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A Systematic Literature Review of Healthcare Embedded Systems Using AI-based Biosignal Analysis
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DOI：10.1145/3793669.png)
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Abstract
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Healthcare Embedded Systems (HES) use biosensors to capture physiological data, analyse it with advanced algorithms, and provide timely alerts during emergencies. These systems enhance healthcare delivery by supporting diagnosis, early symptom detection, and disease prediction. Despite extensive research on data analysis techniques in healthcare, selecting real-time methods for specific embedded hardware remains challenging. This review aims at summarising and synthesising existing literature to: (a) identify the healthcare challenges addressed by HES and the types of biosignals employed, (b) explore the embedded platforms utilised for implementing HES, and (c) examine the data analysis techniques used for real-time HES applications. A systematic search across three electronic databases (2015-2024), identified 50 relevant studies. These studies span various application domains, biosensing modalities, feature extraction methods, and machine learning and deep learning techniques. Raspberry Pi single-board computers emerged as the most popular embedded platform for implementing AI-based HES. Deep learning, especially convolutional neural networks, dominated, with cardiac health as the primary focus. While the reviewed studies demonstrate promising results, they are often constrained by specific experimental contexts. This review offers a comprehensive overview of real-time data analysis in HES and highlights key opportunities for future research to advance the field.
Keywords:
Healthcare embedded systems
machine learning
deep learning
biosignals
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28
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2.4K
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3.5W
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