Abstract
This paper presents the development of an optical fiber-integrated smart textile used as an instrumented pants for biomechanical and activity recognition. The optical fiber sensor is based on the multiplexed intensity variation technique in which a side coupling between a polymer optical fiber (POF) and light sources with controlled modulation is developed. In addition, the sensor system is integrated into pants, where two POFs with 30 sensors each are placed on the left and right legs of the proposed POF Smart Pants. After the device's fabrication and assembly, the 60 optical fiber sensors are characterized as a function of the transverse displacement on the sensor's region. In this case, each sensor presented its sensitivities (108.03 ± 100 mV/mm), which are used on the sensor normalization prior to the data analysis. Then, the tests with volunteer performing different daily activities indicated the suitability of the proposed device on the assessment of biomechanics of human movement in different activities as well as the spatio-temporal parameters of the gait in different velocity conditions. For activity recognition, a neural network is applied and presented 100% accuracy on the activity recognition. Then, to provide an optimization of the number of sensors, the principal components analysis is applied and indicated a threefold reduction of the number of sensors with an accuracy of 99%. Thus, the proposed POF Smart Pants is a feasible alternative for a low-cost and highly reliable sensor system for remote monitoring of different patients, with the possibility of customizing the device for different users.