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Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs.
Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system
(2016)
An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency of safety-relevant driving rules.
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.
Introduction
Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed.
Objective
This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection.
Methods
Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions.
Results
The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods.
Conclusions
The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
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.
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.
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.
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.
Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.