The inner ear hair cells, the receptors sensing mechanical stimuli such as acoustic vibration and acceleration, achieve remarkably high sensitivity to miniscule stimuli by selectively amplifying small inputs. The gating springs hypothesis proposes that a phenomenon called negative stiffness is responsible for the nonlinear sensitivity. According to the hypothesis, the bundle becomes more sensitive in certain region as its stiffness changes due to the opening or closing of transduction channels, which in turn exert force in the same direction of the bundle’s displacement. In this study, we developed a conceptual model of an inertial sensor inspired by the inner ear hair cells, focusing on the hair cell’s amplifying mechanism known as negative stiffness. The negative stiffness was applied to a simple mass-spring-damper system with nonlinear spring derived from gating springs hypothesis. Sinusoidal stimuli of 0.1Hz~10Hz with magnitude of 1pN to 1000pN were applied to the system to match the dynamic range of vestibular organs. Simulation on this nonlinear model was performed on MATLAB, and power transfers and sensitivities in both transient and steady states were obtained and compared with those from the system with linear spring. Parameters were chosen in relation to those of the hair bundle to reproduce operating conditions of both the hair cells and micro inertial sensors. The suggested model displayed compressive nonlinear sensitivity resulting from selective amplification of smaller stimuli despite the energy loss due to large viscous damping typical in micro systems.

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 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.

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.

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