Sukyung Park

Postural feedback scaling deficits in Parkinson's disease

Many differences in postural responses have been associated with age and Parkinsons disease (PD), but until now there has been no quantitative model to explain these differences. We developed a feedback control model of body dynamics that could reproduce the postural responses of young subjects, elderly subjects, and subjects with PD, and we investigated whether the postural impairments of subjects with PD can be described as an abnormal scaling of postural feedback gain. Feedback gains quantify how the nervous system generates compensatory joint torques based on kinematic responses. Seven subjects in each group experienced forward postural perturbations to seven different backward support surface translations ranging from 3- to 15-cm amplitudes and with a constant duration of 275 ms. Ground reaction forces and joint kinematics were measured to obtain joint torques from inverse dynamics. A full-state feedback controller with a two-segment body dynamic model was used to simulate joint kinematics and kinetics in response to perturbations. Results showed that all three subject groups gradually scaled postural feedback gains as a function of perturbation amplitudes, and the scaling started even before the maximum allowable ankle torque was reached. This result implies that the nervous system takes body dynamics into account and adjusts postural feedback gains to accommodate biomechanical constraints. PD subjects showed significantly smaller than normal ankle feedback gain with low scaling and larger hip feedback gain, which led to an early violation of the flat-foot constraint and unusually small (bradykinetic) postural responses. Our postural feedback control model quantitatively described the postural abnormality of the patients with PD as abnormal feedback gains and reduced ability to modify postural feedback gain with changes in postural challenge.

The nervous system uses internal models to achieve sensory integration

All linear accelerometers measure gravito-inertial force, which is the sum of gravitational force (tilt) and inertial force due to linear acceleration (translation). Neural strategies must exist to elicit tilt and translation responses from this ambiguous cue. To investigate these neural processes, we developed a model of human responses and simulated a number of motion paradigms used to investigate this tilt/translation ambiguity. In this model, the separation of GIF into neural estimates of gravity and linear acceleration is accomplished via an internal model made up of 3 principal components: 1) the influence of rotational cues (e.g., semicircular canals) on the neural representation of gravity, 2) the resolution of gravito-inertial force into neural representations of gravity and linear acceleration, and 3) the neural representation of the dynamics of the semicircular canals. By combining these simple hypotheses within the internal model framework, the model mimics human translation and tilt responses for preliminary data from one human subject. These modeled response characteristics are consistent with preliminary data and with the hypothesis that the nervous system uses internal models to estimate tilt and translation in the presence of ambiguous sensory cues.

The Faster Detection of the Step Initiation and the Prediction of the First Step’s Heel Strike Time with the Vertical GRF

A new approach for the detection of the step initiation in the lower extremity exoskeleton is presented. As the detection of the step initiation is the important factor for the lower extremity exoskeleton to shadow the operator’s movement as soon as possible, many studies have been done to detect it faster by using heel-off time or toe-off time. We detect the step initiation faster than other approaches with the vertical ground reaction forces. Also, we predict the first step’s heel strike time with the regression equations based on the vertical ground reaction forces as soon as we detect the step initiation. It could enable the lower extremity exoskeleton to shadow the operator’s movement much faster.

Human Postural Response to Linear Perturbation

Human postural responses appeared to have stereotyped modality, such as ankle mode, knee mode and hip mode in response to various perturbations. We examined whether human postural control gain of full-state feedback could be decoupled along with the eigenvector. To verify the model, postural responses subjected to fast backward perturbation were used. Upright posture was modeled as 3-segment inverted pendulum incorporated with feedback control, and joint torques were calculated using inverse dynamics. Postural modalities such as ankle, knee and hip mode were obtained from eigenvectors of biomechanical model. As oppose to the full-state feedback control, independent eigenvector control assumes that modal control input is determined by the linear combination of corresponding modality. We used optimization method to obtain and compare the feedback gains for both independent eigenvector control and full-state feedback control. As a result, we found that simulation result of eigenvector feedback was not competitive in comparison with that of full-state feedback control. This implies that the CNS would make use of full-state body information to generate compensative joint torques.

Human Postural Feedback Response Described by Eigenvector

Human postural responses appeared to have stereotyped modality, such as ankle mode, knee mode, and hip mode in response to various levels of postural challenges. We examined whether human postural control gain of full-state feedback could be decoupled along with the eigenvectors. To verify the model, postural responses subjected to fast backward perturbation were used. Upright posture was modeled as 3-segment inverted pendulum incorporated with linear feedback control, and joint torques were calculated using inverse dynamics. Postural modalities, such as ankle, knee and hip mode, were obtained from eigenvectors of biomechanics model. As oppose to the full-state feedback control, independent modal control assumes that modal control input is determined by the linear combinations of corresponding modality. We used linear regression to obtain and compare the feedback gains for both eigenvector control gain and full-state feedback. As a result, we found that both feedback gains of two control models that fit the joint torque data are reasonably closed each other especially at the joint angle feedback gains. This implies that the simple parameterization using eigenvectors may be used to correlate the feedback gains of full-state feedback control.

Effect of Initial Lean on Scaling of Postural Feedback Responses

We examined how the central nervous system adjusts postural responses to an increased postural challenge due to an initial lean. Postural feedback responses scale to accommodate biomechanical constraints, such as an allowable ankle joint torque. Initial forward leaning, which is observed among the elderly who are inactive or afraid of falling, brings subjects near to the limit of stability and makes the biomechanical constraints more difficult to obey. We hypothesized that the central nervous system is aware of body dynamics and restrains postural responses when subjects initially lean forward. To test this hypothesis, fast backwards perturbations of various magnitudes were applied to 12 healthy young subjects (3 male, 9 female) aged 20 to 32 years. The subjects were instructed to stand quietly on a hydraulic servo-controlled force platform with their arms crossed over their chests, then to recover from a perturbation by returning to their upright position, without stepping or lifting their heels off the ground, if possible. Initially, the subjects were either standing upright or leaning forward. The force platform was movable in the translational direction and programmed to move backward with various ramp displacements ranging from 1.2 to 15 cm, all with the duration of 275 msec. For each trial, the kinematics and ground reaction force data were recorded, then used to compute the net joint torques, employing a least squares inverse dynamics method. Optimization methods were used to identify a set of equivalent feedback control gains for each trial so that the biomechanical model incorporating this feedback control would reproduce the empirical response. The results showed that the kinematics, joint torque, and feedback gains gradually scaled as a function of the perturbation magnitude before they reached the biomechanical constraint, and the scaling became more severe with an initial forward lean. For example, the model suggested that the magnitude of the ankle joint angle feedback to ankle torque was smaller in the leaning trials than in the initially upright trials, as if the subjects experienced a larger postural perturbation in the leaning trials. These results imply that the central nervous system restrained the postural responses to accommodate the additional biomechanical constraint imposed by the forward posture, thereby suggesting that the central nervous system is aware of body dynamics and biomechanical constraints. The scaling of the postural feedback gains with the perturbation magnitude and initial lean indicates that the postural control can be interpreted as a feedback scheme with scalable gains.

An optimal bending stiffness of running shoes to improve running efficiency

between DLS and SWB, normalized to bodyweight (BW) and dAHI represents the corresponding change in arch height index (AHI), the ratio of DH to FL. Statistical comparisons between males and females were made using student’s t-tests (a D 0.05). Scatter plots and Pearson product–moment correlations were used to investigate potential relationships among FM, FS and measures of body anthropometry, such as height, weight and body mass index.

Loaded Versus Unloaded Gait Balance Stability: A Measure of Dynamic Walking

Several stability indices exist in the literature, each within their contexts and perspectives of quantification. However, no relevant index for the quantification of gait balance stability has been rigorously developed. Here, the novel Dynamic Gait Measure (DGM) is used to characterize the distinct gait balance stability of loaded walking, as compared to normal human walking. The DGM quantifies the normalized effects of inertia of a given gait with respect to the time-varying foot support region. The DGM is formulated in terms of the gait parameters reflecting a given gait strategy, and is extended to multiple steps of the gait cycle. The altered gait kinematics observed during load carriage (decreased single support duration, inertia effects, and step length) results in decreased DGM values (p < 0.0001), indicating that loaded walking is more statically stable compared with the unloaded walking. The DGM is compared with other common gait stability indices to validate its unique ability to catch the alteration (due to load carriage) in its corresponding gait stability characteristics.Copyright

A gain-scheduling approach to model human simultaneous visual tracking and balancing

In this study, we endeavor to better understand the human motor control system in order to help transposing some of its features onto humanoid robots. The postural coordination task investigated is related to an experimental paradigm that consists in visual target tracking task while balancing. We want to test whether the human biomechanical responses, namely phase / antiphase coordination mode transition, as exhibited during the actual experiments can be modeled by a linearized double inverted pendulum and parallel independent PD feedback control loops. Remarkably, these loops implement joint space control using cartesian task space variables. Furthermore, we want to see how the feedback control gains given by an optimization procedure scale w.r.t frequency or target motion magnitude. A closed-loop synthesis is developed that consists in minimizing a minimum torque criterion under both balance and task constraints. We show that the optimal feedback control gains obtained yield model responses consistent with the literature. In a second part, we implement a gain-scheduling approach where control gains values are predicted via interpolation. Finally, our approach implements a controller capable of achieving the task even when the frequency of the target motion varies over time.

Correlation between Balance Ability and Linear Motion Perception

In this study, we investigated the correlation between balancing ability and motion perception. To provide the condition that declined balance ability, subjects were restrained their sole sensation. The balance ability was evaluated by measuring the center of pressure (COP) during quiet standing. Body kinematics was also monitored by using the motion capture system. We quantified the motion perception by measuring the threshold of perceived direction of linear motion. This result suggests that the COP represents the balance ability for quiet standing. However COP is insufficient for representing the balance ability during body movement. To describe the stability during motion, motion perception might compensate the COP.