Capacitive-Type Pressure Sensor for Classification of the Activities of Daily Living

Abstract

In order to operate a gait rehabilitation device, it is necessary to accurately classify the states appearing in activities of daily living (ADLs). In the case of force sensing resistors (FSRs), which are often used as pressure sensors in gait analysis, it is desirable to replace them with other sensors because of their low durability. In the present study, capacitive-type pressure sensors, as an alternative to FSRs, were developed, and their performance was evaluated. In addition, the timed up and go test was performed to measure the ground reaction force in healthy individuals, and a machine learning technique was applied to the calculated biosignal parameters for the classification of five types of ADLs. The performance evaluation results showed that a sensor with thermoplastic polyurethane (substrate and dielectric layer material) and multiwall carbon nanotubes (conductive layer) has sufficient sensitivity and durability for use as a gait analysis pressure sensor. Moreover, when an overlapping filter was applied to the four-layer long short-term memory (LSTM) or the five-layer LSTM model developed for motion classification, the precision was greater or equal to 95%, and unstable errors did not occur. Therefore, when the pressure sensor and ADLs classification algorithm developed in this study are applied, it is expected that motion classification can be completed within a time range that does not affect the control of the gait rehabilitation device.