The widespread adoption of instrumented textiles has made a significant impact in various domains, encompassing health monitoring, rehabilitation, biomechanics, and sports. This study specifically focuses on the development and evaluation of a smart garment that employs low-energy flexible sensors embedded within the fabric to effectively monitor upper body movements. These sensors utilize a piezoresistive polymer integrated into the garment and establish a connection with an electronic board for data acquisition. Wireless data transmission is achieved through the utilization of Bluetooth Low Energy (BLE) technology, with the garment showcasing an impressive average power consumption of approximately 10 μW. To ensure the sensor’s performance and reliability, a comprehensive characterization process is meticulously conducted utilizing a dedicated test bench. Furthermore, this study conducts a comparative analysis between two distinct estimators utilized for determining the flexion/extension angles of the upper body joint. The first estimator leverages a Nonlinear AutoRegressive exogenous (NARX) neural network model, while the second estimator employs a viscoelastic model. Through extensive evaluation, it becomes evident that the NARX neural network model outperforms the viscoelastic model, showcasing superior accuracy with a root-mean-square error of 4.85°. Consequently, the NARX neural network model emerges as the preferred option for accurately estimating the flexion/extension angles of the upper body joint.