Recognition of Intentional Violations of Active Constraints in Cooperative Manipulation Tasks

Active Constraints (ACs) are high-level control algorithms deployed to assist a human operator in man-machine cooperative tasks [1], and define regions within which it is safe for the robot to move and cut [2]. To enhance the performance in cooperative surgical tasks, adaptive constraints have been exploited to optimally adjust the provided level of assistance according to some knowledge of the task, hardware or user. In [3] Hidden Markov Models were used for the run-time detection of the user intention to leave a guidance constraint to circumvent an obstacle. In this work, we present a novel, Neural Network (NN)-based method for the runtime classification of intentional and unintentional violations of ACs, that is trained on either statistical or frequency features from the enforced constraint forces. We investigate which set of parameters yield faster and more reliable classification results, both for guidance and regional constraints

NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features

While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms of latency in executing computational kernels as soon as data is available in their internal memories, assessment of real-time features of a standard GPGPU system needs careful characterization of all subsystems along data stream path. The networking subsystem results in being the most critical one in terms of absolute value and fluctuations of its response latency. Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network Interface Card (NIC) design featuring a configurable and extensible set of network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler GPU memories. NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE (10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency KM3link - channels, but its modularity allows for a straightforward inclusion of other link technologies. To avoid host OS intervention on data stream and remove a possible source of jitter, the design includes a network/transport layer offload module with cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division Multiplexing and APElink protocols. After NaNet architecture description and its latency/bandwidth characterization for all supported links, two real world use cases will be presented: the GPU-based low level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino telescope

GPU-based Real-time Triggering in the NA62 Experiment

Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have been analyzed, pointing out that networking is the most critical one. To keep the latency of data transfer task under control, we devised NaNet, an FPGA-based PCIe Network Interface Card (NIC) with GPUDirect capabilities. For the processing task, we developed specific multiple ring trigger algorithms to leverage the parallel architecture of GPUs and increase the processing throughput to keep up with the high event rate. Results obtained during the first months of 2016 NA62 run are presented and discussed

Static and dynamic evaluation of pelvic floor disorders with an open low-field tilting magnet.

AIM: To assess the feasibility of magnetic resonance defaecography (MRD) in pelvic floor disorders using an open tilting magnet with a 0.25 T static field and to compare the results obtained from the same patient both in supine and orthostatic positions. MATERIALS AND METHODS: From May 2010 to November 2011, 49 symptomatic female subjects (mean age 43.5 years) were enrolled. All the patients underwent MRD in the supine and orthostatic positions using three-dimensional (3D) hybrid contrast-enhanced (HYCE) sequences and dynamic gradient echo (GE) T1-weighted sequences. All the patients underwent conventional defaecography (CD) to correlate both results. Two radiologists evaluated the examinations; inter and intra-observer concordance was measured. The results obtained in the two positions were compared between them and with CD. RESULTS: The comparison between CD and MRD found statistically significant differences in the evaluation of anterior and posterior rectocoele during defaecation in both positions and of rectal prolapse under the pubo-coccygeal line (PCL) during evacuation, only in the supine position (versus MRD orthostatic: rectal prolapse p < 0.0001; anterior rectocoele p < 0.001; posterior rectocoele p = 0.008; versus CD: rectal prolapse p < 0.0001; anterior rectocoele p < 0.001; posterior rectocoele p = 0.01). The value of intra-observer intra-class correlation coefficient (ICC) ranged from good to excellent; the interobserver ICC from moderate to excellent. CONCLUSION: MRD is feasible with an open low-field tilting magnet, and it is more accurate in the orthostatic position than in the supine position to evaluate pelvic floor disorders

Cows fed hydroponic fodder and conventional diet: effects on milk quality

The technology of green fodder production is especially important in arid and semiarid regions. Hydroponics improves on average the amount of crops in the same space, as traditional soil-based farming and can reduce water consumption compared to traditional farming methods. Limited research has been carried out on the use of hydroponic fodder and milk quality. A comparative study of traditional (Malta farm) and hydroponic fodder (Gozo farm) was conducted in Malta with 20 cows of the Holstein\u2013Friesian breed from two farms. Individual and bulk-tank milk samples were collected once a week for a period of 1 month in order to evaluate physical (pH, conductivity, density, freezing point) and chemical (fat, protein, ash, lactose, solid nonfat) parameters as well as mineral (Zn, Cu, Pb, Ba) content. Milk proximate and physical data were processed by analysis of variance (ANOVA) for repeated measures and an ANOVA procedure with farm and time as effects for minerals. The results indicated differences in fat content and pH, showing higher values (P &lt; 0.05) in milk samples of cows fed with the hydroponic rather than the traditional fodder; a significant time effect (P &lt; 0.001) was found in all qualitative analyses except for lactose and salts. Minerals were in the range as reported elsewhere; Cu and Pb content was significantly higher (P &lt; 0.001) in the Gozo farm than the one in Malta, whereas Zn content showed higher values in Malta (P &lt; 0.001) than Gozo. Although the proximate results were similar for both farms, except for the higher fat content for the Gozo farm, principal component analysis (PCA) revealed that milk quality for the Gozo farm was superior to that of the Malta farm. However, further studies are needed to determine the effects of different hydroponic fodder using a large herd size

A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

Industry 4.0 has promoted the concept of automation, supporting workers with robots while maintaining their central role in the factory. To guarantee the safety of operators and improve the effectiveness of the human-robot interaction, it is important to detect the movements of the workers. Wearable inertial sensors represent a suitable technology to pursue this goal because of their portability, low cost, and minimal invasiveness. The aim of this narrative review was to analyze the state-of-the-art literature exploiting inertial sensors to track the human motion in different industrial scenarios. The Scopus database was queried, and 54 articles were selected. Some important aspects were identified: (i) number of publications per year; (ii) aim of the studies; (iii) body district involved in the motion tracking; (iv) number of adopted inertial sensors; (v) presence/absence of a technology combined to the inertial sensors; (vi) a real-time analysis; (vii) the inclusion/exclusion of the magnetometer in the sensor fusion process. Moreover, an analysis and a discussion of these aspects was also developed

Kinematic and dynamic assessment of trunk exoskeleton

In Industry 4.0, wearable exoskeletons have been proposed as collaborative robotic devices to partially assist workers in heavy and dangerous tasks. Despite the recent researches, proposed prototypes and commercial products, some open issues concerning development, improvements and testing still exist. The current pilot study proposed the assessment of a proper biomechanical investigation of passive trunk exoskeleton effects on the human body. One healthy subject performed walking, stoop and semisquat tasks without, with exoskeleton no support and with exoskeleton with support. 3D Kinematic (angles, translations) and dynamic (interface forces) parameters of both human and exoskeleton were estimated. Some differences were pointed out comparing task motions and exoskeleton conditions. The presented preliminary test revealed interesting results in terms of different human joints coordination, interface forces exchanged at contact points and possible misalignment between human and device. The present study could be considered as a starting point for the investigation of exoskeleton effectiveness and interaction with the user

Evaluation of spinal posture during gait with inertial measurement units

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt–twist method was adopted to evaluate flexion–extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context

Detection of upper limb abrupt gestures for human–machine interaction using deep learning techniques

In the manufacturing industry the productivity is contingent on the workers' well-being, with operators at the center of the production process. Moreover, when human-machine interaction occurs, operators' safety is a key requirement. Generally, typical human gestures in manipulation tasks have repetitive kinetics, however external disturbances or environmental factors might provoke abrupt gestures, leading to improper interaction with the machine. The identification and characterization of these abrupt events has not yet been thoroughly studied. Accordingly, the aim of the current research was to define a methodology to ready identify human abrupt movements in a workplace, where manipulation activities are carried out. Five subjects performed three times a set of 30 standard pick-and-place tasks paced at 20 bpm, wearing magneto-inertial measurement units (MIMUs) on their wrists. Random visual and acoustic alarms triggered abrupt movements during standard gestures. The recorded signals were processed by segmenting each pick-and-place cycle. The distinction between standard and abrupt gestures was performed through a recurrent neural network applied to acceleration signals. Four different pre-classification methodologies were implemented to train the neural network and the resulting confusion matrices were compared. The outcomes showed that appropriate preprocessing of the data allows more effective training of the network and shorter classification time, enabling to achieve accuracy greater than 99% and F1-score better than 90%