3D-Printed Transducers for Solid Contact Potentiometric Ion Sensors: Improving Reproducibility by Fabrication Automation.

3D printing technology has become attractive in the development of electrochemical sensors as it offers automation in fabrication, customization on-demand, and reproducibility, among other features. Nonetheless, to date, solid contact potentiometric ion sensors have remained overlooked using this technology. Thus, the novelty of this work relies on demonstrating for the first time the usefulness of the multimaterial 3D printing approach to manufacture potentiometric ion-selective electrodes.

A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance.

Background: To overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the combination of neuroprosthetic systems with robotic devices has been evaluated, resulting in hybrid systems that have promising potential. However, current technology shows a lack of flexibility to adapt to the needs of any application, context or individual. The main objective of this study is the development of a new modular neuroprosthetic system suitable for hybrid FES-robot applications to meet these needs.

Experimental Validation of an Upper Limb Benchmarking Framework in Healthy and Post-Stroke Individuals: A Pilot Study.

In the context of neurorehabilitation, there have been rapid and continuous improvements in sensors-based clinical tools to quantify limb performance. As a result of the increasing integration of technologies in the assessment procedure, the need to integrate evidence-based medicine with benchmarking has emerged in the scientific community. In this work, we present the experimental validation of our previously proposed benchmarking scheme for upper limb capabilities in terms of repeatability, reproducibility, and clinical meaningfulness.

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks.

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way.

Is Lyapunov exponent a reliable metric to detect dynamic stability in Parkinson's disease?

Idiopathic Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide. It affects the nervous system, causing motor and non-motor symptomatology. However, its clinical diagnosis remains dependent on the expertise of clinicians, as perceptual clinical scales are often used.