From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities

Genetic Evidence for Involvement of Neuronally Expressed S1P1 Receptor in Nociceptor Sensitization and Inflammatory Pain

Sphingosine-1-phosphate (S1P) is a key regulator of immune response. Immune cells, epithelia and blood cells generate high levels of S1P in inflamed tissue. However, it is not known if S1P acts on the endings of nociceptive neurons, thereby contributing to the generation of inflammatory pain. We found that the S1P1 receptor for S1P is expressed in subpopulations of sensory neurons including nociceptors. Both S1P and agonists at the S1P1 receptor induced hypersensitivity to noxious thermal stimulation in vitro and in vivo. S1P-induced hypersensitivity was strongly attenuated in mice lacking TRPV1 channels. S1P and inflammation-induced hypersensitivity was significantly reduced in mice with a conditional nociceptor-specific deletion of the S1P1 receptor. Our data show that neuronally expressed S1P1 receptors play a significant role in regulating nociceptor function and that S1P/S1P1 signaling may be a key player in the onset of thermal hypersensitivity and hyperalgesia associated with inflammation