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Updated: Sep 3, 2026

Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
Characteristics of Abdominal Movement Signals Measured by a Wireless Abdomen-Worn Sensor During Home Sleep Apnea
Thi Hang Dang1,2, Nam-Hwan Sung2, Hyung-Ki Min2
1Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan, 44919, Republic of Korea, 82 10-5545-4800.
Background:
Respiratory inductance plethysmography (RIP) belts are the standard for measuring thoracoabdominal movements in home sleep apnea testing (HSAT), but are often cumbersome and power-intensive.
Objective:
This study aimed to characterize abdominal movement signals measured by a wireless, single-point, abdomen-worn sensor to evaluate the sensor's capability to track respiratory dynamics.
Methods:
Overnight recordings were obtained from 37 participants using a wireless abdomen-worn sensor and a thoracic RIP belt during HSAT. The abdominal movement signal was analyzed for breath detection, respiratory rate (RR) estimation, and waveform similarity relative to the RIP signal. Factors influencing the agreement between the 2 signals were also investigated.
Results:
Data from 34 participants were analyzed. The abdominal movement signal showed moderate agreement with the thoracic RIP signal, achieving a sensitivity of 82.44%, a positive predictive value of 76.22%, and an F1-score of 78.99% for breath-cycle detection. RR estimation yielded a mean absolute percentage error of 5.42% and limits of agreement of ±3 breaths per minute (bpm). Morphological similarity was moderate, with an average distance correlation of 0.73 and a mean squared error of 0.64. The agreement between the 2 signals declined with increasing respiratory event severity.
Conclusions:
Compared with a standard thoracic RIP belt, the single-point, wireless, abdomen-worn sensor tracked basic respiratory metrics and waveform morphology with moderate agreement. These findings show promising baseline performance and suggest its viability as a simplified, low-profile data-acquisition platform for home-based respiratory monitoring.

