Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data

Irshad, Muhammad Tausif; Li, Frédéric; Nisar, Muhammad Adeel; Huang, Xinyu; Buss, Martje; Kloep, Leonie; Peifer, Corinna; Kozusznik, Barbara; Pollak, Anita; Pyszka, Adrian; Flak, Olaf; Grzegorzek, Marcin

doi:10.1016/j.compbiomed.2023.107489

Cited by 7 publications

(1 citation statement)

References 45 publications

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“…One of the main decisions was the choice between using quantitative or qualitative data. Quantitative data for CVD diagnosis are generally collected through objective measurements, such as electrocardiographic (ECG) signals, and are often used in machine learning algorithms [30][31][32][33][34][35]. However, raw sensor data are not readily available to patients and can only be accessed by medical personnel with specialized software provided by the medical device manufacturer.…”

Section: Methodsmentioning

confidence: 99%

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Doniec,

Berepiki,

Piaseczna

et al. 2024

Applied Sciences

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Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.

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