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Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.
We identify 74 generic, reusable technical requirements based on the GDPR that can be applied to software products which process personal data. The requirements can be traced to corresponding articles and recitals of the GDPR and fulfill the key principles of lawfulness and transparency. Therefore, we present an approach to requirements engineering with regard to developing legally compliant software that satisfies the principles of privacy by design, privacy by default as well as security by design.
The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%.
The overall goal of this work is to detect and analyze a person's movement, breathing and heart rate during sleep in a common bed overnight without any additional physical contact. The measurement is performed with the help of
sensors placed between the mattress and the frame. A two-stage pattern classification algorithm based has been implemented that applies statistics analysis to recognize the position of patients. The system is implemented in a sensors-network, hosting several nodes and communication end-points to support quick and efficient classification. The overall tests show convincing results for the position recognition and a reasonable overlap in matching.
Die Erholung unseres Körpers und Gehirns von Müdigkeit ist direkt abhängig von der Qualität des Schlafes, die aus den Ergebnissen einer Schlafstudie ermittelt werden kann. Die Klassifizierung der Schlafstadien ist der erste Schritt dieser Studie und beinhaltet die Messung von Biovitaldaten und deren weitere Verarbeitung. Das non-invasive Schlafanalyse-System basiert auf einem Hardware-Sensornetz aus 24 Drucksensoren, das die Schlafphasenerkennung ermöglicht. Die Drucksensoren sind mit einem energieeffizienten Mikrocontroller über einen systemweiten Bus mit Adressarbitrierung verbunden. 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 fühlbaren Einfluss auf das gewohnte Bett. Das System wurde getestet, indem Experimente durchgeführt wurden, die den Schlaf verschiedener gesunder junger Personen aufzeichneten. Die ersten Ergebnisse weisen auf das Potenzial hin, nicht nur Atemfrequenz und Körperbewegung, sondern auch Herzfrequenz zu erfassen.
Die digitale Transformation von Geschäftsprozessen und die stärkere Einbindung von IT-Systemen erzeugen bei kleinen und mittelständischen Unternehmen (KMU) Chancen und Risiken zugleich. Risiken, die insbesondere in einer fehlenden IT-Compliance resultieren können. Wie Studien zeigen, sind KMU in Bezug auf IT-Compliance-Maßnahmen im Vergleich zu kapitalmarktorientierten Unternehmen jedoch im Rückstand [1]. Im Beitrag wird mithilfe von Experteninterviews und einer qualitativen Datenanalyse der Frage nachgegangen, welcher Status quo an Maßnahmen aktuell implementiert und wie der empfundene Compliance-Reifegrad ist. Weiterhin werden die Gründe und Motive erörtert, die zu diesem Zustand geführt haben. Letztlich sind Treiber identifiziert worden, die zu einem höheren Bewusstsein in der Zukunft führen können. Die Arbeit zeigt interessante Erkenntnisse aus der Praxis, da die Experteninterviews Einblicke in den aktuellen Status quo in Bezug auf IT-Compliance liefern.
The goal of the presented project is to develop the concept of home ehealth centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant diseases for a detailed study within this project because of their widespread influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.
Background:
One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective:
This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods:
The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results:
Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions:
These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Objective: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for long-term monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.