Evaluation of the Brain Wave as Biometrics in a Simulated Driving Environment

In the case of user management, continuous or on-demand biometric authentication is effective for achieving higher security with a light system load. However, it requires us to present biometric data unconsciously. In this paper, we focus attention on the brain wave as unconscious biometrics. In particular, assuming driver authentication, we measure the brain waves of drivers when they are using a simplified driving simulator. We evaluate verification performance using 23 subjects and obtain the EER of about 20 %

Unconscious Biometrics for Continuous User Verification

In user management system, continuous or successive (ondemand) authentication is required to prevent identity theft. In particular, biometrics of which data are unconsciously presented to authentication systems is necessary. In this paper, brain waves and intra-palm propagation signals are introduced as biometrics and their verification performances using actually measured data are presented

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Potential applications for virtual and augmented reality technologies in sensory science

peer-reviewedSensory science has advanced significantly in the past decade and is quickly evolving to become a key tool for predicting food product success in the marketplace. Increasingly, sensory data techniques are moving towards more dynamic aspects of sensory perception, taking account of the various stages of user-product interactions. Recent technological advancements in virtual reality and augmented reality have unlocked the potential for new immersive and interactive systems which could be applied as powerful tools for capturing and deciphering the complexities of human sensory perception. This paper reviews recent advancements in virtual and augmented reality technologies and identifies and explores their potential application within the field of sensory science. The paper also considers the possible benefits for the food industry as well as key challenges posed for widespread adoption. The findings indicate that these technologies have the potential to alter the research landscape in sensory science by facilitating promising innovations in five principal areas: consumption context, biometrics, food structure and texture, sensory marketing and augmenting sensory perception. Although the advent of augmented and virtual reality in sensory science offers new exciting developments, the exploitation of these technologies is in its infancy and future research will understand how they can be fully integrated with food and human responses. Industrial relevance: The need for sensory evaluation within the food industry is becoming increasingly complex as companies continuously compete for consumer product acceptance in today's highly innovative and global food environment. Recent technological developments in virtual and augmented reality offer the food industry new opportunities for generating more reliable insights into consumer sensory perceptions of food and beverages, contributing to the design and development of new products with optimised consumer benefits. These technologies also hold significant potential for improving the predictive validity of newly launched products within the marketplace

Odhad emocí a duševní koncentrace pomocí technik Deep Learningu

The purpose of this work is to evaluate the brain waves of humans with deep learn- ing methods and evolutionary computation techniques, and to verify the performance of applied techniques. In this thesis, we apply well–known metaheuristics and Artificial Neural Networks for classifying human mental activities using electroencephalographic signals. We developed a Brain–Computer Interface system that is able to process elec- troencephalographic signals and classify mental concentration versus relaxation. The system is able to automatically extract and learn representation of the given data. Based on scientific protocols we designed the Brain–Computer Interface experiments and we created an original and relevant data for the industrial and academic community. Our experimental data is available to the scientific community. In the experiments we used an electroencephalographic based device for collecting brain information form the subjects during specific activities. The collected data represents brain waves of subjects who was stimulated by writing tasks. Furthermore, we selected the best combination of the input features (brain waves information) using the following two metaheuristic techniques: Simulated Annealing and Geometric Particle Swarm Optimization. We applied a specific type of Artificial Neural Network, named Echo State Network, for solving the mapping between brain information and subject activities. The results indicate that it is possible to estimate the human con- centration using few electroencephalographic signals. In addition, the proposed system is developed with a fast and robust learning technique that can be easily adapted accord- ing to each subject. Moreover, this approach does not require powerful computational resources. As a consequence, the proposed system can be used in environments which are computationally limited and/or where the computational time is an important issue.Cílem práce je ohodnocení lidských mozkových vln s využitím metod hlubokého učení (deep learning) a evolučních výpočetních technik a pro ověření výkonu aplikovaných technik. V diplomové práci jsou využity dobře známé metaheuristiky a umělé neuronové sítě pro klasifikaci lidských mentálních aktivit za použití elektroencefalografických signálů. Bylo vyvinuto rozhraní mozek-počítač, které je schopno zpracovat elektroencefalografické signály a klasifikovat mentální soustředění v porovnání s relaxací. Systém je schopen automaticky extrahovat a naučit se reprezentaci daných dat. Na základě vědeckých protokolů byl navržen experiment pro rozhraní mozek-počítač a byla vytvořena původní a relevantní data pro průmyslovou a akademickou komunitu. Vygenerovaná pokusná data jsou přístupné pro vědeckou komunitu. V rámci experimentů bylo využito zařízení založené na encefalografii pro sběr mozkových signálů subjektu během specifických aktivit. Nasbíraná data reprezentují mozkové vlny subjektu, který byl stimulován psaním úloh. Dále byla vybrána nejlepší kombinace vstupních vlastností (informace o mozkové vlně) s využitím následujících dvou metaheuristických metod: simulovaného žíhání a geometrické optimalizace hejnem částic. Umělá neuronová síť, která se nazývá Echo State síť, byla aplikována pro řešení mapování mezi informacemi z mozku a aktivitami subjektu. Výsledky ukazují, že je možné odhadnout lidskou aktivitu pomocí několika encefalografických signálů. Kromě toho, navrhovaný systém je vyvinut s využitím rychlých a robustních učících technik, které mohou být jednoduše přizpůsobeny podle jednotlivých subjektů. Tento přístup navíc nevyžaduje výkonné výpočetní prostředky. V důsledku toho může být systém využit v prostředí, které jsou výpočetně omezeny a/nebo v případech, kdy výpočetní čas je důležitým hlediskem.460 - Katedra informatikyvýborn

Influence of Muscle Fatigue on Electromyogram-Kinematic Correlation During Robot-Assisted Upper Limb Training

© The Author(s) 2020. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us. sagepub.com/en-us/nam/open-access-at-sage).Introduction: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. Methods: In order to explore this, we initially assessed muscle fatigue in 10 healthy subjects using electromyogram features (average power and median power frequency) during an assist-as-needed interaction with HapticMASTER robot. Spearman’s correlation study was conducted between EMG average power and kinematic force components. Since the robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results. Results: A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8-2.5Hz and median frequency in the band of 20-450Hz are potential fatigue indicators. Also, comparing the correlation coefficients across trials indicated that correlation was reduced as the muscles were fatigued. Conclusions: Robotic assistance based on user’s performance has resulted in lesser muscle fatigue, which caused an increase in the EMG-force correlation. We now intend to utilize the electromyogram and kinematic features for the auto-adaptation of therapeutic human-robot interactions.Peer reviewedFinal Published versio

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being