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Cardiovascular diseases (CVD) are leading contributors to global mortality, necessitating advanced methods for vital sign monitoring. Heart Rate Variability (HRV) and Respiratory Rate, key indicators of cardiovascular health, are traditionally monitored via Electrocardiogram (ECG). However, ECG's obtrusiveness limits its practicality, prompting the exploration of Ballistocardiography (BCG) as a non-invasive alternative. BCG records the mechanical activity of the body with each heartbeat, offering a contactless method for HRV monitoring. Despite its benefits, BCG signals are susceptible to external interference and present a challenge in accurately detecting J-Peaks. This research uses advanced signal processing and deep learning techniques to overcome these limitations. Our approach integrates accelerometers for long-term BCG data collection during sleep, applying Discrete Wavelet Transforms (DWT) and Ensemble Empirical Mode Decomposition (EEMD) for feature extraction. The Bi-LSTM model, leveraging these features, enhances heartbeat detection, offering improved reliability over traditional methods. The study's findings indicate that the combined use of DWT, EEMD, and Bi-LSTM for J-Peak detection in BCG signals is effective, with potential applications in unobtrusive long-term cardiovascular monitoring. Our results suggest that this methodology could contribute to HRV monitoring, particularly in home settings, enhancing patient comfort and compliance.
This study investigates the application of Force Sensing Resistor (FSR) sensors and machine learning algorithms for non-invasive body position monitoring during sleep. Although reliable, traditional methods like Polysomnography (PSG) are invasive and unsuited for extended home-based monitoring. Our approach utilizes FSR sensors placed beneath the mattress to detect body positions effectively. We employed machine learning techniques, specifically Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost algorithms, to analyze the sensor data. The models were trained and tested using data from a controlled study with 15 subjects assuming various sleep positions. The performance of these models was evaluated based on accuracy and confusion matrices. The results indicate XGBoost as the most effective model for this application, followed by RF and KNN, offering promising avenues for home-based sleep monitoring systems.
The massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models.
Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders.
This paper compares two popular scripting implementations for hardware prototyping: Python scripts exe- cut from User-Space and C-based Linux-Driver processes executed from Kernel-Space, which can provide information to researchers when considering one or another in their implementations. Conclusions exhibit that deploying software scripts in the kernel space makes it possible to grant a certain quality of sensor information using a Raspberry Pi without the need for advanced real-time operational systems.
A post-growth economy is a comparatively new paradigm in the tourism discourse. The aim of this article is to find out the commonalities between this concept and Māori tourism and in which way the latter can contribute to a post-growth economy. A qualitative mixed method approach, including in-depth-interviews, participant observation, and secondary analysis is applied. The results show that there is a lot of overlap between Māori tourism and a post-growth economy. Differences are visible, as well, regarding the value approach of Māori tourism and the indicator approach of a post-growth economy. Especially the social innovation created in Aotearoa New Zealand at the instigation of Māori groups of granting legal personhood to parts of nature may serve as a driver for a form of tourism that is in line with the idea of a post-growth economy.
This paper applies the concept of Soja’s Thirdspace to the phenomenon of Lazgi dance and tourism in Uzbekistan. In doing so it analyses the different levels of perception (including Firstspace and Secondspace) of Lazgi and tourism via an autoethnographic lens. Complemented by expert interviews, the interaction of Lazgi and tourism is examined and characteristics of the Lazgisphere (world of Lazgi) in Uzbekistan are distilled. The results show that Lazgi is often directly or indirectly connected with tourism in Uzbekistan, but even more so serves to reaffirm national identity.
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively.
Study design:
Retrospective, mono-centric cohort research study.
Objectives:
The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).
Methods:
An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; .75 rated as excellent), mean error and additional statistical metrics.
Results:
The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).
Conclusions:
The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.
Low-Code Development Plattformen (LCDPs) fördern die digitale Transformation von Organisationen, indem sie die Applikationsentwicklung durch FachbereichsmitarbeiterInnen ohne tiefgreifende Programmierkenntnisse – sogenannte Citizen Developer – ermöglichen. Marktforschungsinstitute prognostizieren, dass in den nächsten Jahren mehr als die Hälfte aller Applikationen mit LCDPs entwickelt werden. Nichtsdestotrotz stehen Organisationen vor der Herausforderung, sich für die richtigen Implementierungs- und Anwendungsansätze von LCDPs zu entscheiden. Dieser Artikel liefert daher ein umfassendes Bild über das praktische Verständnis und aktuelle Ansätze in verschiedenen Organisationen und leitet daraus Handlungsempfehlungen ab. Dafür wurden 16 Experteninterviews durchgeführt und wissenschaftlich analysiert. Die Ergebnisse zeigen, dass die Praxis grundsätzlich ein ähnliches Verständnis des Begriffs LCDP hat. Die Initiative für die Einführung kommt meist aus den Fachbereichen, die Entscheidung für oder gegen die LCDP-Implementierung wird jedoch meist von der Geschäftsführung in Kooperation mit der IT-Abteilung getroffen. Dabei unterscheiden sich die aktuellen Anwendungsansätze: Unternehmen nutzen entweder einen Self-Service-Ansatz durch die Fachbereiche oder integrieren die Entscheidung über eine potenzielle LCDP-Entwicklung durch die Citizen Developer in das bestehende Demand-Management der IT-Abteilung. Eine etablierte und adaptive Governance ist für beide Ansätze eine wichtige Voraussetzung. Die Erkenntnisse des Beitrags tragen zur wissenschaftlichen Diskussion bei, da dieser Artikel eine der ersten umfassenden und wissenschaftlich fundierten qualitativen Analysen über aktuelle praktische Adoptionsansätze der Praxis liefert. PraktikerInnen erfahren zudem, wie andere Unternehmen mit aktuellen Herausforderungen umgehen und welche Ansätze erfolgversprechend sind.