This study investigates the accuracy of the tracking system LPM (local position measurement). The goal was to determine detailed error values of the system in the context of sports performance analyses. Six moderately trained male soccer players (amateur level) performed 276 runs on three different courses at six different speeds. Additionally, ten small-sided game plays were carried out. All runs and game plays were recorded with the LPM tracking system and the motion capture system VICON simultaneously. VICON served as the reference system. The absolute error of all LPM position estimations was on average 23.4±20.7 cm. The estimation for average velocities varied between 0.01 km h(-1) and 0.23 km h(-1), the maximum speed estimations differed by up to 2.71 km h(-1). In addition, the results showed that the accuracy of the LPM system is highly dependent on the instantaneous dynamics of the player and decreases in the margins of the observation field. These dependencies were quantified. Considering commonly used applications of position tracking systems in sports (Leser, Ogris, & Baca, 2011), the accuracy of LPM is acceptable for position and velocity estimations. The system provides valuable results for average velocities but seems to be far less reliable when dealing with high dynamic movements and measuring instantaneous velocities.

The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. therefore, we present GaitRec, a comprehensive and completely annotated large-scale dataset containing bilateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bilateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

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This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the "hip", "knee", "ankle", and "calcaneus". Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA) based representations, and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait (N versus GD) and multiclass classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient, have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step toward the automated classification of GDs and a first performance baseline for future developments in this direction.