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Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review.

Yasmin Abdelaal1, Michaël Aupetit2, Abdelkader Baggag2

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Journal of Medical Internet Research
|December 24, 2024
PubMed
Summary
This summary is machine-generated.

Explainable AI (XAI) is vital for transparent wearable health technologies. While wrist-worn devices are common, making their data understandable requires further development, especially involving user feedback.

Keywords:
XAIanalyticsdeep learningexplainable artificial intelligencehealth informaticsinterpretationmachine learninguser experiencewearablewearable datawearable sensors

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Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Wearable Technology

Background:

  • Wearable technologies are increasingly used in healthcare.
  • Complex AI models in wearables create "black box" issues, hindering trust.
  • Explainable AI (XAI) offers a solution by increasing model transparency.

Purpose of the Study:

  • To review literature on explainability in wearable devices.
  • To explore how XAI enhances data and model interpretability.
  • To identify possibilities at the intersection of wearables and XAI.

Main Methods:

  • Searched ACM, IEEE, PubMed, Springer, JMIR, Nature, Scopus (2018-2022).
  • Included studies on wearables, sensors, mobile phones, XAI, ML, DL, and quantified self data.
  • Analyzed 25 peer-reviewed papers.

Main Results:

  • Wrist-worn wearables (e.g., Fitbit) are common in healthcare.
  • Explainability of data from these devices needs more focus.
  • Post hoc methods, particularly Shapley Additive Explanations, are prominent, often visualized.

Conclusions:

  • XAI integration is key to overcoming "black box" models in wearable health tech.
  • Enhancing data explainability and user involvement is crucial.
  • Further research is needed for transparent and trustworthy AI in healthcare wearables.