My Pillow Knows My Sleep: Sleep Monitoring with Computational Fabrics in the Pillowcase

Sleep is a vital physiological state that significantly impacts overall health. Continuous monitoring of sleep posture, heart rate, respiratory rate, and body movement is crucial for diagnosing and managing sleep disorders. Current monitoring solutions often disrupt natural sleep due to discomfort or raise privacy and instrumentation concerns. We introduce PillowSense, a fabric-based sleep monitoring system seamlessly integrated into a pillowcase. PillowSense utilizes a dual-layer fabric design. The top layer comprises conductive fabrics for sensing electrocardiogram (ECG) and surface electromyogram (sEMG), while the bottom layer features pressure-sensitive fabrics to monitor sleep location and movement. The system processes ECG and sEMG signals sequentially to infer multiple sleep variables and incorporates an adversarial neural network to enhance posture classification accuracy. We fabricate prototypes using off-the-shelf hardware and conduct both lab-based and in-the-wild longitudinal user studies to evaluate the system's effectiveness. Across 151 nights and 912.2 hours of real-world sleep data, the system achieves an F1 score of 88% for classifying seven sleep postures, and clinically-acceptable accuracy in vital sign monitoring. PillowSense's comfort, washability, and robustness in multi-user scenarios underscore its potential for unobtrusive, large-scale sleep monitoring.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2025-09-03
DOI
https://doi.org/10.1145/3749550
Citations
4
Primary Topic
Sleep and Wakefulness Research
Type
article
Field-Weighted Citation Impact
5.72

Funders

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article

My Pillow Knows My Sleep: Sleep Monitoring with Computational Fabrics in the Pillowcase

Jiting Liu, Junxiao Chen, Qijia Shao, Ho Man Colman Leung et al.
4 citations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Sleep and Wakefulness Research
5.72
article

My Pillow Knows My Sleep: Sleep Monitoring with Computational Fabrics in the Pillowcase

Jiting Liu, Junxiao Chen, Qijia Shao, Ho Man Colman Leung, Xia Zhou, Xiaofan Jiang, Marie‐Pierre St‐Onge, Meiqi Zhao, R.F Xu, L. García
article en
4 citations

Abstract

Sleep is a vital physiological state that significantly impacts overall health. Continuous monitoring of sleep posture, heart rate, respiratory rate, and body movement is crucial for diagnosing and managing sleep disorders. Current monitoring solutions often disrupt natural sleep due to discomfort or raise privacy and instrumentation concerns. We introduce PillowSense, a fabric-based sleep monitoring system seamlessly integrated into a pillowcase. PillowSense utilizes a dual-layer fabric design. The top layer comprises conductive fabrics for sensing electrocardiogram (ECG) and surface electromyogram (sEMG), while the bottom layer features pressure-sensitive fabrics to monitor sleep location and movement. The system processes ECG and sEMG signals sequentially to infer multiple sleep variables and incorporates an adversarial neural network to enhance posture classification accuracy. We fabricate prototypes using off-the-shelf hardware and conduct both lab-based and in-the-wild longitudinal user studies to evaluate the system's effectiveness. Across 151 nights and 912.2 hours of real-world sleep data, the system achieves an F1 score of 88% for classifying seven sleep postures, and clinically-acceptable accuracy in vital sign monitoring. PillowSense's comfort, washability, and robustness in multi-user scenarios underscore its potential for unobtrusive, large-scale sleep monitoring.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 9(3)
Hong Kong University of Science and Technology (HK), Columbia University (US), University of Hong Kong (HK), Tsinghua University (CN)
National Science Foundation, Columbia University
Openalex Percentile: Top 4%
Sleep and Wakefulness Research
5.72
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