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These days computer analysis of ECG (Electrocardiograms) signals is common. There are many real-time QRS recognition algorithms; one of these algorithms is Pan-Tompkins Algorithm. Which the Pan-Tompkins Algorithm can detect QRS complexes of ECG signals. The proposed algorithm is analysed the data stream of the heartbeat based on the digital analysis of the amplitude, the bandwidth, and the slope. In addition to that, the stress algorithm compares whether the current heartbeat is similar or different to the last heartbeat after detecting the ECG signals. This algorithm determines the stress detection for the patient on the real-time. In order to implement the new algorithm with higher performance, the parallel programming language CUDA is used. The algorithm determines stress at the same time by determining the RR interval. The algorithm uses a different function as beat detector and a beat classifier of stress.
In modern fruit processing technology, non-destructive quality measuring techniques aresought for determining and controlling changes in the optical, structural, and chemical properties of theproducts. In this context, changes inside the product can be measured during processing. Especiallyfor industrial use, fast, precise, but robust methods are particularly important to obtain high-qualityproducts. In this work, a newly developed multi-spectral imaging system was implemented andadapted for drying processes. Further it was investigated if the system could be used to link changesin the surface spectral reflectance during mango drying with changes in moisture content andcontents of chemical components. This was achieved by recovering the spectral reflectance frommulti-spectral image data and comparing the spectral changes with changes of the total soluble solids(TSS), pH-value and the relative moisture contentxwbof the products. In a first step, the camera wasmodified to be used in drying, then the changes in the spectra and quality criteria during mangodrying were measured. For this, mango slices were dried at air temperatures of 40–80◦C and relativeair humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectralimaging system. The quality criteria were then predicted from spectral data. It could be shown thatthe newly developed multi-spectral imaging system can be used for quality control in fruit drying.There are strong indications as well, that it can be employed for the prediction of chemical qualitycriteria of mangoes during drying. This way, quality changes can be monitored inline during theprocess using only one single measuring device.
Stress is recognized as a factor of predominant disease and in the future the costs for treatment will increase. The presented approach tries to detect stress in a very basic and easy to implement way, so that the cost for the device and effort to wear it remain low. The user should benefit from the fact that the system offers an easy interface reporting the status of his body in real time. In parallel, the system provides interfaces to pass the obtained data forward for further processing and (professional) analyses, in case the user agrees. The system is designed to be used in every day’s activities and it is not restricted to laboratory use or environments. The implementation of the enhanced prototype shows that the detection of stress and the reporting can be managed using correlation plots and automatic pattern recognition even on a very light-weighted microcontroller platform.
To evaluate the quality of sleep, it is important to determine how much time was spent in each sleep stage during the night. The gold standard in this domain is an overnight polysomnography (PSG). But the recording of the necessary electrophysiological signals is extensive and complex and the environment of the sleep laboratory, which is unfamiliar to the patient, might lead to distorted results. In this paper, a sleep stage detection algorithm is proposed that uses only the heart rate signal, derived from electrocardiogram (ECG), as a discriminator. This would make it possible for sleep analysis to be performed at home, saving a lot of effort and money. From the heart rate, using the fast Fourier transformation (FFT), three parameters were calculated in order to distinguish between the different sleep stages. ECG data along with a hypnogram scored by professionals was used from Physionet database, making it easy to compare the results. With an agreement rate of 41.3%, this approach is a good foundation for future research.
Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Sleep is an important part of our life that significantly influences our health and well-being. The monitoring of sleep can provide data based on which sleep quality could be improved. This paper presents a system for heart rate detection during sleep. The data is collected from sensors underneath the test subjects. Though the data contains noise, it needs to be filtered to remove it. Due to the low strength of the signals, they need to be amplified after filtering. At some points of the signal, particular heartbeats may not be tracked by sensors due to the failure of a sensor or other reasons, which should be considered. The heart rate is detected in intervals of 15 s. A tool is implemented that detects the heart rate and visualizes it. The preprocessing of the data is performed with several filters: a highpass filter, a band-reject filter, a lowpass filter, and a motion detector. After the preprocessing of the data, the quality of the signal is significantly increased, and detection is possible.
In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist-rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection). To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run-to-failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear-out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.
With the high resolution of modern sensors such as multilayer LiDARs, estimating the 3D shape in an extended object tracking procedure is possible. In recent years, 3D shapes have been estimated in spherical coordinates using Gaussian processes, spherical double Fourier series or spherical harmonics. However, observations have shown that in many scenarios only a few measurements are obtained from top or bottom surfaces, leading to error-prone estimates in spherical coordinates. Therefore, in this paper we propose to estimate the shape in cylindrical coordinates instead, applying harmonic functions. Specifically, we derive an expansion for 3D shapes in cylindrical coordinates by solving a boundary value problem for the Laplace equation. This shape representation is then integrated in a plain greedy association model and compared to shape estimation procedures in spherical coordinates. Since the shape representation is only integrated in a basic estimator, the results are preliminary and a detailed discussion for future work is presented at the end of the paper.