NeBULA: A Standardized Protocol for the Benchmarking of Robotic-based Upper Limb Neurorehabilitation.

The use of robotic technologies in neurorehabilitation is growing, because they allow highly repeatable exercise protocols and patient-tailored therapies. However, there is a lack of objective methods for assessing these technologies, which makes it difficult to determine their value in rehabilitation settings. While there exist many outcome measurements for motor assessment from a clinical standpoint (such as the Fugl-Meyer scale), the evaluation of performance and clinical benefits of technology for rehabilitation still lacks a standardized approach from a technical standpoint.In this work, we describe NeBULA (Neuromechanical Biomarkers for Upper Limb Assessment), a benchmarking platform for evaluating robotic technology for upper limb neurorehabilitation. By utilizing standardized neuromechanical biomarkers, NeBULA aims at providing a groundwork for assessing and comparing neurorehabilitation robots. We describe its implementation and preliminary results assessing a novel upper limb exoskeleton.Clinical Relevance- Standardized evaluation of neurorehabilitation robots can lead to better patient outcomes, optimizing resources by identifying the most effective technology and by boosting their use in clinical practice. This would provide quantitative and objective information to complement clinical motor evaluation - preventing suboptimal treatments and ensuring that patients receive personalized care. It can also facilitate the transfer of technologyto clinics, identifying the most promising ones for further investment and research.

Model-based online implementation of spike detection algorithms for neuroengineering applications.

Traditional methods for the development of a neuroprosthesis to perform closed-loop stimulation can be complex and the necessary technical knowledge and experience often present a high barrier for adoption. This paper takes a novel Model-Based Design approach to simplifying such closed-loop system development, and thereby lowering the adoption barrier. This work implements a computational model of different spike detection algorithms in Simulink® and compares their performances by taking advantage of synthetic neural signals to evaluate suitability for the intended embedded implementation. Clinical Relevance--- Closed-loop systems have been demonstrated to be suitable for brain repair strategies. Coupling two different brain areas by means of a neuroprosthesis can potentially lead to restoration of communication by inducing activity-dependent plasticity.