Human upright posture is inherently unstable. To counter the mechanical effect of a large-scale perturbation such as a slip, the CNS can make adaptive adjustments in advance to improve the stability of the body center-of-mass (COM) state (i.e., its velocity and position). Such feedforward control relies on an accurate internal representation of stability limits, which must be a function of anatomical, physiological, and environmental constraints and thus should be computationally deducible based on physical laws of motion. We combined an empirical approach with mathematical modeling to verify the hypothesis that an adaptive improvement in feedforward control of COM stability correlated with a subsequent reduction in balance loss. Forty-one older adults experienced a slip during a sit-to-stand task in a block of slip trials, followed by a block of nonslip trials and a re-slip trial. Their feedforward control of COM stability was quantified as the shortest distance between its state measured at seat-off (slip onset) and the mathematically predicted feasible stability region boundary. With adaptation to repeated slips, older adults were able to exponentially reduce their incidence of falls and backward balance loss, attributable significantly to their improvement in feedforward control of stability. With exposure to slip and nonslip conditions, subjects began to select "optimal" movements that improved stability under both conditions, reducing the reliance on prior knowledge of forthcoming perturbations. These results can be fully accounted for when we assume that an internal representation of the COM stability limits guides the adaptive improvements in the feedforward control of stability.
The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.
This study provides a constructive replication of Tsao and Weismer (1997), showing a difference between slow and fast talkers with a new set of speech materials and in a new task. The findings appear to be consistent with a biological basis for intertalker rate differences.
We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).
The electronic records of 398 patients with chronic spontaneous urticaria (CSU) who had had a serum basophil histamine release assay (BHRA) performed as a marker of functional autoantibodies were audited. The BHRA was positive in 105 patients (26.4%). Fifty eight were treated with ciclosporin because they were H1 anti-histamine unresponsive. CSU patients with a positive BHRA were more likely to respond clinically (P<0.001) and to have raised thyroid autoantibodies (P<0.02) than those with a negative BHRA. The BHRA offers a useful predictive biomarker for a good response of H1 antihistamine-unresponsive CSU patients to ciclosporin.
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