| 1. |
- Alirezaie, Marjan, 1980-, et al.
(författare)
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An ontology-based context-aware system for smart homes : E-care@home
- 2017
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.
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| 2. |
- Santini, Marina, et al.
(författare)
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Designing an Extensible Domain-Specific Web Corpus for “Layfication” : A Case Study in eCare at Home
- 2019
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Ingår i: Cyber-Physical Systems for Social Applications. - Hershey, PA, USA : IGI Global. - 9781522593454 - 9781522578802 ; , s. 98-155
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Bokkapitel (refereegranskat)abstract
- In the era of data-driven science, corpus-based language technology is an essential part of cyber physical systems. In this chapter, the authors describe the design and the development of an extensible domain-specific web corpus to be used in a distributed social application for the care of the elderly at home. The domain of interest is the medical field of chronic diseases. The corpus is conceived as a flexible and extensible textual resource, where additional documents and additional languages will be appended over time. The main purpose of the corpus is to be used for building and training language technology applications for the “layfication” of the specialized medical jargon. “Layfication” refers to the automatic identification of more intuitive linguistic expressions that can help laypeople (e.g., patients, family caregivers, and home care aides) understand medical terms, which often appear opaque. Exploratory experiments are presented and discussed.
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| 3. |
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| 4. |
- Abdelakram, Hafid, et al.
(författare)
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Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring
- 2023
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Konferensbidrag (refereegranskat)abstract
- Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.
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| 5. |
- Abdullah, Saad, et al.
(författare)
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A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)
- 2023
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Ingår i: Electronics. - : MDPI AG. - 2079-9292. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.
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| 6. |
- Abdullah, Saad, et al.
(författare)
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Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features
- 2023
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Ingår i: Frontiers in Cardiovascular Medicine. - 2297-055X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.
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| 7. |
- Abdullah, Saad, et al.
(författare)
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Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters
- 2023
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Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350312249 ; , s. 923-924
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Konferensbidrag (refereegranskat)abstract
- Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.
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| 8. |
- Abdullah, Saad, et al.
(författare)
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PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points
- 2023
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Ingår i: Frontiers in Bioengineering and Biotechnology. - 2296-4185. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.
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| 9. |
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| 10. |
- Akalin, Neziha, 1988-, et al.
(författare)
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A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction
- 2023
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Ingår i: International Journal of Social Robotics. - : Springer Science and Business Media B.V.. - 1875-4791 .- 1875-4805.
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Tidskriftsartikel (refereegranskat)abstract
- Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.
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