xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning.

With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis' orientation, and metadata such as wind, starting gate, and ski jumping hill data.

Enhancement of Speed and Accuracy Trade-Off for Sports Ball Detection in Videos-Finding Fast Moving, Small Objects in Real Time.

The detection and localization of the ball in sport videos is crucial to better understand events and actions occurring in those sports. Despite recent advances in the field of object detection, the automatic detection of balls remains a challenging task due to the unsteady nature of balls in images. In this paper, we address the detection of small, fast-moving balls in sport video data and introduce a real-time ball detection approach based on the YOLOv3 object detection model.

Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase.

Methods: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring.

Wearable Real-Time Skateboard Trick Visualization and Its Community Perception.

Motion visualization is an attractive way to provide support for a range of recreational and competitive sports. In skateboarding, sensor technology in particular can help visualization systems capture the motion of athletes to provide relevant information to athletes, judges, and spectators. This article describes the authors' proposed application of a 9D inertial-magnetic measurement unit (IMMU) based real-time trick classification and visualization system.

Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks.

Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks.

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy.

Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days.

A robust Kalman framework with resampling and optimal smoothing.

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time.