Raspberry Pi single-board computers and deep learning, particularly convolutional neural networks, were the most commonly used platforms and techniques for AI-based healthcare embedded systems.
Systematic Review (n=50)
This systematic review highlights that Raspberry Pi and convolutional neural networks are the dominant technologies for real-time AI-based biosignal analysis in healthcare embedded systems, particularly for cardiac applications.
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.
Aziz et al. (2026) conducted a systematic review in Healthcare Embedded Systems (n=50). Healthcare Embedded Systems using AI-based biosignal analysis was evaluated on Healthcare challenges addressed, embedded platforms utilized, and data analysis techniques used for real-time HES applications. Raspberry Pi single-board computers and deep learning, particularly convolutional neural networks, were the most commonly used platforms and techniques for AI-based healthcare embedded systems.