Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing

Chien Lung Shen, Tzu Hao Huang, Po Chun Hsu, Ya Chi Ko, Fen Ling Chen, Wei Chun Wang, Tsair Kao*, Chia Tai Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.

Original languageEnglish
Pages (from-to)826-842
Number of pages17
JournalJournal of Medical and Biological Engineering
Issue number6
StatePublished - 1 Dec 2017


  • Multiple sensors
  • Respiration rate
  • Smart clothing
  • Textile electrode


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