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Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices
Get PDFBattery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers
Rezension von: The Cambridge Handbook of Corrective Feedback in Second Language Learning and Teaching / Nassaji, Hossein; Kartchava, Eva (Hrsg.), 2021
No full textEnvironmental Impact Assessment of IoT Devices: A Graph-based Approach
No full textThe proliferation of the Internet of Things (IoT) has enriched modern life, but their increasing ubiquity raises concerns about environmental impact. To address this, comprehensive Life Cycle Assessments (LCAs) of IoT products, which have historically been manual, costly, and time-consuming, are vital. Noting the recurring nature of core components in IoT devices, such as CPUs and sensors, we propose to use graphs and machine learning to simplify and scale LCA estimations for IoT products. This paper introduces a novel approach to representing IoT devices as graphs with specific component characteristics and interconnections. Applied to a preliminary dataset of smart home IoT devices, the methodology unveils insights into structural similarities using a composite kernel approach. This initial phase lays the groundwork for the machine learning component. The integration of machine learning planned as part of ongoing research, provides a pathway for efficient and timely ecological assessments, ensuring that the rapid growth of IoT aligns with sustainable practices
Evolution of Bed-Based Sensor Technology in Unobtrusive Sleep Monitoring: A Review
No full textWith the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in various settings. We conducted a retrospective literature review, summarizing the state-of-the-art research over the past decade on non-contact bed-based sensor technology in sleep monitoring. We developed a three-category terminology: unobtrusive sensor technology, application, and subject. A total of 263 unique articles were acquired from three databases and screened for relevance, resulting in 21 papers selected for in-depth analysis. The findings revealed eight types of sensors: six mechanical sensors (pressure, accelerometer, piezoelectric, load cell, electromechanical film (EMFI), and hydraulic) and two FOSs (fiber Bragg grating and microbend FOS) that are integrated with or positioned under the bed at three levels of unobtrusiveness. We identified 15 parameters, with heart rate (HR) (14) and respiratory rate (RR) (13) being the most frequently measured. These parameters are generally categorized into three applications: disease-related diagnosis (18), general sleep analysis (9), and general well-being (11). The results indicated that sleep apnea (5) and insomnia (2) were the most frequently detected sleep disorders. Additionally, 59.1% (13) of the systems were tested in a lab environment, with only one undergoing clinical trials. In summary, there is a clear lack of convincing proof of the systems’ effectiveness in continuous in-home sleep monitoring
Perception disparity: Analyzing the destination image of Uzbekistan among residents and non-visitors
Get PDFDestination image is a crucial aspect of tourism research. Although extensively studied, recent research highlights the need to explore residents' views and non-visitors' perceptions of destinations. This study aims to address this gap by contrasting Resident Destination Image (RDI) with Tourist Destination Image (TDI) among non-visitors, using Uzbekistan as a case study. The research investigates how Uzbekistan is perceived by its residents in Samarkand and non-visitors in Germany, employing a mixed-method approach of surveys, focus group discussions, and observations. Findings reveal a significant divergence between the positive self-perception of residents and the often unclear or negative image held by non-visitors. The study underscores the influence of stereotypes on non-visitors' perceptions and the need for targeted marketing to bridge the gap between RDI and TDI to unlock the country's untapped tourism potential. The results suggest that enhancing the destination's image through informed marketing strategies can attract more international tourists and support the country's tourism development
AI-Based System for In-Bed Body Posture Identification Using FSR Sensor
No full textNon-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
Deployment of Artificial Intelligence Models for Sleep Apnea Recognition in the Sleep Laboratory
No full textThere 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
Investigations on the Hadamard product of matrices and polynomials
No full textThe Hadamard product of two matrices of the same order is obtained by entry-wise multiplication of their coefficients. In a similar way, the Hadamard power of a matrix and a polynomial is formed by real powers of their coefficients. Results for the Hadamard product of some important classes of matrices, e.g., positive definite matrices, conditionally negative definite matrices, and matrices with one positive eigenvalue are presented. The results are extended to give sufficient conditions for symmetric matrices to have exactly one positive eigenvalue. A Hurwitz (or stable) polynomial is a real polynomial whose roots are located in the open left half of the complex plane. Results for the Hadamard square root of Hurwitz polynomials of degree five are given. Also, a type of Oppenheim's inequality for Hurwitz matrices is presented. Finally, interval matrices, i.e., matrices with intervals as entries are studied, and new results for the interval property of several classes of matrices, e.g., inverse M-matrices, conditionally positive (negative) semidefinite matrices, and infinitely divisible matrices are given
It's a Match: Connecting Factors of Support and Sustainable Women Entrepreneurs
No full textAlthough entrepreneurship support, women entrepreneurship, and sustainable entrepreneurship are highly relevant for society, the environment, and politics, research on this topic is rare, the results are divergent and there is a lack of focus on the characteristics of support mechanisms. In light of this, we conduct a qualitative content analysis of an entrepreneurship support program for sustainable entrepreneurs in Sweden and its women entrepreneurs. The main objectives of this paper are to identify and investigate the factors that influence the matching of an entrepreneurship support program with women entrepreneurs, and to better understand these processes. This study aims to provide a comprehensive understanding of the establishment, recruitment, and decision to attend a support program from the different parties involved. By using data from semi-structured interviews, desk research and an additional dataset, we found ten prerequisites and factors that influence the matching process between an entrepreneurship support program and women entrepreneurs. The paper offers a matching model that highlights possible differences in the motivation of different stakeholders and the relationships between these differences. By focusing on women sustainable entrepreneurship, this paper contributes to the current discussion on the specific needs these entrepreneurs experience in the initial phases of entrepreneurship support and shows how policy makers and direct support providers can improve their support practices
