Evidence map›Paper›PMID 42688711›Full record

ArticleDigital health

Flexible pressure sensor pants for app-supported sleep posture monitoring: Feasibility, accuracy, and user experience study.

Yongen Miao, Jinchuan He, Ji Chen, Yingchuan Zhao, Yunshan Fan, Sheng Yang, Shisheng He

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yongen MiaoDepartment of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Jinchuan HeDepartment of Medical Physics and Biomedical Engineering, University College London, London, W1T6HB, UK.
Ji ChenDepartment of Respiratory and Critical Care Medicine, 925 Hospital of PLA Joint Logistics Support Force, Guiyang, China.
Yingchuan ZhaoDepartment of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Yunshan FanDepartment of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Sheng YangDepartment of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Shisheng HeDepartment of Spine Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0001-7796-1510

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to develop and validate an innovative wearable system-flexible pressure sensor pants-to overcome the privacy, comfort, and portability limitations of conventional sleep posture monitoring methods (e.g., infrared imaging and pressure-sensitive mattresses). The study evaluates the system's posture classification performance and its feasibility for continuous monitoring in real-world settings. Methods: Thirty-four young adults were recruited. During short-term wakefulness trials, participants wore the sensor-integrated sleep pants and assumed four predefined postures (supine, prone, left lateral, and right lateral), yielding 202 annotated posture instances. A confusion matrix was employed to derive sensitivity, specificity, and overall accuracy. Overnight monitoring involved concurrent data acquisition from the sensorized sleep pants and an infrared camera. Statistical analyses included paired t-tests or Mann-Whitney U tests to examine inter-method differences, Bland-Altman plots for agreement assessment, and scatter plots to quantify linear correlations. User feedback on comfort and impact on sleep was collected via questionnaire. Results: In awake trials, the system achieved 96% overall accuracy, with mean sensitivity and specificity of 0.95 (0.93-0.98) and 0.98 (0.97-0.99), respectively. Overnight data revealed no statistically significant differences between the two monitoring methods across all postures. Bland-Altman analyses indicated high concordance, while scatter plots demonstrated strong correlations: prone (R=0.5423, Conclusion: Flexible pressure sensor pants demonstrated high accuracy, strong agreement with infrared monitoring. The high user satisfaction and practical convenience indicate their potential to meet essential requirements for sleep posture assessment. Compared with traditional methods, they offer greater comfort, portability, and privacy protection, supporting feasibility for real-world sleep posture monitoring.

Indexed as

flexible pressure sensorsmobile healthsleep posture recognitionuser experiencewearable technology

Identifiers

PMID42688711
PMCPMC13535072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.