The quantification of the plantar arch load and its surface distribution is essential to improve locomotion in many sports as well as rehabilitation protocols for patients. This paper has a threefold objective. First, it focuses on the design, fabrication and characterization of dynamic force sensors, based on both a single and multilayer of Velostat piezoresistive polymer. A matrix of 9 x 3 flexible pressure sensors has been developed to analyze and recognize plantar movements. Second, two models linking the plantar pressure to the resistance of the piezoresistive material, with and without taking into account the viscoelastic nature of the piezoresistive material, are proposed. Lastly, an inversion of the models is employed in order to estimate plantar pressure as a function of the variation in material resistance. An evaluation of the plantar pressure for various movements is also proposed.

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.

Physical activity involves movements, which can be considered sources of kinetic energy, that are expected to be important during sports activities. Several transducers can transform this energy into electrical energy. Piezoelectric generators are widely used, and several applications highlight their relevance. However, the generated output power is location dependent, and the analysis of the placement of this kind of generator can be challenging. In order to assess the availability of kinetic energy sources, an acceleration data analysis method is presented. Temporal and harvester model-based studies, using data from 17 inertial measurement units (IMUs) located across the whole human body, were conducted. The results show that piezoelectric cantilever-beam harvesters can be very sensitive to impacts. Extremity segments, such as the feet or hands, can be considered as good energy sources. The most relevant features are proposed as criteria to easily evaluate the harvestable energy sources.

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.

Previous work has shown that the impacts induced by human sport activities are one of the most relevant features to operating a cantilevered piezoelectric harvester. In addition, the optimal orientations of the simulated harvester according to body parts were investigated. In this contribution, we study the influence of the harvester dimensions on the simulated harvested energy. The results show that for a defined mass of active material and the optimal harvester orientation, a low-frequency harvester is preferred. Thus, the harvester operating frequencies, orientation, and location on the human body are explored.