A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality.

The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson's disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants' performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson's disease and cerebral palsy.

Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources.

Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

Clinical decision support system for end-stage kidney disease risk estimation in IgA nephropathy patients.

BACKGROUND: The progression of IgA nephropathy (IgAN) to end-stage kidney disease (ESKD) depends on several factors that are not quite clear and tangle the risk assessment. We aimed at developing a clinical decision support system (CDSS) for a quantitative risk assessment of ESKD and its timing using available clinical data at the time of renal biopsy. METHODS: We included a total of 1040 biopsy-proven IgAN patients with long-term follow-up from Italy (N = 546), Norway (N = 441) and Japan (N = 53). Of these, 241 patients reached ESKD: 104 Italian [median time to ESKD = 5 (3-9) years], 134 Norwegian [median time to ESKD = 6 (2-11) years] and 3 Japanese [median time to ESKD = 3 (2-12) years]. We independently trained and validated two cooperating artificial neural networks (ANNs) for predicting first the ESKD status and then the time to ESKD (defined as three categories: ≤ 3 years, between > 3 and 8 years and over 8 years). As inputs we used gender, age, histological grading, serum creatinine, 24-h proteinuria and hypertension at the time of renal biopsy. RESULTS: The ANNs demonstrated high performance for both the prediction of ESKD (with an AUC of 89.9, 93.3 and 100% in the Italian, Norwegian and Japanese IgAN population, respectively) and its timing (f-measure of 90.7% in the cohort from Italy and 70.8% in the one from Norway). We embedded the two ANNs in a CDSS available online (www.igan.net). Entering the clinical parameters at the time of renal biopsy, the CDSS returns as output the estimated risk and timing of ESKD for the patient. CONCLUSIONS: This CDSS provides useful additional information for identifying 'high-risk' IgAN patients and may help stratify them in the context of a personalized medicine approach.

Investigating the Potential of Commercial-Grade Carbon Black-Filled TPU for the 3D Printing of Compressive Sensors.

The present research aims to exploit commercially available materials and machines to fabricate multilayer, topologically designed transducers, which can be embedded into mechanical devices, such as soft or rigid grippers. Preliminary tests on the possibility of fabricating 3D-printed transducers using a commercial conductive elastomeric filament, carbon black-filled thermoplastic polyurethane, are presented. The commercial carbon-filled thermoplastic polyurethane (TPU), analyzed in the present paper, has proven to be a candidate material for the production of 3D printed displacement sensors. Some limitations in fabricating the transducers from a 2.85 mm filament were found, and comparisons with 1.75 mm filaments should be conducted. Moreover, further research on the low repeatability at low displacements and the higher performance of the hollow structure, in terms of repeatability, must be carried out. To propose an approach that can very easily be reproduced, only commercial filaments are used.

Modeling and Design Optimization of a Rotational Soft Robotic System Driven by Double Cone Dielectric Elastomer Actuators.

Dielectric elastomers (DEs) consist of highly compliant electrostatic transducers which can be operated as actuators, by converting an applied high voltage into motion, and as sensors, since capacitive changes can be related to displacement information. Due to large achievable deformation (on the order of 100%) and high flexibility, DEs appear as highly suitable for the design of soft robotic systems. An important requirement for robotic systems is the possibility of generating a multi degree-of-freedom (MDOF) actuation. By means of DE technology, a controllable motion along several directions can be made possible by combining different membrane actuators in protagonist-antagonist configurations, as well as by designing electrode patterns which allow independent activation of different sections of a single membrane. However, despite several concepts of DE soft robots have been presented in the recent literature, up to date there is still a lack of systematic studies targeted at optimizing the design of the system. To properly understand how different parameters influence the complex motion of DE soft robots, this paper presents an experimental study on how geometry scaling affects the performance of a specific MDOF actuator configuration. The system under investigation consists of two cone DE membranes rigidly connected along the outer diameter, and pre-compressed out-of-plane against each other via a rigid spacer. The electrodes of both membranes are partitioned in four sections that can be activated separately, thus allowing the desired MDOF actuation feature. Different prototypes are assembled and tested to study the influence of the inner radius as well as the length of the rigid spacer on the achievable motion range. For the first experimental study presented here, we focus our analysis on a single actuation variable, i.e., the rotation of the rigid spacer about a fixed axis. A physics-based model is then developed and validated based on the collected experimental measurements. A model-based investigation is subsequently performed, with the aim of studying the influence of the regarded parameters on the rotation angle. Finally, based on the results of the performed study, a model-based optimization of the prototype geometry is performed.

Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data.

The development of an efficient and accurate method for extra-virgin olive oils cultivar and origin authentication is complicated by the broad range of variables (e.g., multiplicity of varieties, pedo-climatic aspects, production and storage conditions) influencing their properties. In this study, artificial neural networks (ANNs) were applied on several analytical datasets, namely standard merceological parameters, near-infra red data and 1H nuclear magnetic resonance (NMR) fingerprints, obtained on mono-cultivar olive oils of four representative Apulian varieties (Coratina, Ogliarola, Cima di Mola, Peranzana). We analyzed 888 samples produced at a laboratory-scale during two crop years from 444 plants, whose variety was genetically ascertained, and on 17 industrially produced samples. ANN models based on NMR data showed the highest capability to classify cultivars (in some cases, accuracy>99%), independently on the olive oil production process and year; hence, the NMR data resulted to be the most informative variables about the cultivars.

Combining genetic algorithms and Lyapunov-based adaptation for online design of fuzzy controllers.

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark.

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.