Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Step Deep Learning for Estimating Human Sleep Pose Occluded by Bed Covers

In this study, a novel sleep pose identification method has been proposed for classifying 12 different sleep postures using a two-step deep learning process. For this purpose, transfer learning as an initial stage retrains a well-known CNN network (VGG-19) to categorise the data into four main pose classes, namely: supine, left, right, and prone. According to the decision made by VGG-19, subsets of the image data are next passed to one of four dedicated sub-class CNNs. As a result, the pose estimation label is further refined from one of four sleep pose labels to one of 12 sleep pose labels. 10 participants contributed for recording infrared (IR) images of 12 pre-defined sleep positions. Participants were covered by a blanket to occlude the original pose and present a more realistic sleep situation. Finally, we have compared our results with (1) the traditional CNN learning from scratch and (2) retrained VGG-19 network in one stage. The average accuracy increased from 74.5% & 78.1% to 85.6% compared with (1) & (2) respectively.
Joint and Long Short-Term Memory Regression of Clinical Scores for Alzheimer’s Disease Using Longitudinal Data

Alzheimer’s disease (AD), the most common type of the dementia, is a progressive neurodegenerative disease that mainly affects elderly. It causes a high financial burden for patients and their families. For effective treatment of AD, it is important to identify the AD progression of clinical disease over time. As the cognitive scores can effectively indicate the disease status, the prediction of the scores using the longitudinal magnetic resonance imaging (MRI) data is highly desirable. In this paper, we propose a joint learning and clinical scores prediction method for AD diagnosis via longitudinal MRI data. Specifically, we devise a novel feature selection method that consists of a temporally constrained group LASSO model and the correntropy. The baseline MRI data is used to jointly select the most discriminative features. Then, we use the stacked long short-term memory (SLSTM) to effectively capture useful information in the input sequence to predict the clinical scores of future time points. Extensive experiments on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database are conducted to demonstrate the effectiveness of the proposed model. Our model can accurately describe the relationship between MRI data and scores, and thus it can be effective in predicting longitudinal scores.
A Two-Stage Tremor Detection Algorithm for Wearable Inertial Sensors During Normal Daily Activities

Continuous monitoring of tremor with wearable wrist sensors during normal daily activities is more difficult than in a clinical setting when subjects perform prescribed activities because some normal daily activities resemble tremor, many normal movements contain frequency content that overlaps with the tremor frequency, and the tremor amplitude has a large dynamic range during normal daily activities. We describe a novel two-stage algorithm that offers improvement at discriminating tremor from other activities. Some of this improvement is attained by using prior domain knowledge that tremor occurs over a narrow range of frequencies for an individual, but the mean tremor frequency may vary significantly between individuals in a study population. We validated the algorithm in continuous recordings from people with Parkinson’s disease and matched control subjects. The algorithm has good face validity, a low rate of false positives on recordings from control subjects (< 1.1%), and good correspondence with the constancy of rest tremor as measured by this question on the MDS-UPDRS (ρ = 0.54).
Neuroevolutive Algorithms for Learning Gaits in Legged Robots

Gait generation for legged robots is a challenging task typically requiring either a hand-tuning design or a kinematic model of the robot morphology to compute the movements, generating a high computational and time efforts. Neuroevolution algorithms with the ability to learn network topologies, such as Neuroevolution of Augmenting Topologies (NEAT), Hypercube-based NEAT (HyperNEAT), and $\tau $ -HyperNEAT, have been used in the computational community to learn gaits in legged robots. An extended version of HyperNEAT called ES-HyperNEAT, where the substrate hosting the nodes of the neural network evolves in shape, seems to be a promising algorithm to evaluate gait learning tasks. Using two four-legged robot platforms with different degrees of freedom (Quadratot and ARGOv2), we compared the performance of a variety of neuroevolution algorithms based on HyperNEAT for learning gaits. The comparative analysis of the results reveals that the three evaluated algorithms, HyperNEAT, $\tau $ -HyperNEAT and ES-HyperNEAT, successfully generate gaits given a fitness function. In particular, for the Quadratot platform, ES-HyperNEAT learns faster and better than the other two methods, a result that is not observed in the ArgoV2. Additionally, ES-HyperNEAT produces more phase changes between joints movement, resulting in more natural robot movements. Finally, ES-HyperNEAT produces complex substrates from simple Compositional Pattern Producing Networks, CPPN-NEAT, allowing the simplification of the underlying neural network.
A Low Power and Low Noise Voltage-Controlled Oscillator in 28-nm FDSOI Technology for Wireless Communication Applications

A 28-nm CMOS (FDSOI) technology is used to design and simulate the VCO presented in this paper. The tuning range of the proposed design is from 0.309 GHz to 2.92 GHz (about 161.75% tuning) with the power dissipation of 3.73 mW, at a supply voltage of 1V. In the frequency range from 0.309 to 2.92 GHz, the lowest phase noise result is -126.53 dBc/Hz at 1 MHz offset from centered oscillating frequency of 1.613 GHz. These results lead to an excellent Figure of Merit (FoM) of -184.97 dBc/Hz. A variable bias current approach is used to control the oscillation frequency. Current mirror circuit has been used to maintain wide tuning range, low power dissipation and reasonable low phase noise.
A Technique for Measuring Visuomotor Feedback Contributions to the Control of an Inverted Pendulum

We developed a new technique to measure the contributions of rapid visuomotor feedback responses to the stabilization of a simulated inverted pendulum. Human participants balanced an inverted pendulum simulated on a robotic manipulandum. At a random time during the balancing task, the visual representation of the tip of the pendulum was shifted by a small displacement to the left or right while the motor response was measured. This response was either the exerted force against a fixation position, or the motion to re-stabilize the pendulum in the free condition. Our results demonstrate that rapid involuntary visuomotor feedback responses contribute to the stabilization of the pendulum.
Evaluation of Parkinson’s Disease at Home: Predicting Tremor from Wearable Sensors

The continuous monitoring of Parkinsons’s disease (PD) symptoms would allow to automatically adjust medication or deep brain stimulation parameters to a patient’s momentary condition. Wearable sensors have been proposed to monitor PD symptoms and have been validated in a number of lab and hospital settings. However, taking these sensors into the daily life of patients introduces a number of difficulties, most notably the absence of an observable ground truth of what the user is currently doing.In this pilot study, we investigate PD symptoms by combining wearable sensors on both wrist and the chest with a questionnaire based evaluation of PD symptoms, in the form of experience sampling method. For a tremor dominant patient, we show that experienced tremor severity can be predicted from the sensor data with correlations of up to r = 0.43. We evaluated different window lengths to calculate the features in and see better results for longer window lengths. Our results show that continuous monitoring of PD symptoms in daily life is feasible using wearable sensors.
Cluster Analysis of Alcohol Consumption during Pregnancy in the Safe Passage Study

Characterization of patterns of alcohol consumption during pregnancy encompasses multiple factors such as magnitude, frequency, and timing of exposure throughout gestation. Traditional statistical models are limited in dealing with multivariate and diverse patterns of exposure as in the context of this analysis. We propose a finite mixture model-based approach to derive clusters of alcohol exposure of participants in the Safe Passage Study (PASS). Daily alcohol consumption data for 11,083 pregnant women have been clustered in 10 different exposed groups. The resulting cluster analysis was able to characterize alcohol consumption in a comprehensive framework capable of taking into account both quantity and timing of exposure as well as the occurrence of binge drinking.
Preictal Time Assessment using Heart Rate Variability Features in Drug-resistant Epilepsy Patients

Epileptic seizures are by default associated with the occurrence of EEG changes as a consequence of alterations in brain activity seconds to hours before the seizure onset. Additionally, the influence of the autonomic nervous system may also be reflected in the electrocardiogram (ECG) trace. In both biosignals (EEG and ECG), differences have been reported between normal epochs, known as interictal periods and the interval preceding seizures, preictal period. However, the existence of a preictal state indicating the transition between epileptic brain states has not yet been clinically defined. In fact, some studies report differences in preictal location and duration among patients and among seizures occurring in the same patient as well.Based on the above, a study was designed in order to investigate the existence of a preictal interval specific for each seizure, using heart rate variability (HRV) features. Time and frequency domain features (linear and non-linear) were extracted from ECG data acquired in 37 drug-resistant epilepsy patients, comprised in EPILEPSIAE database. A total of 209 seizures occurring in the temporal lobe were analysed. The existence of a transition period before the seizure onset was inspected using a linear discriminant analysis classifier.The overall best performance (88.04%±12.30% of accuracy) was obtained by combining RRMean, NN50 and SD2 features when discriminating the 50 min of interictal located farthest from seizure onset from the nearest preictal 50 min.
A validity and reliability study of Conditional Entropy Measures of Pulse Rate Variability*

In this work, we present the feasibility to use a simpler methodological approach for the assessment of the short-term complexity of Heart Rate Variability (HRV). Specifically, we propose to exploit Pulse Rate Variability (PRV) recorded through photoplethysmography in place of HRV measured from the ECG, and to compute complexity via a linear Gaussian approximation in place of the standard model-free methods (e.g., nearest neighbor entropy estimates) usually applied to HRV. Linear PRV-based and model-free HRV-based complexity measures were compared via statistical tests, correlation analysis and Bland-Altman plots, demonstrating an overall good agreement. These results support the applicability of the simpler proposed approach, which is faster and easier-to-implement, making our approach eligible for portable/wearable devices and thus broadening the out-of-lab accessibility of autonomic indexes.
Abnormal interhemispheric functional interactions in drug-naïve adult-onset first episode psychosis patients

Previous neuroimaging studies have shown that the cortical functional dysconnectivity contributed to the etiology of schizophrenia (SCZ). However, the interhemispheric functional interactions in SCZ remain not fully understood. The clinical heterogeneity of SCZ might lead to the variations in the findings and the effect of medication had a profound influence on the patient’s functional features. In this study, we were aimed at investigating the interhemispheric functional interactions in drug-naïve adult-onset first episode psychosis (FEP) patients. Using voxel-mirrored homotopic connectivity (VMHC) analysis for functional magnetic resonance imaging (fMRI) data, we found decreased VMHC values in precuneus, posterior cingulate cortex (PCC), pallidum gyrus, inferior temporal gyrus and superior temporal gyrus in FEP patients. Additionally, the peak VMHC value of PCC was negatively correlated with the PANSS depression factor score. And the peak VMHC value of precuneus was positively correlated with the PANSS positive factor score. The results indicated that the disrupted interhemispheric functional connectivity of posterior default mode network (DMN) was related to emotion and consciousness dysregulation in FEP patients.
Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals

Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to increasing risk for embolic stroke, and therefore being in the focus of cardiologists. While the reported methods for AF detection exhibit high performances, little attention has been given to distinguishing these two arrhythmias. In this study, we propose a deep neural network architecture, which combines convolutional and recurrent neural networks, for extracting features from sequence of RR intervals. The learned features were used to classify a long term ECG signals as AF, AFL or sinus rhythm (SR). A 10-fold cross-validation strategy was used for choosing an architecture design and tuning model hyperparameters. Accuracy of 88.28 %, with the sensitivities of 93.83%, 83.60% and 83.83% for SR, AF and AFL, respectively, was achieved. After choosing optimal network structure, the model was trained on the entire training set and finally evaluated on the blindfold test set which resulted in 89.67% accuracy, and 97.20%, 94.20%, and 77.78% sensitivity for SR, AF and AFL, respectively. Promising performances of the proposed model encourage continuing development of highly specific AF and AFL detection procedure based on deep learning. Distinction between these two arrhythmias can make therapy more efficient and decrease the recovery time to normal heart rhythm.
Cyber Insurance of Information Systems: Security and Privacy Cyber Insurance Contracts for ICT and Helathcare Organizations

Nowadays, more-and-more aspects of our daily activities are digitalized. Data and assets in the cyber-space, both for individuals and organizations, must be safeguarded. Thus, the insurance sector must face the challenge of digital transformation in the 5G era with the right set of tools. In this paper, we present CyberSure — an insurance framework for information systems. CyberSure investigates the interplay between certification, risk management, and insurance of cyber processes. It promotes continuous monitoring as the new building block for cyber insurance in order to overcome the current obstacles of identifying in real-time contractual violations by the insured party and receiving early warning notifications prior the violation. Lightweight monitoring modules capture the status of the operating components and send data to the CyberSure backend system which performs the core decision making. Therefore, an insured system is certified dynamically, with the risk and insurance perspectives being evaluated at runtime as the system operation evolves. As new data become available, the risk management and the insurance policies are adjusted and fine-tuned. When an incident occurs, the insurance company possesses adequate information to assess the situation fast, estimate accurately the level of a potential loss, and decrease the required period for compensating the insured customer. The framework is applied in the ICT and healthcare domains, assessing the system of medium-size organizations. GDPR implications are also considered with the overall setting being effective and scalable.
Smart Warehouse Management System with RFID and Cloud Database

RFID based automated warehouse management and also object detection and accessing the data remotely with cloud storage technology is proposed in this paper. The RFID technology helps better to specify every RF Tag which contains unique id each. In a warehouse, every items or container which have RF Tags can be observed and accessed efficiently. And this efficiency increases with the cloud storing facility. To this purpose, this paper introduced the scanning multiple RF Tags and store the Tag and its positions in the cloud. NODEMCU is a very small device to handle cloud with storing data via Wi-Fi and Arduino to control the scan process. As the warehouse or household items and its position are stored in the cloud so it can remotely be accessed by web or smartphone application.
A Review on Retinal Vessel Segmentation and Classification Methods

Retinal vessel segmentation is the essential attribute for retinal disease identification as it segments the retinal vasculature structure from fundus image. It provides essential information of the retina for disease detection like-diabetic retinopathy, hypertension, glaucoma and other diseases. This paper investigates the various vessel segmentation approaches performed on 2- D retinal images, taken from a fundus camera and a review of the methodology is summarised in a more authentic way. We have compared the available vessel segmentation algorithms based on their performance on DRIVE and STARE dataset by considering performance metrics like accuracy, sensitivity, and specificity.
Autonomous Robot to Detect Diseased Leaves in Plants using Convolutional Neural Networks

The main aim of this proposed work is to build a robot that can detect diseased leaves among normal leaves. A smartphone camera is fixed on a robot which streams live video to a laptop for performing image processing. In the proposed system, Arduino Uno is used for the robot navigation. The robot will be a 4-wheel drive system. Convolutional Neural Network (CNN) is used for the detection of the diseased leaves from a group of normal and diseased leaves.
Subjective Evaluation of Haptic Feedback Technologies for Interactive Multimedia

Current trends in the development of remote control devices for user interaction with multimedia equipment and applications are increasingly advancing the traditional keypad-based technologies, in order to improve the functionality of remote control devices and also to enhance the user experience. Haptic feedback allows the user to have a similar sensation to that provided by conventional buttons, with the advantage of a much greater flexibility, including dynamic choice of mechanical effects capable of discriminating between different functions. However, selection of haptic feedback effects that are capable of enhancing the user experience remains a great challenge for engineers and product designers. This work is a contribution towards this goal by presenting a usability study of two technologies (eccentric rotating mass and linear resonant actuator) capable of providing different haptic feedback effects in remote control devices. A subjective evaluation study was carried out to obtain subjective scores for a significant number of haptic feedback effects. The number of preferred actuators of each type and whether haptic feedback is suitable in remote control devices for interactive multimedia, were also evaluated. The results of this study show that most users prefer an interface with haptic feedback and different scores were obtained for different haptic feedback effects, according to the characteristics and number of the actuators.
Effects of an Exoskeleton-Assisted Gait Motor Imagery Training in Functional Brain Connectivity

Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed during exoskeleton-assisted gait motor imagery (MI) training. Partial Directed Coherence (PDC) analysis was employed to assess the exchange of information flow between EEG signals during gait MI in four healthy subjects, two using an exoskeleton and two without using it. Besides, in order to explore the functional connectivity, an outflow index based on the number of significant directed connectivities revealed by the PDC analysis is proposed. We found that the outflow index increases in the central zone (C2, C3, C4) while decreases in the central-parietal (CP1, CP2) and fronto-central (FC1) zones when the training was assisted by an exoskeleton. The results obtained can be useful to obtain informative features for BCI applications as well as in motor rehabilitation.
Participatory Prototyping to Inform the Development of a Remote UX Design System in the Automotive Domain

This study reports on the empirical findings of participatory design workshops for the development of a supportive automotive user experience design system. Identifying and addressing this area with traditional research methods is problematic due to the different user experience (UX) design perspectives that might conflict and the related limitations of the automotive domain. To help resolve this problem, we conducted research with 12 user experience (UX) designers through individual participatory prototyping activities to gain insights into their explicit, observable, tacit and latent needs. These activities allowed us to explore their motivation to use different technologies; the system’s architecture; detailed features of interactivity; and to describe user needs including efficiency, effectiveness, engagement, naturalness, ease of use, information retrieval, self-image awareness, politeness, and flexibility. Our analysis led us to design implications that translate participants’ needs into UX design goals, informing practitioners on how to develop relevant systems further.
Dynamic Monitoring of Vegetation Fraction by Remote Sensing in Changting County of Fujian Province

Vegetation index(VI)and the transformation method of VI are two key factors in estimating vegetation fraction by Remote Sensing.Based on the common VI and the FCD(Forest Canopy Density)Model principle put forward by ITTO(International Tropical Timber Organization),VBSI vegetation index was proposed in this paper that can decrease the interferences from mountain shadows,soil background,rocks and building.It is expressed as VBSI=f(VI,BI,SI)=VI+n·BI+SI which is suitable for estimating vegetation coverage in mountainous areas.Furthermore,mixed pixel model is one of the good transformation methods from VI to vegetation fraction,which can apply to images from different phases.The expression is:fc=(S-Snon)/(Sveg-Snon). Vegetation fraction estimation in 1994-2003 was processed with ERDAS software system aimed at Chang-ting County,Fujian Province,China.The results show that(1)With VBSI,the error of vegetation fraction resulted from the area of image shadow can be decreased,and the error is 50% of that from Normalized Difference Vegetation Index(NDVI). The overall accuracy from the vegetation fraction estimation based on VBSI and mixed pixel model is more than 80%,which meets the requirements for vegetation fraction dynamic monitoring in region. (2)The coverage of high vegetation in the study area increased by 150.47km2,or an increase of 4.9% of the total land area from 1994 to 2003.This is correlative with the work of soil and water conservation in recent years in Changting County.