Ankle anticipatory postural adjustments during gait initiation in healthy and post-stroke subjects

Background: Anticipatory postural adjustments during gait initiation have an important role in postural stability but also in gait performance. However, these first phase mechanisms of gait initiation have received little attention, particularly in subcortical post-stroke subjects, where bilateral postural control pathways can be impaired. This study aims to evaluate ankle anticipatory postural adjustments during gait initiation in chronic post-stroke subjects with lesion in the territory of middle cerebral artery. Methods: Eleven subjects with post-stroke hemiparesis with the ability to walk independently and twelve healthy controls participated in this study. Bilateral electromyographic activity of tibialis anterior, soleus and medial gastrocnemius was collected during gait initiation to assess the muscle onset timing, period of activation/deactivation and magnitude of muscle activity during postural phase of gait initiation. This phase was identified through centre of pressure signal. Findings: Post-stroke group presented only half of the tibialis anterior relative magnitude observed in healthy subjects in contralesional limb (t=2.38, p=0.027) and decreased soleus deactivation period (contralesional limb, t=2.25, p=0.04; ipsilesional limb, t=3.67, p=0.003) as well its onset timing (contralesional limb, t=3.2. p=0.005; ipsilesional limb, t=2.88, p=0.033) in both limbs. A decreased centre of pressure displacement backward (t=3.45, p=0.002) and toward the first swing limb (t=3.29, p=0.004) was observed in post-stroke subjects. Interpretation: These findings indicate that chronic post-stroke subjects with lesion at middle cerebral artery territory present dysfunction in ankle anticipatory postural adjustments in both limbs during gait initiation.

Dynamic stability metrics for the container loading problem

The Container Loading Problem (CLP) literature has traditionally evaluated the dynamic stability of cargo by applying two metrics to box arrangements: the mean number of boxes supporting the items excluding those placed directly on the floor (M1) and the percentage of boxes with insufficient lateral support (M2). However, these metrics, that aim to be proxies for cargo stability during transportation, fail to translate real-world cargo conditions of dynamic stability. In this paper two new performance indicators are proposed to evaluate the dynamic stability of cargo arrangements: the number of fallen boxes (NFB) and the number of boxes within the Damage Boundary Curve fragility test (NB_DBC). Using 1500 solutions for well-known problem instances found in the literature, these new performance indicators are evaluated using a physics simulation tool (StableCargo), replacing the real-world transportation by a truck with a simulation of the dynamic behaviour of container loading arrangements. Two new dynamic stability metrics that can be integrated within any container loading algorithm are also proposed. The metrics are analytical models of the proposed stability performance indicators, computed by multiple linear regression. Pearson’s r correlation coefficient was used as an evaluation parameter for the performance of the models. The extensive computational results show that the proposed metrics are better proxies for dynamic stability in the CLP than the previous widely used metrics.

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.