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Acting like a startup - using corporate startup structures to manage the digital transformation
(2023)
Digital transformation is proving to be a significant challenge for firms and companies when it comes to maintaining their market position. It is evident that many companies are struggling to find their particular way through this transformation. A corporate startup structure is one way to find a suitable solution quickly. Therefore, we are presenting a model for corporate startup activities, which we will instantiate in an appropriate tool to support the management of corporate startups by their parent firms. We have derived the first requirements and design principles from a comprehensive problem analysis and literature study. In addition to this,we are presenting a first artifact, which should realize the design principles by implementing a practical tool. Forming a cooperation with an automotive firm has enabled us to gain access to real-world data for the design and evaluation of the artifact.
Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.
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.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
Healthy sleep is one of the prerequisites for a good human body and brain condition, including general well-being. Unfortunately, there are several sleep disorders that can negatively affect this. One of the most common is sleep apnoea, in which breathing is impaired. Studies have shown that this disorder often remains undiagnosed. To avoid this, developing a system that can be widely used in a home environment to detect apnoea and monitor the changes once therapy has been initiated is essential. The conceptualisation of such a system is the main aim of this research. After a thorough analysis of the available literature and state of the art in this area of knowledge, a concept of the system was created, which includes the following main components: data acquisition (including two parts), storage of the data, apnoea detection algorithm, user and device management, data visualisation. The modules are interchangeable, and interfaces have been defined for data transfer, most of which operate using the MQTT protocol. System diagrams and detailed component descriptions, including signal requirements and visualisation mockups, have also been developed. The system's design includes the necessary concepts for the implementation and can be realised in a prototype in the next phase.
This document presents an algorithm for a nonobtrusive 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.
Comparison of sleep characteristics measurements: a case study with a population aged 65 and above
(2020)
Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.
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.
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
Fragestellung: Das klinische Standardverfahren und Referenz der Schlafmessung und der Klassifizierung der einzelnen Schlafstadien ist die Polysomnographie (PSG). Alternative Ansätze zu diesem aufwändigen Verfahren könnten einige Vorteile bieten, wenn die Messungen auf eine komfortablere Weise durchgeführt werden. Das Hauptziel dieser Forschung Studie ist es, einen Algorithmus für die automatische Klassifizierung von Schlafstadien zu entwickeln, der ausschließlich Bewegungs- und Atmungssignale verwendet [1].
Patienten und Methoden: Nach der Analyse der aktuellen Forschungsarbeiten haben wir multinomiale logistische Regression als Grundlage für den Ansatz gewählt [2]. Um die Genauigkeit der Auswertung zu erhöhen, wurden vier Features entwickelt, die aus Bewegungs- und Atemsignalen abgeleitet wurden. Für die Auswertung wurden die nächtlichen Aufzeichnungen von 35 Personen verwendet, die von der Charité-Universitätsmedizin Berlin zur Verfügung gestellt wurden. Das Durchschnittsalter der Teilnehmer betrug 38,6 +/– 14,5 Jahre und der BMI lag bei durchschnittlich 24,4 +/– 4,9 kg/m2. Da der Algorithmus mit drei Stadien arbeitet, wurden die Stadien N1, N2 und N3 zum NREM-Stadium zusammengeführt. Der verfügbare Datensatz wurde strikt aufgeteilt: in einen Trainingsdatensatz von etwa 100 h und in einen Testdatensatz mit etwa 160 h nächtlicher Aufzeichnungen. Beide Datensätze wiesen ein ähnliches Verhältnis zwischen Männern und Frauen auf, und der durchschnittliche BMI wies keine signifikante Abweichung auf.
Ergebnisse: Der Algorithmus wurde implementiert und lieferte erfolgreiche Ergebnisse: die Genauigkeit der Erkennung von Wach-/NREM-/REM-Phasen liegt bei 73 %, mit einem Cohen’s Kappa von 0,44 für die analysierten 19.324 Schlafepochen von jeweils 30 s. Die beobachtete gewisse Überschätzung der NREM-Phase lässt sich teilweise durch ihre Prävalenz in einem typischen Schlafmuster erklären. Selbst die Verwendung eines ausbalancierten Trainingsdatensatzes konnte dieses Problem nicht vollständig lösen.
Schlussfolgerungen: Die erreichten Ergebnisse haben die Tauglichkeit des Ansatzes prinzipiell bestätigt. Dieser hat den Vorteil, dass nur Bewegungs- und Atemsignale verwendet werden, die mit weniger Aufwand und komfortabler für Benutzer aufgezeichnet werden können als z. B. Herz- oder EEG-Signale. Daher stellt das neue System eine deutliche Verbesserung im Vergleich zu bestehenden Ansätzen dar. Die Zusammenführung der beschriebenen algorithmischen Software mit dem in [1] beschriebenen Hardwaresystem zur Messung von Atem- und Körperbewegungssignalen zu einem autonomen, berührungslosen System zur kontinuierlichen Schlafüberwachung ist eine mögliche Richtung zukünftiger Arbeiten.