Refine
Year of publication
Document Type
- Conference Proceeding (101)
- Article (21)
- Other Publications (3)
- Part of a Book (2)
Has Fulltext
- no (127) (remove)
Keywords
- AAL (3)
- AHI (1)
- Accelerometer (2)
- Accelerometer sensor (2)
- Accelerometers (2)
- Accessibility (1)
- Activity monitoring (1)
- Algorithm (1)
- Ambient assisted living (2)
- Apnea detection (1)
Institute
Present demographic change and a growing population of elderly people leads to new medical needs. Meeting these with state of the art technology is as a consequence a rapidly growing market. So this work is aimed at taking modern concepts of mobile and sensor technology and putting them in a medical context. By measuring a user’s vital signs on sensors which are processed on a Android smartphone, the target system is able to determine the current health state of the user and to visualize gathered information. The system also includes a weather forecasting functionality, which alerts the user on possibly dangerous future meteorological events. All information are collected centrally and distributed to users based on their location. Further, the system can correlate the client-side measurement of vital signs with a server-side weather history. This enables personalized forecasting for each user individually. Finally, a portable and affordable application was developed that continuously monitors the health status by many vital sensors, all united on a common smartphone.
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.
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.
A significant proportion of road traffic accidents are due to inattentiveness or fatigue at the wheel. Approaches to monitoring the driver's condition range from eye tracking and driving behavior analysis to yawn and blink detection and ECG measurement. This work describes the development of a mobile system for the measurement and processing of ECG data. The aim of the signal processing is to quantify the driver’s fatigue with the heartrate variability (HRV). The work includes the hardware and software design of the sensor. First, the development of low-noise electronics including AD conversion is described. Then the software signal processing with QRS complex detection and plotting front end is explained. The resulting sensor is compact, low-cost and provides a good signal for HRV extraction.
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.
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.
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.
Sleep quality and in general, behavior in bed can be detected using a sleep state analysis. These results can help a subject to regulate sleep and recognize different sleeping disorders. In this work, a sensor grid for pressure and movement detection supporting sleep phase analysis is proposed. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this project is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides this fact, they are also very expensive. The system represented in this work classifies respiration and body movement with only one type of sensor and also in a non-invasive 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 the potential for classification of breathing rate and body movements. Although previous researches show the use of pressure sensors in recognizing posture and breathing, they have been mostly used by positioning the sensors between the mattress and bedsheet. This project however, shows an innovative way to position the sensors under the mattress.
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.
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.
Das häusliche Umfeld kann vor allem für langfristiges Schlafmonitoring verwendet werden. Gute Patientenakzeptanz erfordert niedrige Nutzer- und Installationsbarrieren. Für die Installation zu Hause sind klassische PSG-Systeme aufgrund von ihrer Komplexität wenig passend. Ziel der Entwicklung ist die qualifizierte Erhebung von Parametern, die einerseits eine hinreichend gute Klassifikation von Schlafphasen erlauben und die andererseits durch nicht-invasive Methoden erfasst werden können.
Basierend auf einer Literaturstudie und der Maßgabe nicht-invasive Methoden zu nutzen, wurden folgende Parameter ausgewählt: Körperbewegung, Atmung und Herzschlag. Diese Parameter können nicht-invasiv durch Matratzendrucksensoren erfasst werden. Die Sensorknoten sind als ein Netz von Drucksensoren implementiert, die mit einem leistungsarmen und performanten Mikrocontroller verbunden sind. Alle Knoten sind über einen systemweiten Bus mit Adressarbitrierung verbunden. Der eingebettete Prozessor ist der Mesh-Netzwerk-Endpunkt, der die Netzwerkkonfiguration, Speicherung und Vorverarbeitung der Daten, externen Datenzugriff und Visualisierung ermöglicht.
Das System wurde getestet, indem Experimente durchgeführt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die erhaltenen Ergebnisse bestätigen die Fähigkeit des Systems, Atemfrequenz und Körperbewegung zu erfassen. Ein wesentlicher Unterschied dieses Systems im Vergleich zu anderen Ansätzen ist die innovative Art, die Sensoren unter der Matratze zu platzieren. Diese Eigenschaft erleichtert die kontinuierliche Nutzung des Systems ohne Einfluss auf den gemeinsamen Schlafprozess.
Um Schlafverhalten langfristig zu untersuchen, wird ein Hardwaresystem mit niedrigen Installationsbarrieren für den Einsatz im häuslichen Umfeld. Erste Ergebnisse weisen auf das Potenzial hin, außer Körperbewegung und Atemfrequenz, auch Herzfrequenz erfassen zu können. Die Werte können weiter verbessert werden, wenn die Sensorabfragefrequenz erhöht wird. Nach der Weiterentwicklung des Systems, soll es mit dem Softwarealgorithmus für die Schlafphasenerkennung verbunden werden.