In a laboratory demonstrator, it was demonstrated that the infrastructure realizes the visualization of sensor data in real time over a heterogeneous network. The basis of a medically valid - integrated real-time picture of the condition is an ad hoc interconnected sensor infrastructure.
Technological concept
This system approach to sensor integration in an active foot prosthesis is called real-time active prosthetics/orthotics - time management. An overview of the transmission technology of the sensor infrastructure to the real-time controller and the forwarding to the real-time visualization is depicted in Figure 4.
Hardware, sensors, actors
AAL Laboratory at Harzen University; sketch of installations; (a) sensors on the walls: pulse, blood pressure, breathing rate, skin resistance, movement/position, VOC breath analysis, (b) E-rehabilitation, (c) real-time controller. More detailed information can be found in [21] the so-called final design plan for the urgent care project.
Sensor data visualization
Further information on the exact key sensor data can be found in the publications of Stege et al. The Harz University of Applied Sciences has developed a real-time platform for sensor data fusion of partial realizations of partners as part of the Rapid Care Project.
User acceptance studies
Figure 19 illustrates that the sub-system of the project partner Otto Bock was assessed positively by the test persons. The comprehensibility of the display and results was rated with 3.58 out of 5 points (see Figure 20).
Conclusions
The comprehensibility of the instructions was more incomprehensible to the test subjects as they were overwhelmed by the technology. International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers.
TOP 1%
Introduction
- Synovial joints and osteoarthritis
- Epidemiology and impact
Osteoarthritis affects all structures within the joint and is defined as a condition that causes pain within the joint, loss of function and reduced quality of life for patients [3]. With such a large proportion of the population affected, musculoskeletal conditions including osteoarthritis have a significant impact both medically and economically.
Standard methods of detection
- X-radiography
- Magnetic resonance imaging
- Other biomarkers of osteoarthritis
- Current challenges in diagnosis and treatment
The description of the radiographic findings at different KL grades can be seen in Table 1. This was subsequently reflected in the most common use of the scale in the assessment of the knee joint. To date, all biomarkers for the disease consider circulating biochemicals or images of the knee in a static state.
Acoustic medical technologies for joint health
- Acoustic detection
This allows trends in the frequency component of the signal to be correlated with joint angle. The nature of the VAG signal means that it is not easily analyzed by conventional signal processing techniques. This sensor uses a 1 mm high cylindrical SU8 probe on each console that is in direct contact with the participant's skin and transmits vibrations to the sensor.
Conclusion
Osteoarthritis of the Knee: Comparison of Radiography, CT and MR Imaging to Assess Extent and Severity. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Materials and methods
- Sample
- fNIRS data acquisition .1 Preparation
Right-handedness was followed to limit any differences in hemodynamic responses due to differences in hemispheric dominance [13]. However, some of the detectors/sources had to be optimized during the calibration process by fixing the hair with gel. In the second phase, the subjects were asked to perform similar movements, but with random intentions.
Data analysis 1 Pre-processing
- Feature extraction
This raw light intensity data is then used to calculate the hemodynamic response of the brain. In nirsLAB, the operator can change all input parameters of the Beer-Lambert law. The same signal is smoothed, and since it is a large time series, (c) a portion of the signal is shown for closer inspection, which is for the first 5 seconds of activity during the task.
Classification process
- Linear discriminant analysis
- Artificial neural network (ANN)
It is given by the equation where N represents samples, while Xi and Xi+1 represent successive peaks in the signal. LDA works by maximizing the Fisher criterion given in Eq. The scatter matrix S_B between classes is defined as in Eq. where no represents multiple samples belonging to class i, the class scatter matrix Sw is defined as in Eq. The optimal v is the eigenvector corresponding to the largest eigenvalue that can be written as in Eq. 10) provided that Sw is nonsingular.
Results and discussions
- Channel selection
- Classification accuracy
- Control command generation
They are not compared with each other here, but they are implemented to grasp a comprehensive idea of how these different brain hemodynamic intentions can be evaluated. The movement prediction accuracy of an individual can be changed under the influence of such conditions. The controllers also emit one definite movement that can be analyzed while testing the data.
Conclusion
Applications of functional near-infrared spectroscopy (fNIRS) neuroimaging in exercise-cognitive science: A systematic, methodology-focused review. Factors influencing executive function by physical activity level in young adults: A near-infrared spectroscopy study. Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in nature/.
Splitting an electrocardiographic signal into low-frequency and high-frequency components
Moreover, only jump-type irregularities of the dynamic variable V tð Þ contribute to the emergence of dependenceΦð Þ2ð Þ, jumps and bursts (out-τ bursts) of chaotic series V tð Þ contribute to the emergence of S fð Þ. The described signal smoothing process is focused on minimizing the "high-frequency" information in the "low-frequency" part of the VRð Þt signal and, conversely, minimizing the "low-frequency" information in the "high-frequency" part of the VSð Þt signal. Such a relaxation procedure realizes the maximum degree of entropy generation and uses the ratio of entropy to Fisher information, which is a quantitative measure of the heterogeneity of the density distribution of the analyzed data set.
Parameterization of the singular component of the ECG signal
It is known from the stability theory of difference schemes that this difference scheme will be absolutely stable at ω<1=2. The parameter T0 by formula (17) will be determined by algorithm 1 assuming that the “experimental” spectrum SSð Þf is calculated by formula (16). As a result of Algorithm 1, we obtain three parameters SSð Þ ¼0 SS∗ð Þ,0 n0 ¼n0∗,T0 ¼T0∗, which characterize the interpolation expression (17) for the singular component of the spectrum SSð Þ.f.
Informative diagnostic parameters of the singular component of the ECG signal
For all considered conditions of the cardiovascular system, we obtained the same dependencies and based on the obtained dependencies, we calculated the informative parameters of the singular component of the ECG signals (table 1). The high specificity of S fð Þ samples obtained during the study of the cardiovascular system in the norm with the indicated pathologies can be used to diagnose the disease. The obtained informative parameters can be considered as distinguishing features for the differential diagnosis of cardiovascular diseases (e.g. using artificial neural networks).
Parameterization of the regular part of the ECG signal and determination of informative diagnostic parameters
SrRð Þ ¼f S fð Þ Ssð Þf (21) The resulting difference characterizes the contribution of the “resonant” components Srð Þf and the “irregularity jumps”SRð Þf to the general. LSM estimates^aen^able obtained from row f g,τk ðk¼1, …,k1Þ close to τ¼0, using representation (21) for Φ^ð Þ2 ð Þ.τ. As a result of the proposed algorithm, we obtain the three parameters σ1 ¼σ1∗, H1 ¼H1∗ and T1 ¼T1∗, which characterize the interpolation expression (21) for Φð ÞR2 ð Þ.τ.
Informative diagnostic parameters of the regular component of the ECG signal
For the considered conditions of the cardiovascular system, based on the dependencies obtained, the informative parameters of the regular component of the ECG signals were calculated (Table 2). Thus, for the considered functional conditions of the cardiovascular system, three informative parameters n0,T0,Ss(0) for the singular component of the ECG signal and three informative diagnostic parameters σ1,H1,T1 for the regular component of the ECG signal were obtained by vibration noise spectroscopy. A complex of six diagnostic parameters can be used to diagnose catastrophic conditions of the cardiovascular system (eg, using an artificial neural network, where these parameters are considered as input data).
Fluctuation dynamics of electrocardiograms and the choice of sampling frequency of the studied signals
To identify the characteristics of the analyzed signals, it is necessary to evaluate the entire set of digitized data of V(t) electrocardiograms for the indicated conditions of the cardiovascular system. The results of the corresponding analysis for the indicated functional states of the cardiovascular system at a sampling frequency of ECG signal fd=500 Hs are shown in Table 3. We will perform a comparative analysis of informative parameters for the two conditions of the cardiovascular system: normal (Table 4) and ventricular tachycardias5fd=5frequency and H2d=0frequency (T2d=5frequency) 0 Hs.
The use of neural network technology in flicker-noise spectroscopy of an electrocardiogram
At the same time, the signal analysis in flicker noise spectroscopy reveals the dynamics of changes in parameters H1 and T1 at small τ, as well as parameters T0 and n0, characteristic of dependence S(f) in the high-frequency region. Thus, when analyzing a complex chaotic signal during flicker-noise spectroscopy, a series of parameters characterizing the correlation relationships in the sequences of irregularity jumps and irregularity bursts characteristic of this signal are determined at a sampling frequency offd. Thus, when analyzing a complex chaotic signal, which is an ECG signal, we consider a set of six parameters that characterize the correlation relationships in the series of irregularities - "jumps" and irregularities - "bursts" inherent in this signal.
The choice of artificial neural network and its characteristics
A modular version of the construction of a neural network for the recognition of pathologies (the number of input parameters, the number of neurons in the intermediate layer, the number of analyzed pathologies). Parameterization of the common component of the ECG signal for diagnosis of the critical conditions of the cardiovascular system. In this section, we will provide an overview of the embedded system structure and applications.
Biomedical application of embedded systems
- Embedded systems hardware
- Embedded systems software
- Medical device design
- Embedded systems restrictions
Small devices with embedded systems such as Raspberry pi, Ardunio can collect patient data and provide data processing with overwritten software, with the reduction and increase of processing power. Applications such as image processing, web server, user interface, operating system are now also written for embedded systems. In addition, hardware vulnerabilities such as Meltdown and Spectre, caused by a hardware shortage in processors, also pose a problem for embedded system solutions.
Conclusions
The issues that cause problems in the embedded system design process are listed as follows; Debugging tools, schedule, engineering team skill level, firmware itself, microprocessor, programming tools, interfaces, other hardware. Apart from these, it is not difficult to predict that new designs will be made according to the needs that have not yet come to mind. Embedded system applications where real-time solutions, ANN, CNN, Machine Learning, Deep Learning, Federated Learning, NLP applications and more are used together or separately will increase diversity.
