Modulation of laser-evoked pain perception and event-related potentials with non-invasive stimulation of the motor cortex

In the last two decades new techniques of non-invasive brain stimulation have been introduced that enable relatively long-lasting and reversible facilitation or inhibition of distinct cortical areas by modulating the excitability of underlying neurons. Among these methods, repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are the most widespread ones. To date, both have been successfully used to modulate various perceptual, cognitive and motor functions in healthy subjects and several diseases, including chronic pain. Their efficacy regarding acute pain perception in healthy subjects, however, is still not well-established. The aims of our studies were to investigate the effects of a novel rTMS paradigm, called continuous theta-burst stimulation (cTBS), and tDCS on laser-induced acute pain perception and laser-evoked potentials (LEPs) when applied to the motor cortex of healthy adult volunteers. In two psychophysical and two electrophysiological experiments, we have compared the effects of real cTBS and two tDCS protocols (anodal and cathodal) to those of sham stimulations. We have shown for the first time that cTBS over the motor cortex significantly alleviated laser-induced pain on both hands, accentuating on the con tralateral limb. The effect of cTBS was accompanied by reduced N2-P2 LEP amplitudes in the case of medium intensity pain. In the tDCS experiments, cathodal stimulation of the motor cortex reduced mild pain contralateral to the side of stimulation. Moreover, cathodal tDCS attenuated N2-P2 LEP components, without modulating thresholds of medium intensity pain. On the contrary, anodal tDCS facilitated laser-induced warm sensation contralateral to the side of tDCS, without affecting either pain sensation or LEPs. Our results indicate that non-invasive stimulation of the motor cortex causes antinociceptive effects that depend on the parameters of stimulation and are probably due to excitability changes in remote pain-related areas such as the operculoinsular region and the anterior cingulate cortex. These findings further strengthen the application of cTBS and tDCS in pain research, which might contribute to a more efficient manipulation of brain plasticity for therapeutic purposes

Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only

Forecasting Hands and Objects in Future Frames

This paper presents an approach to forecast future presence and location of human hands and objects. Given an image frame, the goal is to predict what objects will appear in the future frame (e.g., 5 seconds later) and where they will be located at, even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We design a new two-stream convolutional neural network (CNN) architecture for videos by extending the state-of-the-art convolutional object detection network, and present a new fully convolutional regression network for predicting future scene representations. Our experiments confirm that combining the regressed future representation with our detection network allows reliable estimation of future hands and objects in videos. We obtain much higher accuracy compared to the state-of-the-art future object presence forecast method on a public dataset

RealTimeChess: Lessons from a Participatory Design Process for a Collaborative Multi-Touch, Multi-User Game

We report on a long-term participatory design process during which we designed and improved RealTimeChess, a collaborative but competitive game that is played using touch input by multiple people on a tabletop display. During the design process we integrated concurrent input from all players and pace control, allowing us to steer the interaction along a continuum between high-paced simultaneous and low-paced turn-based gameplay. In addition, we integrated tutorials for teaching interaction techniques, mechanisms to control territoriality, remote interaction, and alert feedback. Integrating these mechanism during the participatory design process allowed us to examine their effects in detail, revealing for instance effects of the competitive setting on the perception of awareness as well as territoriality. More generally, the resulting application provided us with a testbed to study interaction on shared tabletop surfaces and yielded insights important for other time-critical or attention-demanding applications.

An Evaluation of eScience Lab Kits for Online Learning

Higher education online science courses generally lack the hands-on components essential in understanding theories, methods, and techniques in chemistry and biology. Companies like eScience Labs construct kits to facilitate online learning, which provide students with hands-on activities relevant to their science courses. In order to evaluate ease, efficacy, and comprehension of the forensic science kits by eScience Labs was completed while writing observations of the activities during and after completion; the lab manual learning objectives were compared to results of activities and two stopwatches took elapsed time of each activity to compare with the stated times in the kit manual. This method determined that the eScience manual does not provide enough information for a college freshman to fully understand the topic; however, combining these labs with professor provided online lectures would allow full comprehension of the forensic science applications or techniques. Recommendations to obtain maximum learning outcomes include requiring the completion of prerequisites like algebra and general chemistry. With these aspects combined, the eScience lab kit is a great addition to an introductory forensic science course as it provides safe and interactive hands-on activities

Load flow studies on stand alone microgrid system in Ranau, Sabah

This paper presents the power flow or load flow analysis of Ranau microgrid, a standalone microgrid in the district of Ranau,West Coast Division of Sabah. Power flow for IEEE 9 bus also performed and analyzed. Power flow is define as an important tool involving numerical analysis applied to power system. Power flow uses simplified notation such as one line diagram and per-unit system focusing on voltages, voltage angles, real power and reactive power. To achieved that purpose, this research is done by analyzing the power flow analysis and calculation of all the elements in the microgrid such as generators, buses, loads, transformers, transmission lines using the Power Factory DIGSilent 14 software to calculate the power flow. After the analysis and calculations, the results were analysed and compared