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Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we present the MyoCeption, a comprehensive setup, which enables intuitive modeling of the user's musculoskeletal system, as well as proportional stimulation of the muscles with 16-bit resolution through up to 10 channels. The system can be used to provide open-loop force control, which, if coupled with an adequate body tracking system, can be used to implement an impedance control where the control loop is closed around the body posture. The system is completely self-contained and can be used in a wide array of scenarios, from rehabilitation to VR to teleoperation. Here, the MyoCeption's control environment has been experimentally validated through comparison with a third-party simulation suite. The results indicate that the musculoskeletal model used for the MyoCeption provides muscle geometries that are qualitatively similar to those computed in the baseline model.

The focus of this study is to discuss the metrological performance of a first prototype of a vision-based measurement system targeted to in-production dimensional quality control of turned parts. The instrument exploits telecentric optical profilometry, is characterized by short inspection time and is suitable for automated charge/discharge of the parts to be inspected. Section II discusses those mechanical arrangements targeted to optimize images acquired as well as the main issues that eventual mechanical misalignments of components might introduce in the quality of images. Section III describes a comparison analysis performed on measurements collected both with the aforementioned instrument and other on-the-market devices commonly used in production line. The data acquired are presented in section IV and were useful to evaluate the metrological performance of each instrument and the combination of the variability of the instruments and the variability of the parts.

Industry 4.0 has pushed online quality control to be distributed along the production line, therefore, data are acquiring a progressively more important role. The quality of the collected data necessarily depends on the uncertainty of measurement and for this reason, today the main focus is to reduce this uncertainty to the minimum level. In this study the measurement uncertainty of a hand-held non-contact laser scanner (G3F) and the uncertainty associated to the measurement process, i.e. the uncertainty associated to the whole measurement process when the device is operated in the production line by human operators, were evaluated. Data were collected from a Toyota Auris and each measuring point was inspected at least 20 times by three different operators on different parts. This data set has been, at first, processed by a type A uncertainty analysis. Then, a Gage R&R analysis helped investigate reproducibility, repeatability and the system variability.

Thanks to the availability of a significant amount of inexpensive commercial Frequency Modulated Continuous Wave Radar sensors, designed primarily for the automotive domain, it is interesting to understand if they can be used in alternative applications. Radars designed for automotive applications, in fact, thanks to their high transmission frequency, large bandwidth and very short chirp signals, are able to provide sub-millimeter resolution and a large detection bandwidth, to the point that it is here proposed to implement a vibrational analysis of a target and evaluate what information on the vibrations can be extracted, and the performance obtainable. In the present work the use of a commercial Frequency Modulated Continuous Wave radar is described, and the performances achieved in terms of target's displacement and frequency measurement are compared with the measurement results obtained through a laser vibrometer, considered as the reference instrument.

This paper presents the wave tank test results on an innovative system for converting mechanical energy from waves directly to electricity. It consists of a point-pivoted buoy that oscillates in presence of waves and a ball screw based electro-mechanical generator. A damping force was applied for the system control by connecting the generator to an electrical load. The numerical model of the complete system and power conversion has been developed and simulation results allowed predicting the system performances. The prototype was equipped with all necessary instrumentation to measure the wave-to-mechanical efficiency (i.e. the hydrodynamic efficiency of the buoy) and the mechanical-to-electrical efficiency (i.e. the efficiency of ball screw based generator): in this way, the wave-to-wire conversion efficiency of the whole system has been characterized. The device and electro-mechanical generator design are firstly described. Then the experimental test campaign results on the scaled system with regular wave inputs are shown and discussed. Finally, a comparison between experimental and numerical model results is made.

Myocontrol is the control of an assistive device via the interpretation of the subject's intent using surface electromyography, and one paradigmatic instance of myocontrol is in upper-limb prosthetics applications. The reliability of this kind of control remains a key issue - effective and stable upper-limb myocontrol is one of the most interesting open problems in the field of human-robot interfaces and rehabilitation. In this work we focused on the myocontrol of a prosthetic hand while grasping: performing grasp actions only when, and exactly for the duration, the user desires, avoiding failures that can lead to frustrating or catastrophic results. One specific step to improve stability in the myocontrol of prosthetic hands is the possibility to automatically detect the occurrence of a failure. For this purpose, the availability of an automatic “oracle” able to accomplish this work enables the possibility of self-adaptation of the myocontrol system - e.g. via on-demand model updates for incremental learning. According to this view, we performed an experiment using a simplified but still realistic grasping protocol involving four able-bodied expert myocontrol users, and we extracted features from a state-of-the-art commercial prosthetic hand to automatically identify instability in the myocontrol. The results show that a standard classifier is able to detect failures with a mean balanced error rate of 15.98% over the subjects that took part in the experiments. Our results can also be potentially applied in non-medical applications such as, e.g., teleoperation using extra-light interfaces.

Natural myocontrol employs pattern recognition to allow users to control a robotic limb intuitively using their own voluntary muscular activations. The reliability of myocontrol strongly depends on the signals initially collected from the users, which must appropriately capture the variability encountered later on during operation. Since myoelectric signals can vary based on the position and orientation of the limb, it has become best practice to gather data in multiple body postures. We hereby concentrate on this acquisition protocol and investigate the relative merits of collecting data either statically or dynamically. In the static case, data for a desired hand configuration is collected while the users keep their hand still in certain positions, whereas in the dynamic case, data is collected while users move their limbs, passing through the required positions with a roughly constant velocity. Fourteen able-bodied subjects were asked to naturally control two dexterous hand prostheses mounted on splints, performing a set of complex, realistic bimanual activities of daily living. We could not find any significant difference between the protocols in terms of the total execution times, although the dynamic data acquisition was faster and less tiring. This would indicate that dynamic data acquisition should be preferred over the static one.

The paper introduces the system architecture developed within the GO0DMAN project integrating process and quality control for multi-stage manufacturing plants. It shows how it fits the requirements set for the development of zero defect manufacturing. Focus is put on the quality control systems, designed to exhibit smart behaviors, aiming to enhance their performance in varying conditions. Two examples of these inspection tools are presented, one for accurate measurement of internal dimensions of turned components, the other for vibration based diagnosis of a fan using Neural Network on chip.

This paper describes the design of a Synchronous Reluctance (SynR) Machine for a Flywheel Based Energy Storage System (FBESS). A preliminary design has been obtained and has been structurally and electromagnetically analyzed by FEM analysis. In order to improve the machine performance, an optimization process has been developed and the optimized design has been analyzed, in order to characterize the overall working range in generator and motor mode.

Companies deal with different problems that must be solved. Operations Research offers a methodology (SSM) that helps to identify their solutions. This project consisted in analyzing components of a real supermarket process in order to identify process improvements. SSM helped to find feasible improvements which were analyzed applying Agile Requirements.

Myocontrol, that is control of prostheses using bodily signals, has proved in the decades to be a surprisingly hard problem for the scientific community of assistive and rehabilitation robotics. In particular, traditional surface electromyography (sEMG) seems to be no longer enough to guarantee dexterity (i.e., control over several degrees of freedom) and, most importantly, reliability. Multi-modal myocontrol is concerned with the idea of using novel signal gathering techniques as a replacement of, or alongside, sEMG, to provide high-density and diverse signals to improve dexterity and make the control more reliable. In this paper we present an offline and online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. A total number of twenty sEMG and FMG sensors were used simultaneously, in several combined configurations, to predict opening/closing of the hand and activation of two degrees of freedom of the wrist of ten intact subjects. The analysis was targeted at determining the optimal sensor combination and control parameters; the experimental results indicate that sEMG sensors alone perform worst, yielding a nRMSE of 9.1%, while mixing FMG and sEMG or using FMG only reduces the nRMSE to 5.2-6.6%. To validate these results, we engaged the subject with median performance in an online goal-reaching task. Analysis of this further experiment reveals that the online behaviour is similar to the offline one.

An immunological data-driven model is proposed, for age related changes in the network of relationships among cell quantities of eight peripheral B lymphocyte subpopulations, that is, cells exhibiting all combinations of three specific receptor clusters (CD27, CD23, CD5). The model is based on immunological data (quantities of cells exhibiting CD19, characterizing B lymphocytes) from about six thousands patients, having an age ranging between one day and ninety-five years, by means of a suitably combination of data analysis methods, such as piecewise linear regression models. With relaxed values for statistically significant models (coefficient p-values bounded by 0.05), we found a network holding for all ages, that likely represents the general assessment of adaptive immune system for healthy human beings. When statistical validation comes to be more restrictive, we found that some of these interactions are lost with aging, as widely observed in medical literature. Namely, interesting (inverse or directed) proportions are highlighted among mutual quantities of a partition of peripheral B lymphocytes.

Predicting and controlling plant behavior in controlled environments is a growing requirement in precision agriculture. In this context sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. In this paper we present a general architecture for automatic greenhouse control. In particular, we focus on a preliminary model for predicting the risk of new infections of downy mildew of basil (Peronospora belbahrii) on sweet basil. The architecture has three main elements of innovation: new kinds of sensors are used to extract information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques.