Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review

Abstract

The integration of XAI into wearable health care technologies is crucial to address the issue of black box models. While wrist-worn wearables are widespread, there is a notable gap in making the data they generate explainable. Post hoc methods such as Shapley Additive Explanations have gained traction for their adaptability in explaining complex algorithms visually. However, user evaluation remains an area in which improvement is needed, and involving users in the development process can contribute to more transparent and reliable artificial intelligence models in health care applications. Further research in this area is essential to enhance the transparency and trustworthiness of artificial intelligence models used in wearable health care technology.

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