A novel gesture recognition system based on fuzzy logic for healthcare applications

This work demonstrates an interesting approach to gesture recognition for elderly people for the purpose of health monitoring at home. The system proposes to detect disorder symptoms on the basis of gesture analysis and generate alarms, thereby finding significance in elderly healthcare. Here the gestures are tracked using Microsoft’s Kinect sensor. From each frame captured by the Kinect sensor, four centroids representing four parts of the body are calculated and from these four centroids a novel feature set is extracted in terms of Euclidean distances and angles. We have noticed that for different persons’ body types the extracted features might vary. Thus to accommodate these non-uniformities, we have used the concept of interval type-2 fuzzy logic based classification. The unknown gesture is recognized based on matching with all the known gestures from the dataset. The proposed methodology provides a high accuracy rate of 92.14%

User-Centred BCI Videogame Design

International audienceThis chapter aims to offer a user-centred methodological framework to guide the design and evaluation of Brain-Computer Interface videogames. This framework is based on the contributions of ergonomics to ensure these games are well suited for their users (i.e., players). It provides methods, criteria and metrics to complete the different phases required by ae human-centred design process. This aims to understand the context of use, specify the user needs and evaluate the solutions in order to define design choices. Several ergonomic methods (e.g., interviews, longitudinal studies, user based testing), objective metrics (e.g., task success, number of errors) and subjective metrics (e.g., mark assigned to an item) are suggested to define and measure the usefulness, usability, acceptability, hedonic qualities, appealingness, emotions related to user experience, immersion and presence to be respected. The benefits and contributions of the user centred framework for the ergonomic design of these Brain-Computer Interface Videogames are discussed

Analytical Methods for High Dimensional Physiological Sensors

abstract: This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems. The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time. The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques. The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states. The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN. The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

On the Use of Games for Noninvasive EEG-Based Functional Brain Mapping

The use of statistical models and statistical inference for characterizing the interplay between brain structures and human behavior (functional brain mapping) is common in neuroscience. Statistical methods, however, require the availability of sufficiently large data sets. As a result, experimental paradigms used to collect behavioral trials from individuals are data centered and not user centered. This means that experimental paradigms are tuned to collect as many trials as possible, are generally rather demanding, and are not always motivating or engaging for individuals. Subject cooperation and their compliance with the task may decrease over time. Whenever possible, paradigms are designed to control for factors such as fatigue, attention, and motivation. In this paper, we propose the use of the Kinect motion tracking sensor (Microsoft, Inc., Redmond, WA, USA) in a game-based paradigm for noninvasive electroencephalogram (EEG)-based functional motor mapping. Results from an experimental study with able-bodied subjects playing a virtual ball game suggest that the Kinect sensor is useful for isolating specific movements during the interaction with the game, and that the computed EEG patterns for hand and feet movements are in agreement with results described in the literature