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Ballistocardiography (BCG) can be used to monitor heart rate activity. Besides, the accelerometer should have high sensitivity and minimal internal noise; a low-cost approach was taken into consideration. Several measurements have been executed to determine the optimal positioning of a sensor under the mattress to obtain a signal strong enough for further analysis. A prototype for an unobtrusive accelerometer-based measurement system has been developed and tested in a conventional bed without any specific extras. The influence of the human sleep position for the output accelerometer data was tested. The obtained results indicate the potential to capture BCG signals using accelerometers. The measurement system can detect heart rate in an unobtrusive form in the home environment.
Due to the rising need for palliative care in Russia, it is crucial to provide timely and high-quality solutions for patients, relatives, and caregivers. A methodology for remote monitoring of patients in need of palliative care and the requirements will be developed for a hardware-software complex for remote monitoring of patients' health at home.
The digital twin concept has been widely known for asset monitoring in the industry for a long time. A clear example is the automotive industry. Recently, there has also been significant interest in the application of digital twins in healthcare, especially in genomics in what is known as precision medicine. This work focuses on another medical speciality where digital twins can be applied, sleep medicine. However, there is still great controversy about the fundamentals that constitute digital twins, such as what this concept is based on and how it can be included in healthcare effectively and sustainably. This article reviews digital twins and their role so far in what is known as personalized medicine. In addition, a series of steps will be exposed for a possible implementation of a digital twin for a patient suffering from sleep disorders. For this, artificial intelligence techniques, clinical data management, and possible solutions for explaining the results derived from artificial intelligence models will be addressed.
Autism spectrum disorders (ASD) affect a large number of children both in the Russian Federation and in Germany. Early diagnosis is key for these children, because the sooner parents notice such disorders in a child and the rehabilitation and treatment program starts, the higher the likelihood of his social adaptation. The difficulties in raising such a child lie in the complexity of his learning outside of children's groups and the complexity of his medical care. In this regard, the development of digital applications that facilitate medical care and education of such children at home is important and relevant. The purpose of the project is to improve the availability and quality of healthcare and social adaptation at home of children with ASD through the use of digital technologies.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.
Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
To evaluate the quality of a person's sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.
The respiratory rate is a vital sign indicating breathing illness. It is necessary to analyze the mechanical oscillations of the patient's body arising from chest movements. An inappropriate holder on which the sensor is mounted, or an inappropriate sensor position is some of the external factors which should be minimized during signal registration. This paper considers using a non-invasive device placed under the bed mattress and evaluates the respiratory rate. The aim of the work is the development of an accelerometer sensor holder for this system. The normal and deep breathing signals were analyzed, corresponding to the relaxed state and when taking deep breaths. The evaluation criterion for the holder's model is its influence on the patient's respiratory signal amplitude for each state. As a result, we offer a non-invasive system of respiratory rate detection, including the mechanical component providing the most accurate values of mentioned respiratory rate.
This paper presents a bed system able to analyze a person’s movement, breathing and recognize the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the bed-frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors. First test results have indicated the potential of the proposed approach for the recognition of sleep positions with 83% of correct recognized positions.
The process of restoring our body and brain from fatigue is directly depend-ing on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing.
In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In comparison to the leading standard, which is polysomnography, the proposed approach is a non-invasive system. It recognises respiration and body move-ment with only one type of low-cost pressure sensors forming a mesh archi-tecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization.
The system was tested by executing experiments recording the sleep of different healthy young subjects. The results obtained have indicated the po-tential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process.
Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.
We consider the problem of increasing the informative value of electrocardiographic (ECG) surveys using data from multichannel electrocardiographic leads, that include both recorded electrocardiosignals and the coordinates of the electrodes placed on the surface of the human torso. In this area, we were interested in reconstruction of the surface distribution of the equivalent sources during the cardiac cycle at relatively low hardware cost. In our work, we propose to reconstruct the equivalent electrical sources by numerical methods, based on integral connection between the density of electrical sources and potential in a conductive medium. We consider maps of distributions of equivalent electric sources on the heart surface (HSSM), presenting source distributions in the form of a simple or double electrical layer. We indicate the dynamics of the heart electrical activity by the space-time mapping of equivalent electrical sources in HSSM.
The recovery of our body and brain from fatigue directly depends on the quality of sleep, which can be determined from the results of a sleep study. The classification of sleep stages is the first step of this study and includes the measurement of vital data and their further processing. The non-invasive sleep analysis system is based on a hardware sensor network of 24 pressure sensors providing sleep phase detection. The pressure sensors are connected to an energy-efficient microcontroller via a system-wide bus. A significant difference between this system and other approaches is the innovative way in which the sensors are placed under the mattress. This feature facilitates the continuous use of the system without any noticeable influence on the sleeping person. The system was tested by conducting experiments that recorded the sleep of various healthy young people. Results indicate the potential to capture respiratory rate and body movement.
This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.
With the advancement in sensor technology and the trend shift of health measurement from treatment after diagnosis to abnormalities detection long before the occurrence, the approach of turning private spaces into diagnostic spaces has gained much attention. In this work, we designed and implemented a low-cost and compact form factor module that can be deployed on the steering wheel of cars as well as most frequently touch objects at home in order to measure physiological signals from the fingertip of the subject as well as environmental parameters. We estimated the heart rate and SpO2 with the error of 2.83 bpm and 3.52%, respectively. The signal evaluation of skin temperature shows a promising output with respect to environmental recalibration. In addition, the electrodermal activity sensor followed the reference signal, appropriately which indicates the potential for further development and application in stress measurement.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
Assistive environments are entering our homes faster than ever. However, there are still various barriers to be broken. One of the crucial points is a personalization of offered services and integration of assistive technologies in common objects and therefore in a regular daily routine. Recognition of sleep patterns for the preliminary sleep study is one of the health services that could be performed in an undisturbing way. This article proposes the hardware system for the measurement of bio-vital signals necessary for initial sleep study in a non-obtrusive way. The first results confirm the potential of measurement of breathing and movement signals with the proposed system.
Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.
Stress and physical activities are important aspects of life of people. Body reactions on stress and on physical activities can be very similar but long-term stress leads to diseases and damages the body. Currently there is no method to differentiate easily and clearly between these two aspects in a time slot. We have confronted this problem while developing a mobile system for detection and analysis of stress. This paper presents an approach, which uses a long-term monitor with ECG/EKG capabilities and analysis of the heart rate data that is extracted from the device. The focus of the work is to find characteristics that are useful for differentiation between physical activity and stress.
To assess the quality of a person’s sleep, it is essential to examine the sleep behaviour by identifying the several sleep stages, their durations and sleep cycles. The established and gold standard procedure for sleep stage scoring is overnight polysomnography (PSG) with the Rechtschaffen and Kales (R-K) method. Unfortunately, the conduct of PSG is timeconsuming and unfamiliar for the subjects and might have an impact of the recorded data. To avoid the disadvantages with PSG, it is important to make further investigations in low-cost home diagnostic systems. For this intention it is necessary to find suitable bio vital parameters for classifying sleep stages without any physical impairments at the same time.
Due to the promising results in several publications we want to analyse existing methods for sleep stage classification based on the parameters body movement,
heartbeat and respiration. Our aim was to find different behaviour patterns in the several sleep stages. Therefore, the average values of 15 wholenight PSG recordings -obtained from the ‘DREAMS Subjects Database’- where analysed in the light of heartbeat, body movement and respiration with 10 different methods.
Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm.
Sleep study can be used for detection of sleep quality and in general bed behaviors. These results can helpful for regulating sleep and recognizing different sleeping disorders of human. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this work is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides, these methods not only decrease practicality due to the process of having to put them on, but they are also very expensive. The system proposed in this paper classifies respiration and body movement with only one type of sensor and also in a noninvasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed excellent results in the classification of breathing rate and body movements.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
Sleep analysis using a Polysomnography system is difficult and expensive. That is why we suggest a non-invasive and unobtrusive measurement. Very few people want the cables or devices attached to their bodies during sleep. The proposed approach is to implement a monitoring system, so the subject is not bothered. As a result, the idea is a non-invasive monitoring system based on detecting pressure distribution. This system should be able to measure the pressure differences that occur during a single heartbeat and during breathing through the mattress. The system consists of two blocks signal acquisition and signal processing. This whole technology should be economical to be affordable enough for every user. As a result, preprocessed data is obtained for further detailed analysis using different filters for heartbeat and respiration detection. In the initial stage of filtration, Butterworth filters are used.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.
The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
Die Schlafapnoe ist eine häufig auftretende Schlafstörung,
die unterschiedliche Auswirkungen auf unseren Alltag hat; so wurde z. B.
über eine Tagesschläfrigkeit von etwa 25 % der Patienten mit obstruktiver
Schlafapnoe (OSA) berichtet. Ziel dieser Arbeit ist die Entwicklung eines
Systems, das eine nichtinvasive Erkennung der Schlafapnoe in häuslicher
Umgebung ermöglichen soll.
In recent decades, it can be observed that a steady increase in the volume of tourism is a stable trend. To offer travel opportunities to all groups, it is also necessary to prepare offers for people in need of long-term care or people with disabilities. One of the ways to improve accessibility could be digital technologies, which could help in planning as well as in carrying out trips. In the work presented, a study of barriers was first conducted, which led to selecting technologies for a test setup after analysis. The main focus was on a mobile app with travel information and 360° tours. The evaluation results showed that both technologies could increase accessibility, but some essential aspects (such as usability, completeness, relevance, etc.) need to be considered when implementing them.
Sleep is a multi-dimensional influencing factor on physical health, cognitive function, emotional well-being, mental health, daily performance, and productivity. The barriers such as time-consuming, invasiveness, and expense have caused a gradual shift in sleep monitoring from traditional and standard in-lab approach, e. g., polysomnography (PSG) to unobtrusive and noninvasive in-home sleep monitoring, yet further improvement is required. Despite an increasing interest in fiberoptic-based methods for cardiorespiratory estimation, the traditional mechanical-based sensors consist of force-sensitive resistors (FSR), lead zirconate titanate piezoelectric (PZT), and accelerometers yet serve as the dominant approach. The part of popularity lies in reducing the system’s complexity, expense, easy maintenance, and user-friendliness. However, care must be taken regarding the performance of such sensors with respect to accuracy and calibration.
Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.
The exposure to the light has a great influence on human beings in their everyday life. Various lighting sources produce light that reaches the human eye and influences a rhythmic release of melatonin hormone, that is a sleep promoting factor.
Since the development of new technologies provides more control over illuminance, this work uses an IoT based lighting system to set up dim and bright scenarios. A small study has been performed on the influence of illuminance on sleep latency. The system consists of different light bulbs, sensors and a central bridge which are interconnected like a mesh network. Also, a mobile app has been developed, that allows to adjust the lighting in various rooms. With the help of a ferro-electret sensor, like applied in sleep monitoring systems, a subject’s sleep was monitored. The sensor is placed below the mattress and it collects data, which is stored and processed in a cloud or in other alternative locations.
The research was conducted on healthy young subjects after being previously exposed to the preconfigured illuminance for at least three hours before bedtime. The results indicate correlation between sleep onset latency and exposure to different illuminance before bedtime. In a dimmed environment, the subject fell asleep in average 28% faster compared to the brighter environment.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less errorprone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
Autismus-Spektrum-Störungen (ASD) bei Kindern werden häufig zu spät diagnostiziert und die Begleitung der chronischen Krankheit gestaltet sich schwierig. Der vorgestellte Ansatz erlaubt die Behandlung der Kinder in dem bekannten häuslichen Umfeld und versucht die Beziehungen zwischen Schlaf und Verhalten herauszuarbeiten. Die gewonnenen Erkenntnisse sollen die Lebensqualität der Patienten verbessern und den Eltern Hilfestellung geben. Die notwendige infrastrukturelle Unterstützung wird durch medizinisches Fachpersonal geleistet, das auf einen web-basierten Service zurückgreifen kann, der sämtliche Prozesse (Diagnostik, Datenerfassung, -aufzeichnung und Training etc.) begleitet. Die anonymisierten Daten werden in einem Diagnosesystem zentral abgelegt und können so für zukünftige Behandlungsstrategien nutzbar sein. Die umfassende Lösung setzt auf zentrale Elemente von Smart-Homes und AAL auf.
The development of home health systems can provide continuous and user-friendly monitoring of key health parameters. This project aims to create a concept for such a system, implement it on a test basis, and evaluate it. Three health areas were selected for this purpose:
Sleep, Stress, and Rehabilitation. Appropriate devices were installed in the homes of test subjects and used by them for two weeks. Besides, relevant questionnaires were completed to obtain a complete picture. Finally, the implemented system was evaluated, and the results of the conducted study showed that home health systems have great potential. However, it is necessary to consider some points to increase the usability of the system and the motivation of the users. Among others, ease of use of the equipment is of extreme importance.
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.
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.
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.
Generating synthetic data is a relevant point in the machine learning community. As accessible data is limited, the generation of synthetic data is a significant point in protecting patients' privacy and having more possibilities to train a model for classification or other machine learning tasks. In this work, some generative adversarial networks (GAN) variants are discussed, and an overview is given of how generative adversarial networks can be used for data generation in different fields. In addition, some common problems of the GANs and possibilities to avoid them are shown. Different evaluation methods of the generated data are also described.