The individual monitoring of cross-country skiers’ technique-related parameters is crucial to identifying possible athlete-individual deficits that need to be corrected in order to optimize the athlete’s performance in competition. To be able to record relevant biomechanical parameters during training in the field, the development of measuring systems exploiting the athlete’s full potential is the key. Known mobile monitoring systems for measuring forces on ski poles use comparably heavy uniaxial load cells mounted on the pole with a data logger also attached to the pole or carried by the athlete. Measurements that are more accurate can be acquired using wire-based systems. However, wire-based systems are highly immobile and only usable when the athletes undergo a stationary test, e.g., on a treadmill. This paper focuses on the functional design of a measuring system using specialized, miniaturized electronics for acquiring data from strain sensors. These data are then used to determine the technique-related parameters pole force and angle of bend. The functional design is also capable of transmitting the acquired data wirelessly via Bluetooth to a smartphone that runs a proprietary app. This approach is advantageous regarding mass, dynamic behavior, analyzing functionality, and signal processing compared to the state of the art.

Understanding the probabilistic distribution of the electrical properties of carbon fibers (CFs) at the filament level is crucial for the development of function‐integrative composite materials and the design of tailored CFs. While models describing the distribution of CF tensile strength are well established, the distribution of electrical resistivity remains largely unexplored. This work presents a comprehensive quantitative description of resistivity distribution at the single filament level and extends the established correlation with the tensile modulus from the scale of fiber bundles to individual filaments. The study is based on extensive experimental characterization of two commercially available CF, PAN‐based HTA40 and pitch‐based YSH‐70A‐10S. Maximum likelihood estimation (MLE) and the Akaike information criterion (AIC) are applied to evaluate the distribution of filament resistivities. The results indicate that the electrical resistivity distributions can be accurately described by either Weibull or Gamma distributions. Furthermore, the correlation between the electrical resistivity and tensile modulus of filaments is analyzed, and the influence of diameter variations on the observed distributions and correlations is discussed.