Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Chakrabarti, Shweta; Biswas, Nupur; Jones, L.; Kesari, Santosh; Ashili, Shashaanka

doi:10.3390/diagnostics12092110

Cited by 32 publications

(21 citation statements)

References 142 publications

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“…Moreover, the development of communication tools, such as smart wearables equipped with Machine Learning and Deep Learning models, has opened the door to real-time continuous monitoring. In this context, smart wearables have shown high feasibility in predicting various diseases such as Cardiovascular Diseases [33], diabetes [37], liver disease [38], fatigue and stress [39], mental illness [40], and many other diseases [41]. In addition, ML models have been used to increase the efficiency of healthcare decision systems [42].…”

Section: Machine Learning and Healthcarementioning

confidence: 99%

Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

et al. 2023

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Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has long been a key obstacle in AI, ML and DL. Here, Multimodal Machine Learning (Multimodal ML) has emerged as a method that enables the training of complex ML and DL models that use heterogeneous data in their learning process. In addition, Multimodal ML enables the integration of multiple models in the search for a single, comprehensive solution to a complex problem. In this review, the technical aspects of Multimodal ML are discussed, including a definition of the technology and its technical underpinnings, especially data fusion. It also outlines the differences between this technology and others, such as Ensemble Learning, as well as the various workflows that can be followed in Multimodal ML. In addition, this article examines in depth the use of Multimodal ML in the detection and prediction of Cardiovascular Diseases, highlighting the results obtained so far and the possible starting points for improving its use in the aforementioned field. Finally, a number of the most common problems hindering the development of this technology and potential solutions that could be pursued in future studies are outlined.

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Section: Machine Learning and Healthcarementioning

confidence: 99%

Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

et al. 2023

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“…Commercial wrist-worn devices collect data using accelerometry and other sensors to allow for remote noninvasive measurements, making them generally more convenient, accessible, and comfortable compared with medical or research-grade devices [25,26]; however, their ability to accurately assess outcomes is an important factor for use and application in intervention design, implementation, and evaluation. Although research related to accuracy and validity has been done, continuous advancement in technology and frequent product upgrades make it challenging for research to keep pace with the most recent devices [27].…”

Section: Accuracy Of Wrist-worn Commercial Devices For Assessing Phys...mentioning

confidence: 99%

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

Artese

Raj

Sung

2023

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Purpose of review The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device. Recent findings Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology. Summary With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.

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“…Wrist-worn digital devices, especially smartwatches, are currently the most popular smart consumer wearable tools for healthcare diagnosis and self-management due to their convenience for long-term monitoring ( Chakrabarti et al, 2022 ). In 2021, a methodological review surveyed the electronic health (eHealth) technologies for PD detection in daily life from the past two decades, while the management of symptoms was not investigated ( Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The role of wrist-worn technology in the management of Parkinson’s disease in daily life: A narrative review

Wezel

Zhao

et al. 2023

Front. Neuroinform.

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Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.

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Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Cited by 32 publications

References 142 publications

Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

The role of wrist-worn technology in the management of Parkinson’s disease in daily life: A narrative review

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