Why the study?
Wearable-based technologies for real-time monitoring of stroke-related parameters are undertaken to prevent death and disability, but their characteristics, limitations, and integration strategies need review.
Integrating wearable technologies with IoT, EHRs, and machine learning, alongside multimodal EEG-fNIRS, may improve the precision of dynamic stroke risk prediction.
Wearables for stroke monitoring require accuracy validation before clinical use; leaves open optimal IoT-EHR integration strategies.
We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.
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Chen et al. (2021) studied this question.
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