The link between visual information and postural control was investigated based on a multi-degree-of-freedom model using the framework of the uncontrolled manifold (UCM) hypothesis. The hypothesis was that because visual information specifies the position of the body in space, it would couple preferentially into those combinations of degrees of freedom (DOFs) that move the body in space and not into combinations of DOFs that do not move the body in space. Subjects stood quietly in a virtual reality cave for 4-min trials with or without a 0.2, 2.0 Hz, or combined 0.2 and 2.0 Hz visual field perturbation that was below perceptual threshold. Motion analysis was used to compute six sagittal plane joint angles. Variance across time of the angular motion was partitioned into (1) variance associated with motion of the body and (2) variance reflecting the use of flexible joint combinations that keep the anterior-posterior positions of the head (HD(POS)) and center of mass (CM(POS)) invariant. UCM analysis was performed in the frequency domain in order to link the sensory perturbation to each variance component at different frequencies. As predicted, variance related to motion of the body was selectively increased at the 0.2-Hz drive frequency but not at other frequencies of sway for both CM(POS) and HD(POS). The dominant effect with the 2.0-Hz visual drive also was limited largely to variance related to motion of the body.

BackgroundStudies of human upright posture typically have stressed the need to control ankle and hip joints to achieve postural stability. Recent studies, however, suggest that postural stability involves multi degree-of-freedom (DOF) coordination, especially when performing supra-postural tasks. This study investigated kinematic synergies related to control of the body’s position in space (two, four and six DOF models) and changes in the head’s orientation (six DOF model).Methodology/Principal FindingsSubjects either tracked a vertically moving target with a head-mounted laser pointer or fixated a stationary point during 4-min trials. Uncontrolled manifold (UCM) analysis was performed across tracking cycles at each point in time to determine the structure of joint configuration variance related to postural stability or tracking consistency. The effect of simulated removal of covariance among joints on that structure was investigated to further determine the role of multijoint coordination. Results indicated that cervical joint motion was poorly coordinated with other joints to stabilize the position of the body center of mass (CM). However, cervical joints were coordinated in a flexible manner with more caudal joints to achieve consistent changes in head orientation.Conclusions/SignificanceAn understanding of multijoint coordination requires reference to the stability/control of important performance variables. The nature of that coordination differs depending on the reference variable. Stability of upright posture primarily involved multijoint coordination of lower extremity and lower trunk joints. Consistent changes in the orientation of the head, however, required flexible coordination of those joints with motion of the cervical spine. A two-segment model of postural control was unable to account for the observed stability of the CM position during the tracking task, further supporting the need to consider multijoint coordination to understand postural stability.

The control of upright stance is commonly explained on the basis of the single inverted pendulum model (ankle strategy) or the double inverted pendulum model (combination of ankle and hip strategy). Kinematic analysis using the uncontrolled manifold (UCM) approach suggests, however, that stability in upright standing results from coordinated movement of multiple joints. This is based on evidence that postural sway induces more variance in joint configurations that leave the body position in space invariant than in joint configurations that move the body in space. But does this UCM structure of kinematic variance truly reflect coordination at the level of the neural control strategy or could it result from passive biomechanical factors? To address this question, we applied the UCM approach at the level of muscle torques rather than joint angles. Participants stood on the floor or on a narrow base of support. We estimated torques at the ankle, knee, and hip joints using a model of the body dynamics. We then partitioned the joint torques into contributions from net, motion-dependent, gravitational, and generalized muscle torques. A UCM analysis of the structure of variance of the muscle torque revealed that postural sway induced substantially more variance in directions in muscle torque space that leave the Center of Mass (COM) force invariant than in directions that affect the force acting on the COM. This difference decreased when we decorrelated the muscle torque data by randomizing across time. Our findings show that the UCM structure of variance exists at the level of muscle torques and is thus not merely a by-product of biomechanical coupling. Because muscle torques reflect neural control signals more directly than joint angles do, our results suggest that the control strategy for upright stance involves the task-specific coordination of multiple degrees of freedom.

Gait is a significant factor that affects human health, and monitoring a person's gait with sensing devices during daily life can detect abnormal gait events that affect numerous physical health problems. In particular, flat feet can cause changes in alignment conditions of the foot, ankle, leg, pelvis and spine. The primary problem with previous studies of wearable devices for measuring gait have focused on quantitatively monitoring the degree of gait rather than the limited gait ability. The existing method of feeding back the degree of gait or activity does not consider the severity of the subject and is insufficient for qualitative evaluation or training of gait. The significance of this study is development of convenient detecting and long-term tracking tools that can be used by both patients and clinicians for prescreening flat feet and monitoring the progress of flat feet treatment. For wearable devices for flatfoot detection to be most effective, detection systems and algorithms must be accurate, robust, reliable and computationally-efficient. In this paper, we developed an integrated smart wearable gait-monitoring device comprised of three sensors: front force, rear force, and an ankle flex sensor. We propose a new flat feet detection methodology based on a dynamic sensing window and a deep neural network with scaled principal component analysis (PCA). We tested 24 subjects, including both those with healthy gait and flat-feet-affected gait. Our study shows that the proposed sensing devices could be worn comfortably. The proposed deep neural network (DNN) model outperformed the other five classifier algorithms considered, and the area under the curve (AUC) value of the method was 87.1%. This wearable device can thus be easily and simply used both by patients and doctors to monitor the progress of flat feet and prescreen for possible gait problems in daily life.