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

• DOI: 10.1007/s40846-017-0247-z
• Publication Date: 2017-07-01T14:50:30Z
• Authors: Chien-Lung Shen; Tzu-Hao Huang; Po-Chun Hsu; Ya-Chi Ko; Fen-Ling Chen; Wei-Chun Wang; Tsair Kao; Chia-Tai Chan
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; Original Article; 02 engineering and technology; 3. Good health

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