• MeSH
• biosenzitivní techniky přístrojové vybavení   MeSH
• genetická variace MeSH

• Konspekt
• Biochemie. Molekulární biologie. Biofyzika
• NLK Obory
• biochemie
• chemie, klinická chemie
• NLK Publikační typ
• elektronické časopisy

• MeSH
• biosenzitivní techniky přístrojové vybavení   MeSH
• inženýrství přístrojové vybavení   MeSH
• Publikační typ
• periodika MeSH

• Konspekt
• Technika všeobecně
• NLK Obory
• technika

• MeSH
• inženýrství přístrojové vybavení   MeSH
• Publikační typ
• periodika MeSH

• Konspekt
• Technika všeobecně
• NLK Obory
• technika lékařská, zdravotnický materiál a protetika

• MeSH
• chemie MeSH
• Publikační typ
• periodika MeSH

• Konspekt
• Chemie. Mineralogické vědy
• NLK Obory
• chemie, klinická chemie

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

• MeSH
• biomechanika MeSH
• dolní končetina fyziologie   MeSH
• exoskeleton * MeSH
• lidé MeSH
• pohyb fyziologie   MeSH
• senioři MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

• MeSH
• dechová frekvence fyziologie   MeSH
• dospělí MeSH
• dýchání MeSH
• lidé středního věku MeSH
• lidé MeSH
• počítačové zpracování signálu MeSH
• polysomnografie metody   MeSH
• senzitivita a specificita MeSH
• spánek fyziologie   MeSH
• syndromy spánkové apnoe patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• elektrochemie metody   MeSH
• finanční podpora výzkumu jako téma MeSH
• klinické laboratorní techniky metody   MeSH
• polymery chemie   klasifikace   MeSH
• vlhkost MeSH

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

• MeSH
• audiovizuální záznam MeSH
• časové faktory MeSH
• dýchání * MeSH
• lidé MeSH
• monitorování fyziologických funkcí přístrojové vybavení   MeSH
• pohyb MeSH
• srdeční frekvence fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The use of microwave technology is currently under investigation for non-invasive estimation of glycemia in patients with diabetes. Due to their construction, metamaterial (MTM)-based sensors have the potential to provide higher sensitivity of the phase shift of the S21 parameter (∠S21) to changes in glucose concentration compared to standard microstrip transmission line (MSTL)-based sensors. In this study, a MSTL sensor and three MTM sensors with 5, 7, and 9 MTM unit cells are exposed to liquid phantoms with different dielectric properties mimicking a change in blood glucose concentration from 0 to 14 mmol/L. Numerical models were created for the individual experiments, and the calculated S-parameters show good agreement with experimental results, expressed by the maximum relative error of 8.89% and 0.96% at a frequency of 1.99 GHz for MSTL and MTM sensor with nine unit cells, respectively. MTM sensors with an increasing number of cells show higher sensitivity of 0.62° per mmol/L and unit cell to blood glucose concentration as measured by changes in ∠S21. In accordance with the numerical simulations, the MTM sensor with nine unit cells showed the highest sensitivity of the sensors proposed by us, with an average of 3.66° per mmol/L at a frequency of 1.99 GHz, compared to only 0.48° per mmol/L for the MSTL sensor. The multi-cell MTM sensor has the potential to proceed with evaluation of human blood samples.

• MeSH
• krevní glukóza * MeSH
• lidé MeSH
• mikrovlny MeSH
• monitorování fyziologických funkcí MeSH
• selfmonitoring glykemie * MeSH
• studie proveditelnosti MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• biosenzitivní techniky * MeSH
• biotechnologie MeSH
• chemické techniky analytické MeSH
• Publikační typ
• periodika MeSH

• Konspekt
• Biotechnologie. Genetické inženýrství
• NLK Obory
• biomedicínské inženýrství
• chemie, klinická chemie