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Review
. 2024 Feb 13;14(2):203.
doi: 10.3390/jpm14020203.

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Ali Olyanasab  1 , Mohsen Annabestani  2
Affiliations
Review

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Ali Olyanasab et al. J Pers Med. .

Abstract

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer's physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual's physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

Keywords: machine learning; personalized; wearable devices.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The blue plot illustrates number of publications on wearable devices utilizing machine learning and the orange plot is number of publications on personalized wearable devices using machine learning [15]. The keywords used were “wearable machine learning” and “personalized wearable machine learning”. The extrapolated data for 2024 were based on the number of publications up to 25 January 2024.
Figure 2
Figure 2
Illustration of three main categories of personalized wearable devices: bio-electrical, bio-impedance, and electro-chemical and electro-mechanical wearable devices. The figure was generated using the Bing AI chat bot.
Figure 3
Figure 3
Schematics depicting the sensor setups for: (a) a system to prevent heat stroke in hot environments [22], (b) a real-time emotion recognition system [16], and (c) an intelligent wearable system for wound monitoring [28]. (c) is adapted by permission from [28]. Copyright 2022 American Chemical Society.
Figure 4
Figure 4
Illustration of examples on bio-impedance and electro-chemical wearables as presented in the literature: (a) a wearable system utilizing electromagnetic sensors to non-invasive glucose level measuring [33], (b) a non-printed integrated-circuit textile [39], (c) a microfluidic patch for continuous sweat analysis [35], and (d) an impedance-based wearable for real-time bladder monitoring [12].
Figure 5
Figure 5
Setup for various electro-mechanical wearable devices: (a) a set of wearable sensors to estimate knee flexion [48], (b) a thin, soft, and miniaturized design for arterial blood pressure [56], and (c) Ti3C2Tx MXene sensor modules for full-body motion classifications [58].
See this image and copyright information in PMC

References

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