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Review
. 2022 Apr 18:2022:4653923.
doi: 10.1155/2022/4653923. eCollection 2022.

Machine Learning for Healthcare Wearable Devices: The Big Picture

Farida Sabry  1 , Tamer Eltaras  1 , Wadha Labda  1 , Khawla Alzoubi  2 , Qutaibah Malluhi  1
Affiliations
Review

Machine Learning for Healthcare Wearable Devices: The Big Picture

Farida Sabry et al. J Healthc Eng. .

Abstract

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

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Conflict of interest statement

The authors declare that there are no conflicts of interest in this study.

Figures

Figure 1
Figure 1
Wearable device application model.
Figure 2
Figure 2
Human body as a system and signals that can be used as a source of data for machine learning models.
Figure 3
Figure 3
Healthcare machine learning tasks and sensors used for each one in literature.
Figure 4
Figure 4
Box plot of accuracy for the machine learning techniques used in different classification problems for papers cited in Tables 1–3 with accuracy as the evaluation metric. On each box, the central mark is the median and the edges of the box are the 25th and 75th percentiles. The small circles represent outliers.
Figure 5
Figure 5
Challenges to healthcare ML applications on wearable devices.
Figure 6
Figure 6
MLOps for wearable device application.
See this image and copyright information in PMC

References

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