This article focuses on the design of a sensor system for a non-planar surface, in particular a cylindrical shape, such as a kayak paddle. The main objective is to develop a piezoresistive sensor system to measure the pressure exerted by the hand on the shaft. The study begins with static characterization of the sensors, including dispersion analysis to assess their sensitivity, linearity and measurement range. A calibration process is carried out using a dedicated test bench, and an inverse viscoelastic model is used to establish an accurate relationship between the measured resistance and the corresponding pressure. The sensor system is connected to a data acquisition board equipped with an analog-to-digital converter (ADC) that enables the direct conversion of analog data into digital resistance values. Furthermore, Bluetooth Low Energy (BLE) wireless communication is employed to facilitate data transfer to a computer, enabling a detailed pressure mapping of the kayak paddle and real-time data collection. The calibrated sensors are then tested and validated on the kayak paddle, facilitating the mapping of pressure zones on the paddle surface. This mapping provides information for locating areas of high pressure exertion during kayaker movements.

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.

Understanding hand–paddle interaction is essential for optimizing performance and preventing injury in kayaking, yet coaches still lack objective, practical tools. We present a soft, instrumented glove that measures and dynamically maps palmar pressure throughout the stroke cycle. A matrix of piezoresistive sensors is integrated into the glove and connected to dedicated electronics housed in a waterproof enclosure. A viscoelastic model converts sensor resistance into forces, enabling time-resolved 3D mapping of contact mechanics. Data are transmitted via Bluetooth Low Energy (BLE). Experimental validation on a kayak ergometer across multiple cadences demonstrated accurate measurements (per-sensor root mean square error (RMSE) of ±2 N), clear delineation of pull and push phases, evolving pressure distribution over the motion, and a peak total right-hand force of 186 N at high cadence. Beyond feasibility, these results position the glove as a practical training aid: it supports athlete-specific load monitoring and the early detection of potentially problematic movement patterns.