Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

Chen, Yun-Hsuan

doi:10.3390/s21020460

Cited by 24 publications

(12 citation statements)

References 124 publications

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“…In terms of future applications, it is expected to be used for EHRs or annual health examinations. Moreover, it can be combined with wearable devices to obtain accurate physiological data in real time [ 48 ]. Finally, since there was only one dataset in this research, the generalization ability of the model still needs to be tested.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction

Zheng

Chen

2022

Healthcare

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Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study’s dataset to monitor changes in individuals’ health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors.

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Section: Discussionmentioning

confidence: 99%

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction

Zheng

Chen

2022

Healthcare

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“…For EEG sensors to be used in wearables, the abovementioned problems must be addressed. Consequently, contact electrodes acquire data from only the prefrontal lobe, and other sensors, such as EOG, magnetoencephalography (MEG), body temperature and heartbeat sensors, are utilized to obtain additional information for comprehensive application judgment [ 78 , 79 ].…”

Section: Electroencephalogrammentioning

confidence: 99%

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

Ching

Wang

et al. 2022

Biosensors

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Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

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“…Compared with CT and MRI medical equipment, the significant advantages of PAI are label-free and dynamic functional imaging with smaller equipment. Present, a wearable system like EEG and fNIRS has been used in stroke prediction [ 153 ], which is expected to be guidance on stroke monitoring and prediction. Although the present PAI system is limited for daily monitoring of human beings, the potential for body condition monitoring before, during, and after the onset of stroke should not be underestimated.…”

Section: Challenges and Prospects Of Pai For Strokementioning

confidence: 99%