The Cutaneous Silent Period in Motor Neuron Disease

Objective: To investigate the cutaneous silent period (CSP) by measuring its onset latency, duration and amount signal suppression in patients with motor neuron disease (MND) grouped according to the intensity of upper motor neuron involvement (UMN), and to test the effect of contralateral hand contraction. Methods: Painful stimulation was applied at the V finger, and contraction recorded from the abductor digiti minimi (ADM) muscle (baseline condition). Afterwards, CSP was studied during strong contralateral ADM contraction (test condition). 10-15 consecutive traces were recorded for each condition, signals were rectified, averaged, and analyzed offline. Results: 46 patients were investigated, 15 with progressive muscular atrophy (PMA), 16 with typical amyotrophic lateral sclerosis (ALS), 15 with primary lateral sclerosis/predominant UMN-ALS (PLS+UMN-ALS), and 28 controls. In the baseline condition, all MND groups showed delayed onset latencies (p = 0.001). There was no significant difference in the CSP duration. Suppression was lower in the PLS + UMN-ALS group (p = 0.004). In the control group, contralateral contraction did not change CSP, but onset latency shortened significantly in the PMA group. Conclusions: CSP onset latency is delayed in all investigated groups of MND, including in PMA, indicating subclinical UMN involvement. Changes in CSP can indicate UMN lesion in MND. Significance: CSP should be explored to identify UMN involvement in MND.info:eu-repo/semantics/publishedVersio

Medical conferences: Value for money?

Does anything change? Why do we attend congresses? Such meetings are also popular in science and engineering, literature and the arts, business and finance. But it is time to consider what is gained

3D Depth Measurement for Holoscopic 3D Imaging System

Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms

Size Optimization of 3D Stereoscopic Film Frames

3D stereoscopic film has inherently the problem of large frame size that many 3D images compression algorithms already investigated for exploiting complex method, outputting poorly displayed frame, andtakings a long processing time. Consequently, this paper presents the development of a unique technique of clipping the redundant areas from the frames that do not appear in 3D stereoscopic film which delivered side-by-side (SbS) format over anorthodox packet-based network. This implies reducing the size of the 3D film with natural depth perception

Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks

Copyright © 2020 by the authors. The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. The CNN training results show an accuracy as high as 100%, which present superior performance in all factors. On the other hand, the validation results average about 99% accuracy. The CNN algorithm has proven to be the most accurate classification tool for micro gesture recognition.Imam Abdulrahman bin Faisal Universit

Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique

Holoscopic 3D imaging is a promising technique for capturing full-colour spatial 3D images using a single aperture holoscopic 3D camera. It mimics fly’s eye technique with a microlens array, which views the scene at a slightly different angle to its adjacent lens that records three-dimensional information onto a two-dimensional surface. This paper proposes a method of depth map generation from a holoscopic 3D image based on graph cut technique. The principal objective of this study is to estimate the depth information presented in a Holoscopic 3D image with high precision. As such, depth map extraction is measured from a single still holoscopic 3D image which consists of multiple viewpoint images. The viewpoints are extracted and utilised for disparity calculation via disparity space image technique and pixels displacement is measured with sub-pixel accuracy to overcome the issue of the narrow baseline between the viewpoint images for stereo matching. In addition, cost aggregation is used to correlate the matching costs within a particular neighbouring region using sum of absolute difference (SAD) combined with gradient-based metric and “winner takes all” algorithm is employed to select the minimum elements in the array as optimal disparity value. Finally, the optimal depth map is obtained using graph cut technique. The proposed method extends the utilisation of holoscopic 3D imaging system and enables the expansion of the technology for various applications of autonomous robotics, medical, inspection, AR/VR, security and entertainment where 3D depth sensing and measurement are a concern.NPR

Real-time Emotional State Detection from Facial Expression on Embedded Devices

From the last decade, researches on human facial emotion recognition disclosed that computing models built on regression modelling can produce applicable performance. However, many systems need extensive computing power to be run that prevents its wide applications such as robots and smart devices. In this proposed system, a real-time automatic facial expression system was designed, implemented and tested on an embedded device such as FPGA that can be a first step for a specific facial expression recognition chip for a social robot. The system was built and simulated in MATLAB and then was built on FPGA and it can carry out real time continuously emotional state recognition at 30 fps with 47.44% accuracy. The proposed graphic user interface is able to display the participant video and two dimensional predict labels of the emotion in real time together.The research presented in this paper was supported partially by the Slovak Research and Development Agency under the research projects APVV-15-0517 & APPV-15-0731 and by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the project VEGA 1/0075/15