with low entropy means that, on average, the body posture is relatively predictable 23 , Then, what is the meaning of entropy minimization in the problem of the human postural control? Ones are easily tempted to consider that the postural control system is trying to prevent any postural sways. From the optimal control point of view, this may seem to be true since the usual cost functions include states e. However, it is important to note that the equilibrium point during the quiet standing is not necessarily the same as standing still with no postural sway.
In general, humans do not take the strategy of standing still since it requires excessive efforts Instead, the human postural control system allows flexibility to some extent such that whenever the postural sway is within a certain range, little control efforts are made in correcting the posture In our previous study 23 , we developed an algorithm to compute the entropy of human quiet standing and showed that people with better balance control have smaller entropy of postural sway distribution.
Postural sway data captured by the center of pressure COP were used to extract the reduced order dynamics of the human postural control system in the framework of a Markov chain. The reduced order dynamics were expressed in the form of transition probability matrix that describes the movement of COP, and thus the evolution of COP distribution.
With the careful development of the algorithm, the distribution is guaranteed to converge to be stationary. This framework, a free energy principle applied to human postural control system, is called Invariant Density Analysis see Data analysis section. For the detailed development of the framework, please refer to Hur et al.
From the Invariant Density Analysis , it was found that people with degraded sensorimotor functions or under perturbations had significantly higher entropy compared with healthy people with no perturbations 16 , As mentioned in the Introduction, skin stretch feedback showed its potential for balance rehabilitation.
Skin stretch is a simple mechanism for generating additional sensory signals and can be made accessible in almost every situation via fixed structures or wearable robotic devices. Skin stretch feedback is a type of sensory augmentation that enables the error augmentation for motor learning. In terms of free energy principle, the additionally-sampled sensory data i. In other words, the increased prediction accuracy of the sensory states due to skin stretch feedback, and thus the increased SNR contributes to the optimal motor planning.
The motor adjustment from the optimal motor planning further contributes to minimizing the prediction errors. These repeated processes eventually minimize the entropy of sensory state probability Previously, we developed a wearable sensory augmentation system for standing balance rehabilitation using a skin stretch feedback device Fig. The device was inspired by the concept of a light touch as mentioned in the Introduction. This preliminary study 13 found that the sensory augmentation due to skin stretch feedback at the fingertip could enhance the standing balance, indicating the reinforcement of the accuracy of the sensory prediction.
Skin stretch feedback system and experiment setup. The components of the system include Fig. A wearable skin stretch device Fig. As the subject sways, IMU detects the change of the body orientation and the motor rotates accordingly. Then, the contactor stretches the fingertip pad, which induces the skin stretch feedback. Different sizes of housings and contactors were fabricated to cover various finger sizes of all subjects so that the contact at the fingertip was maintained with qualitatively small normal force.
Subjects wore the SSD at their index finger and the waist belt around their waist. The recorded sway angle was processed by myRIO in real-time. To find K d , we tuned the parameters during the pilot test to make sure the subjects do not feel any discomfort, but feel light touch at their fingertips. Intuitively, this idea of D control is plausible since skin stretch feedback is based on the flow or slip at the contact surface.
Both direction and velocity information of postural sway can be provided and augmented by additional light touch of a stationary surface 48 , 49 , 50 , Moreover, it was found that postural sway was highly consistent with the driving frequency of moving surface where subjects are touching. This emphasizes the important role of cutaneous feedback in modulating the control of upright posture 50 , In this way, when the subject stayed still regardless of the pitch angle e.
This also removed the drift issues of the IMU data. To test the idea of free energy principle and the efficacy of the skin stretch feedback in reducing the entropy of the standing balance, the following experiment was conducted. In Pan et al. We assumed a correlation of 0. Then, at least 12 subjects were needed for each group for a significance level of 0.
To ensure significance, 15 subjects were recruited three females and twelve males: mean age s. The subjects have neither been neurologically impaired nor had balance issues before. They were informed of the experimental procedures except how the sensory feedback device operates. All subjects provided the informed consent before the experiment started.
Subjects wore the SSD on their index finger and put on the waist belt around their waist. Their arms hung naturally by their sides. Subjects were instructed to stand quietly. No other instructions were given to subjects about the behavior of the contactor.
To simulate the sensory deficit condition, both visual and vestibular deficits were induced by asking subjects to close their eyes and tilt their head up at least 45 Fig.
Under such a head-extended condition, the vestibular organ moves and the utricle otoliths are put into irregular location, which perturbs the vestibular sensory system and therefore results in postural imbalance 53 , Subjects put on an overhead safety harness for protection against unwanted and unexpected falls.
The experiment comprised of practice and main sessions. Practice session aimed at familiarizing the subjects with the experiment setup. Subjects were asked to stand quietly with barefoot on a forceplate. There were three practice trials before the main session to make sure that subjects become familiar with the experiment. Each condition was repeated 10 times to minimize random effects by taking the mean values of each metric.
Note that the order of all 20 trials was fully randomized, and subjects were not informed of the sensory augmentation condition. Break was provided upon request, and a two-minute rest was provided between every 5 trials to avoid muscle fatigue. Note that in both sensory augmentation conditions, subject wore the SSD all times even when no skin stretch feedback was provided. Based on the free energy principle, human postural control system tries to minimize the entropy of body posture sampled from a postural sway probability density.
In the following, a brief explanation of IDA is presented. IDA includes several measures including entropy to parameterize the invariant density. Postural sway measures were computed in anterior-posterior AP , medio-lateral ML , and radial Rad direction. Previously, we 23 introduced a reduced-order finite Markov chain model to analyze the stochastic structure of postural sway, which provides insight into the long-term system behavior. This model describes the states zero-mean COP data of the dynamical system and evolution of those states.
To acknowledge the details of subject-specific COP behaviors, five parameters were introduced as follows. H is the entropy and is the measure of randomness. In addition to the above five parameters, we computed a metric that may provide insight into the control mechanisms of the postural control system.
The eigenvector corresponding to the second largest eigenvalue is of our interests For simplicity, we call the eigenvector corresponding to the second largest eigenvalue as the second eigenvector.
Recently, the second eigenvector has been used to formulate an intuitive understanding of the dynamics for a finite state-space ergodic Markov chain by decomposing the state space into essential features 55 , 56 , Specifically, Dellnitz 55 reported that the state space could be subdivided into two subsets depending on the sign of the corresponding second eigenvector i.
Each subset shows different dynamic behavior. Collins and DeLuca 20 proposed a Stabilogram Diffusion Analysis SDA that could provide several physiologically-meaningful parameters from stabilogram from the viewpoint of statistical mechanics. It was shown that the postural control system during quiet standing could be separated into two different control mechanisms: open-loop and closed-loop control. Figure 2 shows an example of a resultant planar stabilogram-diffusion plot.
Short-term and long-term regions are dominated by the open-loop and closed-loop control strategies, respectively. This phenomenon can be described by the following parameters. Linear and log-log stabilogram diffusion plots in the radial Rad direction.
Short-term and long-term regions fitted by straight black regression lines are dominated by the open-loop and closed-loop control strategies respectively.
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