Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD

Community Engagement newsletter, Faculty of Veterinary Science, Summer, February 2018

Community education on small scale chicken farming / Mareli Smalberger -- Hluvukani experience / Elza Hollenbach, Melyssa van Heerden, Ivan van Wyk and Anke Venter -- Community engagement in stormy Cape Town / Lindi Smit, Carrea Greeff, Samantha Domenico and Fladia Buongiorno -- World Rabies Day: Faculty partners with GDARD and community partners at Temba Animal Clinic / Lesego Teffu (Public Relations Coordinator) and Dr Eugene Machimana (Senior Lecturer) -- Say bye to rabies, Mmammundu Primary School / Kenja-Lee MillarNews articles with colour photos about the various community engagement projects of the Faculty of Veterinary Science, University of Pretoria.ab201

Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques

The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line

Multi-DoFs Exoskeleton-Based Bilateral Teleoperation with the Time-Domain Passivity Approach

SummaryIt is well known that the sense of presence in a tele-robot system for both home-based tele-rehabilitation and rescue operations is enhanced by haptic feedback. Beyond several advantages, in the presence of communication delay haptic feedback can lead to an unstable teleoperation system. During the last decades, several control techniques have been proposed to ensure a good trade-off between transparency and stability in bilateral teleoperation systems under time delays. These proposed control approaches have been extensively tested with teleoperation systems based on identical master and slave robots having few degrees of freedom (DoF). However, a small number of DoFs cannot ensure both an effective restoration of the multi-joint coordination in tele-rehabilitation and an adequate dexterity during manipulation tasks in rescue scenario. Thus, a deep understanding of the applicability of such control techniques on a real bilateral teleoperation setup is needed. In this work, we investigated the behavior of the time-domain passivity approach (TDPA) applied on an asymmetrical teleoperator system composed by a 5-DoFs impedance designed upper-limb exoskeleton and a 4-DoFs admittance designed anthropomorphic robot. The conceived teleoperation architecture is based on a velocity-force (measured) architecture with position drift compensation and has been tested with a representative set of tasks under communication delay (80 ms round-trip). The results have shown that the TDPA is suitable for a multi-DoFs asymmetrical setup composed by two isomorphic haptic interfaces characterized by different mechanical features. The stability of the teleoperator has been proved during several (1) high-force contacts against stiff wall that involve more Cartesian axes simultaneously, (2) continuous contacts with a stiff edge tests, (3) heavy-load handling tests while following a predefined path and (4) high-force contacts against stiff wall while handling a load. The found results demonstrated that the TDPA could be used in several teleoperation scenarios like home-based tele-rehabilitation and rescue operations

A linear optimization procedure for an EMG-driven neuromusculoskeletal model parameters adjusting: Validation through a myoelectric exoskeleton control

This paper presents a linear optimization procedure able to adapt a simplified EMG-driven NeuroMusculoSkeletal (NMS) model to the specific subject. The optimization procedure could be used to adjust a NMS model of a generic human articulation in order to predict the joint torque by using ElectroMyoGraphic (EMG) signals. The proposed approach was tested by modeling the human elbow joint with only two muscles. Using the cross-validation method, the adjusted elbow model has been validated in terms of both torque estimation performance and predictive ability. The experiments, conducted with healthy people, have shown both good performance and high robustness. Finally, the model was used to control directly and continuously a exoskeleton rehabilitation device through EMG signals. Data acquired during free movements prove the model ability to detect the human’s intention of movement

A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: A case study on shoulder and elbow joints

The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints. However, a model optimization is needed to adjust the model's parameters to a specific subject and most of the optimization approaches presented in literature consider complex NMS models that are unsuitable for being used in a control paradigm since they suffer from long-lasting setup and optimization phases. In this work we present a minimal NMS model for predicting the elbow and shoulder torques and we compare two optimization approaches: a linear optimization method (LO) and a non-linear method based on a genetic algorithm (GA). The LO optimizes only one parameter per muscle, whereas the GA-based approach performs a deep customization of the muscle model, adjusting 12 parameters per muscle. EMG and force data have been collected from 7 healthy subjects performing a set of exercises with an arm exoskeleton. Although both optimization methods substantially improved the performance of the raw model, the findings of the study suggest that the LO might be beneficial with respect to GA as the latter is much more computationally heavy and leads to minimal improvements with respect to the former. From the comparison between the two considered joints, it emerged also that the more accurate the NMS model is, the more effective a complex optimization procedure could be. Overall, the two optimized NMS models were able to predict the shoulder and elbow moments with a low error, thus demonstrating the potentiality for being used in an admittance-based myo-control scheme. Thanks to the low computational cost and to the short setup phase required for wearing and calibrating the system, obtained results are promising for being introduced in industrial or rehabilitation real time scenarios