Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling

• Published in: IEEE sensors journal Vol. 15; no. 1; pp. 110 - 122
• Main Authors: Atalay, Ozgur, Kennon, William Richard, Demirok, Erhan
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2015
• Subjects: Contacts; Fabrics; Monitoring; Resistance; Sensors; Strain; Yarn; conductive yarn; Breathing frequency; electro-mechanical modelling; respiration belt; knitted sensor; silver-coated nylon yarn; textile-based strain sensor
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2014.2339739

In this paper, a textile-based strain sensor has been developed to create a respiration belt. The constituent materials and the knitted structure of the textile sensor have been specifically selected and tailored for this application. Electromechanical modeling has been developed by exploiting Peirce's loop model in order to describe the fabric geometry under static and dynamic conditions. Kirchhoff's node and loop equations have been employed to create a generalized solution for the equivalent electrical resistance of the textile sensor for a given knitted loop geometry and for a specified number of loops. A laboratory test setup was built to characterize the prototype sensor and the resulting equivalent resistance under strain levels up to 40%, and consistent resistance response levels have been obtained from the sensor which correlate well with the modelled data. Production of the respiration belt was realized by bringing together knitted sensor and a relatively inelastic textile strap. Both machine simulations and real-time measurements on a human subject have been performed in order to calculate average breathing frequencies under different static and dynamic conditions. Also, different scenarios have been performed, such as slow breathing and rapid breathing. The sensory belt was located in either the chest area or in the abdominal area during the experimental measurements and the sensor yielded a good response under both static and dynamic conditions. However, body motion artefacts affected the signal quality under dynamic conditions and an additional signal-processing step was added to eliminate unwanted interference from the breathing signal.