The detection of biosignals by using a comfortable material is important to improve human health. This paper presents a complete wearable system with smart clothing for the long-term monitoring of lead-I ECG and respiratory signals. The proposed system is divided into three parts, including biosignal-monitoring clothing, a biosignal acquisition device, and a software platform. The smart clothing integrates fabric-based ECG dry electrodes, conductive fiber traces, and a high-sensitivity capacitive respiration transducer to sense the biosignals. The challenge including the integration of ECG electrodes and the respiration transducer in the clothing system to provide the high-quality ECG and respiration signals for clinical use has been overcome in the proposed biosignal-monitoring clothing. The sensed signals on the smart clothing are collected in the biosignal-acquisition device through fabric-based traces and a specially designed clothing structure. Furthermore, the biosignals are processed using the biosignal-acquisition device and sent to the remote smart device through the Bluetooth module. The device according to the requirement of the front-end clothing system and the actual measurement accuracy is implemented and contributed onto the reduction in the effect of motion artifact. The software platform on the smart device provides real-time biosignal monitoring and health-information analysis. A highly efficient ECG QRS complex detection algorithm and respiratory-rate detection algorithm are also proposed. The ECG QRS complex detection algorithm is verified using the MIT/BIH Arrhythmia Database to demonstrate the achievement of high performance. The overall measured sensitivity, positive prediction, and error rate of the proposed algorithm are 99.86%, 99.93%, and 0.19%, respectively. The measured ECG signal of the clothing system is compared with the commercial silver/silver-chloride electrode by using the BIOPAC MP36 acquisition system. The result confirms the high quality of the signal, so the measured ECG signal can be used for medical applications. The function of the respiration transducer is verified by the BIOPAC SS11LA airflow transducer, and the overall accuracy of 19 test subjects is 98.74%. Using the proposed system, long-term health care in daily life can be achieved.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)
- Electrical and Electronic Engineering