1,720,975 research outputs found
How capable is non-invasive EEG data of predicting the next movement? A mini review
No full textIn this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users
Pantograph monitoring system and method
No full textA method for automatic diagnostics of images related to pantographs, comprising the steps that consist in: capturing, by means of an image capture apparatus located in one of a plurality of image acquisition sites, an image that shows a pantograph of a locomotive, the image being taken from an aerial view during the travel of the locomotive, the image comprising the gliding area of a plurality of slippers of the pantograph; identifying, by means of a module for classifying the pantograph model, the model of the pantograph within a plurality of pantograph models, on the basis of the image captured; determining, by means of a module for classifying materials, a material of which the slippers are composed among a plurality of materials, on the basis of the pantograph model identified; and determining, by means of a module for classifying the degree of wear, a value related to the state of wear for each one of the plurality of slippers, on the basis of the type of material determined
Wavelength and Space Code Division Multiplexing in Optical Tomography - 5th EOS Topical Meeting on Advanced Imaging Techniques
No full text
Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation
No full textHuman motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation
Classifying Human Body Acceleration Patterns Using a Hierarchical Temporal Memory.
No full textThis paper introduces a novel approach to the detection of human body movements during daily life. With the sole use of one wearable wireless triaxial accelerometer attached to one's chest, this approach aims at classifying raw acceleration data robustly, to detect many common human behaviors without requiring any specific a-priori knowledge about movements. The proposed approach consists of feeding sensory data into a specifically trained Hierarchical Temporal Memory (HTM) to extract invariant spatial-temporal patterns that characterize different body movements. The HTM output is then classified using a Support Vector Machine (SVM) into different categories. The performance of this new HTM+SVM combination is compared with a single SVM using realword data corresponding to movements like "standing", "walking", "jumping" and "falling", acquired from a group of different people. Experimental results show that the HTM+SVM approach can detect behaviors with very high accuracy and is more robust, with respect to noise, than a classifier based solely on SVMs
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