Adequate and restorative sleep is essential for maintaining physiological health, cognitive function and overall well-being. However, sleep disorders - particularly obstructive sleep apnoea (OSA) - can significantly impair sleep quality and contribute to the development of other complications. Despite its high prevalence, OSA often remains undiagnosed, largely due to the limited accessibility and convenience of current diagnostic procedures. To address this challenge, the present study with 20 participants focuses on the development and preliminary evaluation of a patient-centred system designed for automated detection of sleep apnoea and long-term monitoring of therapeutic interventions. The system integrates electrical impedance pneumography and photoplethysmography to record key physiological parameters including respiratory effort, heart rate and peripheral oxygen saturation (SpO₂). System modules were implemented for signal processing, data analysis and wireless transmission. An initial evaluation of the prototype was conducted to assess signal quality. While the results indicate several areas for optimisation - particularly in terms of hardware stability and reliability of data transmission - the overall concept shows considerable potential for future application in clinical and home environments.

Scoping reviews represent a powerful methodological tool for synthesizing knowledge across broad and interdisciplinary domains, particularly where technological advancements intersect with medical practice. Unlike traditional systematic reviews that rely on narrowly defined research questions and stringent inclusion criteria, scoping reviews allow for a wider scope of inquiry and the inclusion of diverse evidence types, including grey literature, technical reports, and early-stage research. This flexibility is particularly advantageous when assessing the impact of emerging technologies on medical protocols and interventions, where the pace of innovation often outstrips the availability of standardized clinical data. This paper explores the methodological advantages of scoping reviews in the context of investigating technological implementations in healthcare. As a practical illustration, we present a case study on the potential of artificial intelligence (AI) technologies in the treatment of insomnia. We outline the process of developing a scoping review protocol tailored to this topic and discuss how it supports a comprehensive understanding of the field, enables identification of knowledge gaps, and informs future research directions. The findings underscore the value of scoping reviews in mapping complex, evolving research landscapes and in facilitating interdisciplinary insights that may be missed by more narrowly focused review methodologies.

Driving safety is an important matter that, if violated, can have serious consequences such as injury or even death. Several studies have shown that fatigue, and the drowsiness that often results from it, has a significant impact on road safety. It is therefore essential to detect the symptoms of fatigue at an early stage so that appropriate countermeasures can be taken before a dangerous situation arises. Various systems can be used to detect fatigue, including those that monitor physiological signals and look for specific patterns to detect changes and trigger appropriate action if necessary. Mobile systems, which offer greater flexibility, are the focus of some of these efforts. In order to facilitate a methodological approach in the development of new systems, it is imperative to obtain a systematic overview within the initial phase. This enables the identification of both established and promising approaches, as well as the identification of actual gaps that necessitate further research. The aim of this article is to set out a protocol for the conduct of a systematic review in order to be able to subsequently carry it out. This includes researching and defining features such as eligibility criteria, selecting appropriate databases, search strategies and, most importantly, defining the specific research question. The result of this work is a clear and methodologically prepared summary of the most important points to be considered when conducting the systematic review on the subject of ”Mobile Physiological Monitoring Systems for Driver Fatigue Detection”.

The mental health of children and adolescents is susceptible to being adversely influenced by external stressors and events. A particularly notable example is the COVID-19 pandemic, which has profoundly disrupted the lives of billions of children and families and has been associated with an escalation in mental health issues, including depression, anxiety, and stress-related difficulties. As cited by the World Health Organization, there has been a documented 25% increase in the incidence of anxiety and depression. Before the pandemic, German data indicated a considerable prevalence of anxiety (15%) and depressive symptoms (10%), with similar trends observed worldwide. Impulsivity, in addition, is linked to various behavioral problems in childhood and adolescence, potentially resulting in challenges regarding emotional regulation as individuals advance in age. This protocol seeks to examine the interrelations between stress, sleep, and impulsivity in children and adolescents. Hence, herein, we present a methodological protocol designed to systematically collect data across these domains. The presented method uses mixed longitudinal methods to investigate the relationship between sleep, stress, and impulsivity in children, combining questionnaires, specialist evaluations, and data obtained from mobile sensors and speech analysis aimed at detecting impulsivity-related discourse patterns. The data obtained will be used to develop a cost-effective approach involving automatic and structured speech analysis, sleep pattern assessment and stress indicators captured by mobile devices. This will aid in the diagnosis of Impulse Control Disorders (ICD) in children and adolescents and provide valuable information for the diagnosis and implementation of the intervention program. Combining psychological expertise with technological innovation, the project will contribute to fundamental research and the development of digital tools to support young people’s mental health.

CuTouch is an innovative somatosensory training system aimed at improving diminished discriminative cutaneous sensitivity in the fingertips. The system employs a multimodal approach with visual, audio, and tactile cues, integrating neuroplasticity and spasticity therapy techniques to enhance attention and subsequently sensory perception. The system consists of two core components: the App and the Station. The mobile application facilitates cutaneous stimulation sessions, conducts user testing, and monitors progress. It activates specific points and patterns, simulates textures, and synchronizes vibrotactile feedback with music note onsets and frequencies. The app’s discrimination testing module is inspired by established two-point discrimination and tactile discrimination tests, enabling standardized, remote assessments with consistent pressure. The Station, a specialized hardware device, delivers precise vibrotactile stimulation, offering a comprehensive at-home training solution. Future research will explore expanding the system’s capabilities and applications to confirm its effectiveness and broader impact further.

Touch, as one of the five primary senses, provides critical somatosensory information, including pressure, vibration, temperature, pain, and skin stress. Somatosensory deficits, often resulting from stroke or aging, significantly impair fingertip sensitivity, affecting daily function and quality of life. Vibrotactile stimulation devices have emerged as a modern therapeutic approach to address these deficits. A well-established bidirectional relationship exists between somatosensory function and sleep, where somatosensory stimulation aids sleep regulation, and sleep enhances somatosensory recovery. However, the potential of somatosensory therapy in ambulatory settings remains largely unexplored. While MRI and EEG have been used to measure the effects of somatosensory stimulation, current therapeutic evaluations still rely primarily on patient feedback, highlighting the need for objective assessment methods. This research initiates an in-depth literature review on the interplay between somatosensory therapy and sleep, alongside the application of EEG for therapy evaluation in home environments. The study aims to guide data selection strategies by sourcing information from patients, therapy sessions, sleep monitoring, and EEG recordings. A system architecture will be designed to integrate somatosensory therapy with sleep monitoring and EEG, addressing hardware requirements, communication protocols, and information architecture. Furthermore, deep learning models will be developed to analyze the interaction between sleep and somatosensory therapy, enabling personalized therapy adaptations.

Advances in wearable technology have significantly enhanced the ability to monitor sleep in naturalistic, real-world environments, providing valuable insights beyond traditional laboratory-based methods. Smartwatches, equipped with sensors such as accelerom-eters, gyroscopes, and photoplethysmography (PPG), offer an accessible means to collect continuous sleep-related data. However, the limited access to raw sensor data and the use of proprietary algorithms in most commercial devices present substantial challenges for researchers aiming for transparency, reproducibility, and methodological flexibility. In response to this gap, this work introduces the development of a research-oriented software application specifically designed to enable the efficient extraction, visualization, and exportation of sleep metrics from smartwatches. The system empowers researchers to configure data acquisition parameters, access both processed metrics and raw sensor readings, and export data in customizable formats, such as CSV and JSON, thereby facilitating downstream scientific analysis. Particular attention was given to creating a user-friendly interface optimized for mobile devices, along with secure data handling mechanisms. This work highlights the growing importance of customizable, open-access tools in sleep research, offering a flexible alternative to closed commercial ecosystems. By bridging the gap between consumer devices and academic research needs, the proposed solution paves the way for broader adoption of wearable technology in decentralized sleep studies and fosters new possibilities for personalized health monitoring and longitudinal sleep assessment in diverse populations.

The classification of sleep and wake states is of paramount importance in the context of sleep disorders. In order to detect and monitor disorders such as obstructive sleep apnea (OSA), it is essential to obtain the total sleep time (TST) so as to assess the severity of the patient’s sleep apnea. With the advent of new technologies for detecting events associated with sleep disorders, it is not always straightforward to calculate the sleep/wakefulness state. Consequently, this work presents the development of a deep learning model (a variant of U-Net) for the detection of sleep/wakefulness states. For this purpose, an engineering approach using Keras Tuner and the use of three signals with minimal processing was employed. The three signals, oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (AbdRes), were selected to ensure both patient comfort during signal collection and the possibility of using portable monitors. The models were trained and tested on data from polysomnography studies, namely the Sleep Heart Health Study (SHHS) and the Multiethnic Study of Atherosclerosis (MESA). The best performing model achieved results with 88% binary precision, 88% recall, 89% precision, 89% f1-score and Cohen’s Kappa of 0.74 for the SHHS test set. The model obtained 82% binary accuracy, 82% recall, 84% precision, 82% f1-score and 0.62 Cohen’s kappa for the MESA data set.

There are a large number of scientific publications that focus on the development and evaluation of artificial intelligence (AI) models for the detection of various pathologies in the field of sleep medicine. However, most of these publications do not show the process or methodology to be followed for the final deployment of these models in a complete diagnostic system (in terms of software and hardware). This is a major drawback when translating from the development or research environment to the real clinical setting. This work focuses on a methodology for deploying an AI model for sleep apnea detection with the end user in mind: the clinician. For the deployment, the transmission of data between the device, the cloud platform and the machine learning server, as well as the protocols used, were considered. In addition, the storage and visualization of the data has been taken into account so that it can be analyzed accurately by experts.

Non-invasive sleep monitoring holds significant promise for enhancing healthcare by offering insights into sleep quality and patterns. In this context, accurate detection of body position is crucial, as it provides essential information for diagnosing and understanding the causes of various sleep disorders, including sleep apnea. The aim of this work is to develop an efficient system for sleep position detection using a minimal number of FSR (Force Sensitive Resistor) sensors and advanced machine learning techniques. A hardware setup was developed incorporating 3 FSR sensors, on-board signal processing for frequency boundary filtering and gain adjustment, an ADC (Analog-to-digital converter), and a computing unit for data processing. The collected data was then cleaned and structured before applying various machine learning models, including Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and XGBoost. An experiment with 15 subjects in 4 different sleeping positions was conducted to evaluate the system. The SVC demonstrated notable performance with a test accuracy of 64%. Analysis of the results identified areas for future improvement, including better differentiation between similar positions. The study highlights the feasibility of using FSR sensors and machine learning for effective sleep position detection. However, further research is needed to improve accuracy and explore more advanced techniques. Future efforts will aim to integrate this approach into a comprehensive, unobtrusive sleep monitoring system, contributing to better healthcare services.

Paying attention helps us learn, advance in our careers, and build successful relationships, but when it’s compromised, achievement of any kind becomes far more challenging. Causes of not paying attention can range from common factors like sleep deprivation, stress, or a mood disorder to health difficulties such as ADHD, OCD, or a thyroid problem that affects concentrating. This work extracts paying attention and not paying attention behavior patterns in the context of learning. In early work, our study identified attention and distraction behaviors using gathered video recordings of online classes. The work found ten paying attention behaviors and six distracted behavior patterns. In this paper, we use computer vision techniques to extract features related to these behaviors. These features are distance between hand and face, pitch yaw roll, eye-to-camera distance, hand-to-camera distance, iris direction, gaze tracking, mouth aspect ratio, eye aspect ratio, distance between face and frame side, and facial landmark configuration. This research also applied three types of machine learning—logistic regression, decision trees, and random forest—and the accuracy rates were 79%, 86%, and 89%, respectively. This result is better than relying only on two extracted features in our previous work.

The importance of sleep in the life of a human being to function in nowadays society is known from a large number of studies. A standard method for sleep analysis is polysomnography (PSG), which uses multiple sensors to measure and analyze multiple signals, allowing precise and detailed sleep analysis. Nonetheless, the cost of using PSG technologies is high in terms of complexity, personnel and time. To overcome these shortcomings, alternative solutions can be used to reduce costs and increase comfort for patients. The objective of this work is to design and develop a prototype of the software component of the sleep analysis system, taking into account the aspects of data flow, data storage and user interface in addition to data processing. The software components implemented and developed in the Morpheus System and described in this article comprise a usable platform capable of assisting custom research implementations in IoT.

The development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training.

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.

Sleep is an essential part of human existence, as we are in this state for approximately a third of our lives. Sleep disorders are common conditions that can affect many aspects of life. Sleep disorders are diagnosed in special laboratories with a polysomnography system, a costly procedure requiring much effort for the patient. Several systems have been proposed to address this situation, including performing the examination and analysis at the patient's home, using sensors to detect physiological signals automatically analysed by algorithms. This work aims to evaluate the use of a contactless respiratory recording system based on an accelerometer sensor in sleep apnea detection. For this purpose, an installation mounted under the bed mattress records the oscillations caused by the chest movements during the breathing process. The presented processing algorithm performs filtering of the obtained signals and determines the apnea events presence. The performance of the developed system and algorithm of apnea event detection (average values of accuracy, specificity and sensitivity are 94.6%, 95.3%, and 93.7% respectively) confirms the suitability of the proposed method and system for further ambulatory and in-home use.

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.

The influence of sleep on human health is enormous. Accordingly, sleep disorders can have a negative impact on it. To avoid this, they should be identified and treated in time. For this purpose, objective (with an appropriate device) or subjective (based on perceived values) measurement methods are used for sleep analysis to understand the problem. The aim of this work is to find out whether an exchange of the two methods is possible and can provide reliable results. In accordance with this goal, a study was conducted with people aged over 65 years old (a total of 154 night-time recordings) in which both measurement methods were compared. Sleep questionnaires and electronic devices for sleep assessment placed under the mattress were applied to achieve the study aims. The obtained results indicated that the correlation between both measurement methods could be observed for sleep characteristics such as total sleep time, total time in bed and sleep efficiency. However, there are also significant differences in absolute values of the two measurement approaches for some subjects/nights, which leads us to conclude that the substitution is more likely to be considered in case of long-term monitoring where the trends are of more importance and not the absolute values for individual nights.

Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.

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.

Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.

Home health applications have evolved over the last few decades. Assistive systems such as a data platform in connection with health devices can allow for health-related data to be automatically transmitted to a database. However, there remain significant challenges concerning intermodular communication. Central among them is the challenge of achieving interoperability, the ability of devices to communicate and share data with each other. A major goal of this project was to extend an existing data platform (COMES®) and establish working interoperability by connecting assistive devices with differing approaches. We describe this process for a sleep monitoring and a physical exercise device. Furthermore, we aimed to test this setup and the implementation with a data platform in both a laboratory and an in-home setting with 11 elderly participants. The platform modification was realized, and the relevant changes were made so that the incoming data could be processed by the data platform, as well as visually displayed in real-time. Data was recorded by the respective device and transmitted into the data server with minor disruptions. Our observations affirmed that difficulties and data loss are far more likely to occur with increasing technical complexity, in the event of instable internet connection, or when the device setup requires (elderly) subjects to take specific steps for proper functioning. We emphasize the importance for tests and evaluations of home health technologies in real-life circumstances.

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 citizen-centered health platform project is intended to provide a platform that can be used in EU cross-border regions, where social and economic exchange occurs across national borders. The overriding challenges are: (a) social: improving citizen-centered health and care provision; (b) technical: providing a digital platform for networking citizens, service providers, and municipal actors; (c) economic: developing long-term successful (sustainable) business models/value chains. The platform should strengthen and expand existing networks and establish new regional networks. Each network addresses particular challenges and apply them in a region-specific manner. Here, the national boundary conditions and the interregional needs play an essential role. These objectives require sufficient participation of civil society representatives. Furthermore, the platform will establish an overarching, sustainable, and knowledge-based network of health experts. The platform is to be jointly developed and implemented in the regions and follow an open-access approach. Therefore, synergies will be shared more quickly, strengthening competencies and competitiveness. In addition to practice partners, scientific and municipal institutions and SMEs are involved. The actors thus contribute to scientific performance, innovative strength, and resilience.

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

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.

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.

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.

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.

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.

Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless technologies, data storage, and evaluation of RR intervals was developed. Some tests yelled runtimes up to 24 hours with wireless Lan activated and streaming.

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

This work is a study about a comparison of survey tools and it should help developers in selecting a suited tool for application in an AAL environment. The first step was to identify the basic required functionality of the survey tools used for AAL technologies and to compare these tools by their functionality and assignments. The comparative study was derived from the data obtained, previous literature studies and further technical data. A list of requirements was stated and ordered in terms of relevance to the target application domain. With the help of an integrated assessment method, the calculation of a generalized estimate value was performed and the result is explained. Finally, the planned application of this tool in a running project is explained.

BACKGROUND: The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011). RESULTS: In order to obtain more detailed information about the electrical activity of the heart, we carry out a reconstruction of the distribution of equivalent electrical sources on the heart surface. In this area, we hold reconstruction of the equivalent sources during the cardiac cycle at relatively low hardware cost. ECG maps of electrical potentials on the surface of the torso (TSPM) and electrical sources on the surface of the heart (HSSM) were studied for different times of the cardiac cycle. We carried out a visual and quantitative comparison of these maps in the presence of pathological regions of different localization. For this purpose we used the model of the heart electrical activity, based on cellular automata. CONCLUSIONS: The model of cellular automata allows us to consider the processes of heart excitation in the presence of pathological regions of various sizes and localization. It is shown, that changes in the distribution of electrical sources on the surface of the epicardium in the presence of pathological areas with disturbances in the conduction of heart excitation are much more noticeable than changes in ECG maps on the torso surface.

Fatigue and drowsiness are responsible for a significant percentage of road traffic accidents. There are several approaches to monitor the driver’s drowsiness, ranging from the driver’s steering behavior to the analysis of the driver, e.g., eye tracking, blinking, yawning, or electrocardiogram (ECG). This paper describes the development of a low-cost ECG sensor to derive heart rate variability (HRV) data for drowsiness detection. The work includes hardware and software design. The hardware was implemented on a printed circuit board (PCB) designed so that the board can be used as an extension shield for an Arduino. The PCB contains a double, inverted ECG channel including low-pass filtering and provides two analog outputs to the Arduino, which combines them and performs the analog-to-digital conversion. The digital ECG signal is transferred to an NVidia embedded PC where the processing takes place, including QRS-complex, heart rate, and HRV detection as well as visualization features. The resulting compact sensor provides good results in the extraction of the main ECG parameters. The sensor is being used in a larger frame, where facial-recognition-based drowsiness detection is combined with ECG-based detection to improve the recognition rate under unfavorable light or occlusion conditions.

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.

The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news, and many other factors that influence the economy in real time. However, while making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This survey merges current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This assessment is made in order to establish how sensor technology can support traders to avoid poor decision making during the trading process. The purpose of this paper is: 1) to review the studies about the impact of stress on the decision-making process and on biological stress parameters that are applied in sensor design; 2) to compare different ways to measure stress by using sensors that are currently available in the market according to basic biometric principles under trading context; and 3) to suggest new directions in the use of sensor technology in stock markets.

Large and complex systems design is still being a challenge even bigger when developing embedded, distributed or real-time systems. OSGi is a platform created to reduce some of the software design problems, increasing reusability, modularity, etc. While, MDA is also designed to simplify software process development using different modelling layers. This paper describes how OSGi services can be designed and implemented using a MDA methodology and how easy it can be implemented with open source tools like Eclipse and its plugins.

Current system-on-chip (SoC) designs incorporate an increasing number of mixed-signal components. Design reuse techniques have proved successful for digital design but these rules are difficult to transfer to mixed-signal design. A top-down methodology is missing but the low level of abstraction in designs makes system integration and verification a very difficult, tedious and complex task. This paper presents a contribution to mixed-signal design reuse where a design methodology is proposed based on modular and parametric behavioural components. They support a design process where non-ideal effects can be incorporated in an incremental way, allowing easy architectural selection and accurate simulations. A working example is used through the paper to highlight and validate the applicability of the methodology.

The application of design reuse to analog and mixed-signal components for System-on-Chip (SoC) is an emerging and revolutionary field. This paper presents a methodological approach to this area illustrated with a mixed-signal case study.

This paper introduces a denotational semantics for a core of the draft IEEE standard analog and mixed signal design language VHDL-AMS, and derives general results about the behaviour of VHDL-AMS programs from it. We include, for example, a demonstration that VHDL-AMS parallelism is benign in the absence of shared initializations. As proof of concept we have built an interpreted simulator that prototypes the semantics and which runs multi-process mixed analog and digital descriptions correctly.

A formal refinement calculus targeted at system-level descriptions in the IEEE standard hardware description language VHDL is described here. Refinement can be used to develop hardware description code that is “correct by construction”. the calculus is closely related to a Hoare-style programming logic for VHDL and real-time systems in general. That logic and a semantics for a core subset of VHDL are described. The programming logic and the associated refinement calculus are shown to be complete. This means that if there is a code that can be shown to implement a given specification, then it will be derivable from the specification via the calculus.