Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Area of Science:

• Biomedical Engineering
• Digital Health
• Wearable Technology

Background:

• Smart wearable devices are increasingly used, impacting lifestyles and advancing biological research and personalized healthcare.
• These sensor-equipped devices function as digital diagnostic tools, enabling prediction and detection of physiological and psychological conditions.
• Wrist-worn wearables are particularly noted for their diagnostic potential across various health domains.

Purpose of the Study:

• To review the diagnostic applications of wrist-worn wearables for detecting multiple diseases.
• To explore the role of machine learning in analyzing wearable data for health insights.
• To discuss current challenges and future perspectives of wearables in research and personalized healthcare.

Main Methods:

• Literature review focusing on diagnostic applications of wrist-worn wearables.
• Analysis of machine learning techniques applied to wearable sensor data.
• Examination of challenges and future trends in wearable technology for healthcare.

Main Results:

• Wearables effectively aid in diagnosing cardiovascular, neurological, metabolic disorders, and psychological illnesses.
• Machine learning significantly enhances the analysis of wearable data for accurate health predictions.
• Identified key challenges include data quality, privacy, and integration into clinical workflows.

Conclusions:

• Wrist-worn wearables, coupled with machine learning, show great promise as diagnostic tools.
• Further research and development are needed to overcome current challenges for widespread adoption.
• Wearable technology is poised to revolutionize personalized healthcare and biomedical research.