The Impact of Domain Shift on Predicting Perceived Sleep Quality from Wearables

The Impact of Domain Shift on Predicting Perceived Sleep Quality from Wearables

Machine learning models for personal informatics systems are typically trained offline on <i>records of a specific population of users</i>, resulting in <i>population models.</i> These models may suffer performance degradation in real-world settings due to <i>domain shi...

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Bibliographic Details
Main Authors: Nouran Abdalazim, Leonardo Alchieri, Lidia Alecci, Pietro Barbiero, Silvia Santini
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
personal informatics systems
sleep behaviour monitoring
sleep quality recognition
dataset bias
domain shift
covariate shift
Online Access:https://www.mdpi.com/1424-8220/25/13/4012
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Summary:Machine learning models for personal informatics systems are typically trained offline on <i>records of a specific population of users</i>, resulting in <i>population models.</i> These models may suffer performance degradation in real-world settings due to <i>domain shift</i>, i.e., differences in data distributions across users and contexts. Domain adaptation techniques can address this <i>issue</i> by, <i>e.g.,</i> personalizing models with user-specific data. <i>In this paper, we quantify the impact of domain shift</i> on <i>the performance</i> of both population and personalized models <i>in a specific scenario:</i> sleep quality recognition. <i>To this end, we also collect and make available to the research community the new BiheartS dataset</i>. Our analysis shows <i>that</i> domain shift <i>causes the</i> accuracy of population models <i>to decrease</i> by up to 18.54 percentage points, when <i>used</i> on <i>new data</i>. Personalized models, <i>instead</i>, show robust performance across datasets. However, <i>crafting personalized models typically requires using new data or user-provided labels</i>, limiting their <i>applicability in real settings</i>. To <i>mitigate</i> the limitations <i>of both population and personalized models</i>, we propose a novel unsupervised domain adaptation approach: the cluster-based population model (CBPM). CBPM achieves accuracy improvements of up to 13.45 percentage points <i>w.r.t. population model</i> without requiring <i>the use of</i> user-specific records or <i>labels</i>.
ISSN:1424-8220

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