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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
Summary
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.
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.
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