Wearable optical fiber sensor based on a bend singlemode-multimode-singlemode fiber structure for respiration monitoring

Respiration rate (RR) is an important information related to human physiological health. A wearable optical fiber sensor for respiration monitoring based on a bend singlemode-multimodesinglemode (SMS) fiber structure, which is highly sensitive to bend, is firstly proposed and experimentally demonstrated. The sensor fastened by an elastic belt on the abdomen of a person will acquire the respiration signal when the person breaths, which will introduce front and back movement of the abdomen, and thus bend of SMS fiber structure. Short-time Fourier transform (STFT) method is employed for signal processing to extract characteristic information of both the time and frequency domain of the measured waveform, which provides accurate RR measurement. Six different SMS fiber sensors have been tested by six individuals and the experimental results demonstrated that the RR signals can be effectively monitored among different individuals, where an average Pearson Correlation Coefficient of 0.88 of the respiration signal has been achieved, which agrees very well with that of commercial belt respiration sensor. The proposed technique can provide a new wearable and portable solution for monitoring of respiratory with advantage of easy fabrication and robust to environment

Low-Cost System for Triggering of Magnetic Resonance Imaging

Kardiovaskulární magnetická rezonance (CMRI - Cardiac Magnetic Resonance Imaging) je sada technik zobrazování magnetickou rezonancí (MRI - Magnetic Resonance Imaging) navržených k posouzení kardiovaskulární morfologie, komorové funkce, perfúze myokardu, charakterizace tkáně, kvantifikace průtoku a onemocnění koronárních tepen. Protože MRI je neinvazivní metoda bez použití ionizujícího záření, je vhodná pro dlouhodobé sledování účinku léčby a sledování průběhu onemocnění. Ve srovnání s MRI ostatních částí těla čelí CMRI specifickým problémům způsobeným srdečním a respiračním pohybem. K překonání pohybových artefaktů proto vyžaduje CMRI synchronizaci se srdeční a respirační aktivitou nebo techniky zadržování dechu. V diplomové práci jsou testovány dva nově navržené senzory (pneumatický, optický) pro monitorování základních lidských životních funkcí. Oba testované senzory jsou porovnány se systémem EKG (Elektrokardiografie), který je považován za zlatý standard CMRI.Cardiovascular magnetic resonance (CMRI) is a set of magnetic resonance imaging (MRI) techniques designed to assess cardiovascular morphology, ventricular function, myocardial perfusion, tissue characterization, flow quantification and coronary artery disease. Since MRI is a non-invasive method and without use ionizing radiation, it is suitable for longitudinal monitoring of treatment effect and follow-up of disease progress. Compared to MRI of other body parts, CMRI faces specific challenges from cardiac and respiratory motion. Therefore, CMRI requires synchronous with cardiac and respiratory activities or breath-holding techniques to overcome motion artifacts. The diploma thesis tests two newly furnished sensors (pneumatic, optical) for monitoring basic human life functions. Both tested sensors are compared with the ECG system, which is considered the gold standard CMRI.450 - Katedra kybernetiky a biomedicínského inženýrstvídobř

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system