Quantitative gait analysis value as a predictor of shunt surgery effectiveness in normal pressure hydrocephalus: A technical note.

Introduction: Shunt surgery (SS) remains the most effective treatment for idiopathic Normal pressure hydrocephalus (iNPH), but the selection of the patients with the greatest potential benefit remains elusive.

Objective: Identify gait features predictive of best response to SS in iNPH.

Methods: Eight patients with iNPH were assessed at baseline, after Cerebrospinal fluid tap-test (CSF-TT) and SS, with clinical scales (Clinical/Patient Global Clinical Impression, EuroQol-5D, Clinical Dementia Rating Scale(CDR), MoCA test, Hoehn-Yahr Scale) and gait analysis with inertial sensors.

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect.

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication.

Gait stride-to-stride variability and foot clearance pattern analysis in Idiopathic Parkinson's Disease and Vascular Parkinsonism.

The literature on gait analysis in Vascular Parkinsonism (VaP), addressing issues such as variability, foot clearance patterns, and the effect of levodopa, is scarce. This study investigates whether spatiotemporal, foot clearance and stride-to-stride variability analysis can discriminate VaP, and responsiveness to levodopa. Fifteen healthy subjects, 15 Idiopathic Parkinson's Disease (IPD) patients and 15 VaP patients, were assessed in two phases: before (Off-state), and one hour after (On-state) the acute administration of a suprathreshold (1.

Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop.

Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge.