Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Proposal of a new clinical protocol for evaluating fatigability in adult SMA patients.

Objective: Spinal Muscular Atrophy (SMA) is a genetic neuromuscular disease affecting the lower motor neuron, carrying a significant burden on patients' general motor skills and quality of life, characterized by a great variability in phenotypic expression. As new therapeutic options make their appearance on the scene, sensitive clinical tools and outcome measures are needed, especially in adult patients undergoing treatment, in which the expected clinical response is a mild improvement or stabilization of disease progression. Methods: Here, we describe a new functional motor scale specifically designed for evaluating the endurance dimension for the upper and lower limbs in adult SMA patients. Results: The scale was first tested in eight control healthy subjects and then validated in ten adult SMA patients, proving intra- and inter-observer reliability. We also set up an evaluation protocol by using wearable devices including surface EMG and accelerometer. Conclusions: The endurance evaluation should integrate the standard clinical monitoring in the management and follow-up of SMA adult patients.

A robotic model to investigate human motor control.

The role of the mechanical properties of the neuromuscular system in motor control has been investigated for a long time in both human and animal subjects, mainly through the application of mechanical perturbations to the limb during natural movements and the observation of its corrective responses. These methods have provided a wealth of insight into how the central nervous system controls the limb. They suffer, however, from the fact that it is almost impossible to separate the active and passive components of the measured arm stiffness and that the measurement may themselves alter the stiffness characteristic of the arm. As a complement to these analyses, the implementation of a given neuroscientific hypothesis on a real mechanical system could overcome these measurement artifact and provide a tool that is, under full control of the experimenter, able to replicate the relevant functional features of the human arm. In this article, we introduce the NEURARM platform, a robotic arm intended to test hypotheses on the human motor control system. As such, NEURARM satisfies two key requirements. First, its kinematic parameters and inertia are similar to that of the human arm. Second, NEURARM mimics the main physical features of the human actuation system, specifically, the use of tendons to transfer force, the presence of antagonistic muscle pairs, the passive elasticity of muscles in the absence of any neural feedback and the non-linear elastic behaviour. This article presents the design and characterization of the NEURARM actuation system. The resulting mechanical behaviour, which has been tested in joint and Cartesian space under static and dynamic conditions, proves that the NEURARM platform can be exploited as a robotic model of the human arm, and could thus represent a powerful tool for neuroscience investigations.

Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions.

Generalization ability in tactile sensing for robotic manipulation is a prerequisite to effectively perform tasks in ever-changing environments. In particular, performing dynamic tactile perception is currently beyond the ability of robotic devices. A biomimetic approach to achieve this dexterity is to develop machines combining compliant robotic manipulators with neuroinspired architectures displaying computational adaptation. Here we demonstrate the feasibility of this approach for dynamic touch tasks experimented by integrating our sensing apparatus in a 6 degrees of freedom robotic arm via a soft wrist. We embodied in the system a model of spike-based neuromorphic encoding of tactile stimuli, emulating the discrimination properties of cuneate nucleus neurons based on pathways with differential delay lines. These strategies allowed the system to correctly perform a dynamic touch protocol of edge orientation recognition (ridges from 0 to 40°, with a step of 5°). Crucially, the task was robust to contact noise and was performed with high performance irrespectively of sensing conditions (sensing forces and velocities). These results are a step forward toward the development of robotic arms able to physically interact in real-world environments with tactile sensing.

The neuro-robotics paradigm: NEURARM, NEUROExos, HANDEXOS.

This work describes the neuro-robotics paradigm: the fusion of neuroscience and robotics. The fusion of neuroscience and robotics, called neuro-robotics, is fundamental to develop robotic systems to be used in functional support, personal assistance and neuro-rehabilitation. While usually the robotic device is considered as a "tool" for neuroscientific studies, a breakthrough is obtained if the two scientific competences and methodologies converge to develop innovative platforms to go beyond robotics by including novel models to design better robots. This paper describes three robotic platforms developed at the ARTS lab of Scuola Superiore Sant'Anna, implementing neuro-robotic design paradigm.