Fingertip Micro-Motion as a Source of Respiratory Information During Sleep Using Triaxial Accelerometers

2026-04-24

AI Summary

This study investigates whether a triaxial accelerometer worn on the fingertip can effectively capture instantaneous respiratory rate (IRR) and respiratory effort during sleep. To this end, the authors propose a respiratory surrogate signal (TAA-resp) derived via an inverse derivative-based nonlinear transformation, and introduce a Respiratory Motion Index (RMI) based on time–frequency analysis to quantify micromotion intensity and signal quality. The first systematic validation demonstrates that fingertip micromotions serve as a reliable source of respiratory information: TAA-resp yields high-quality data for 22.2% ± 15.6% of overnight recordings, with IRR estimation errors as low as 0.027 ± 0.022 Hz. Furthermore, RMI exhibits strong discriminative ability in predicting signal quality, achieving a sensitivity of 0.74 and specificity of 0.75.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Objective: Triaxial accelerometers (TAAs) are widely used in homecare medicine. This study investigates whether TAA signals recorded at the fingertip encode respiratory information, particularly instantaneous respiratory rate (IRR) and respiratory effort, during sleep. Method: We propose an antiderivative-based nonlinear transformation to convert TAA signals into a respiratory surrogate, termed TAA-resp. To quantify the embedded respiratory-induced motion, a modern time-frequency analysis tool is applied to derive an index, referred to as the respiratory motion index (RMI). The proposed TAA-resp and RMI are validated on a dataset comprising 39 full-night recordings with simultaneous polysomnography (PSG) and a fingertip TAA measurements. Criteria for labeling TAA-resp signal quality as good, moderate, or poor are established, and expert annotations are obtained. Result: On average, TAA-resp over 22.2% $\pm$ 15.6% of full-night recordings encodes high-quality respiratory information, reaching up to 58.9% in some cases. TAA-resp shows stronger correlation with thoracic and abdominal motion than with airflow, indicating predominant capture of respiratory effort. High-quality TAA-resp offers an accurate IRR estimate with root mean square error $0.027 \pm 0.022$ Hz. RMI is higher for high-quality segments and lower for poor-quality segments, and its distribution aligns with physiology, with higher values during REM, N2, and N3 sleep and in the absence of apnea or hypopnea events. In leave-one-subject-out cross-validation, RMI predicts quality labels with 0.74 sensitivity and 0.75 specificity. Conclusion: Fingertip-mounted TAAs encode meaningful respiratory information. Leveraging this underutilized signal may enhance home-based sleep monitoring in channel-limited settings.

Problem

• respiratory monitoring
• tri-axial accelerometer
• sleep
• instantaneous respiratory rate
• respiratory effort

Innovation

• triangular accelerometers
• respiratory surrogate
• nonlinear transformation
• time-frequency analysis
• respiratory motion index