Benjamin H. Groh

Activity recognition in beach volleyball using a Deep Convolutional Neural Network

Many injuries in sports are caused by overuse. These injuries are a major cause for reduced performance of professional and non-professional beach volleyball players. Monitoring of player actions could help identifying and understanding risk factors and prevent such injuries. Currently, time-consuming video examination is the only option for detailed player monitoring in beach volleyball. The lack of a reliable automatic monitoring system impedes investigations about the risk factors of overuse injuries. In this work, we present an unobtrusive automatic monitoring system for beach volleyball based on wearable sensors. We investigate the possibilities of Deep Learning in this context by designing a Deep Convolutional Neural Network for sensor-based activity classification. The performance of this new approach is compared to five common classification algorithms. With our Deep Convolutional Neural Network, we achieve a classification accuracy of 83.2%, thereby outperforming the other classification algorithms by 16.0%. Our results show that detailed player monitoring in beach volleyball using wearable sensors is feasible. The substantial performance margin between established methods and our Deep Neural Network indicates that Deep Learning has the potential to extend the boundaries of sensor-based activity recognition.

Wearable trick classification in freestyle snowboarding

Digital motion analysis in freestyle snowboarding requires a stable trick detection and accurate classification. Freestyle snowboarding contains several trick categories that all have to be recognized for an application in training sessions or competitions. While previous work already addressed the classification of specific tricks or turns, there is no known method that contains a full pipeline for detection and classification of tricks from multiple categories. In this paper, we suggest a classification pipeline containing the detection, categorization and classification of tricks of two major freestyle trick categories. We evaluated our algorithm based on data from two different acquisitions with a total number of eleven athletes and 275 trick events. Tricks of both categories were categorized with recall results of 96.6% and 97.4%. The classification of the tricks was evaluated to an accuracy of 90.3 % for the first and 93.3% for the second category.

Classification and visualization of skateboard tricks using wearable sensors

The application of wearables and customized signal processing methods offers new opportunities for motion analysis and visualization in skateboarding. In this work, we propose an automatic trick analysis and visualization application based on inertialmagnetic data. Skateboard tricks are detected and classified in real-time and visualized by means of an animated 3D-graphic. We achieved a trick detection recall of 96.4%, a classification accuracy of 89.1% (considering correctly performed tricks) and an error of the board orientation visualization of 2.21.9. The system is extendable in its application and can be incorporated as support for skateboard training and competitions.

Workshop on wearables for sports

Wearables are becoming mainstream technology, however there is still room for improvement in the sports domain of this field. Monitoring performance and collecting large scale data are of high interest among athletes - amateurs and professionals alike. The current state-of-the art wearable solutions for sports analysis are able to provide individual statistics to the user, however they have shortcomings in certain aspects, such as isolating and visualizing important information for the user, beyond statistics. This workshop focuses on the application of wearable technology in sports. We will explore novel ideas and application scenarios of how sensors and actuators are capable of supporting athletes in monitoring and improving their performance. We will discuss the design space of the domain by bringing together experts from various communities and exchanging ideas from different perspectives on wearables for sports applications. Participants will collaboratively produce sports related prototype applications.

Blood glucose level prediction based on support vector regression using mobile platforms

The correct treatment of diabetes is vital to a patients health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human bodys response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

IMU-based pose determination of scuba divers' bodies and shanks

A simple method for an underwater pose determination of scuba divers can provide a deeper insight in the biomechanics of scuba diving and thereby improve education and training systems. In this work, we present an inertial sensor-based approach for the pose determination of the upper body and the shank orientation during fin kicks. Accelerometer measurements of gravity and a gyroscope-based method are used to determine absolute body angles in reference to the ground and the angular change of the shanks during fin kicks. The proposed algorithms were evaluated with data acquired from ten divers and a camera-based gold standard. The results were analyzed to a mean error of 0° with a standard deviation of 10° for the upper body pose determination. The absolute angle of the shanks at the turning points between fin kicks was determined with an error of 0° ± 11°, the relative shank angle with an error of 0° ± 8°.

Movement prediction in rowing using a Dynamic Time Warping based stroke detection

In professional rowing competitions, sensor data is transmitted from an on-board sensor unit on the boat to an external computer system. This system calculates the current position of each boat in real-time. However, incomplete localizations occur as a result of radio transmission outages. This paper introduces an algorithm to overcome transmission outages by predicting the rowing movement. The prediction algorithm is based on accelerometer and GPS data that is provided by the on-board unit before an outage occurs. It uses Subsequence Dynamic Time Warping (subDTW) to detect the rowing strokes in the acceleration signal. Knowing the previous strokes, the system predicts the upcoming strokes, as the rowing motion follows a periodic pattern. Thereby, the GPS measured velocity can be extrapolated and the position is predicted. A further outcome of the subDTW stroke detection is an accurate determination of the rowing stroke rate. In our experiment, we evaluate the rowing stroke detection and stroke rate determination based on subDTW as well as the prediction algorithm for simulated outages of professional race data. It shows a subDTW stroke signal detection of 100% after the start phase of the race. The prediction in case of a sensor outage of 5 seconds leads to a correlation between the predicted velocity and the actual velocity of 0.96 and a resulting position error (RMSE) of 0.3 m.

Wearable real-time ecg monitoring with emergency alert system for scuba diving.

Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a divers ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system. After performing a predefined study protocol, the algorithms accuracy was evaluated with 10 subjects in a dry environment and with 5 subjects in an underwater environment. The results showed that, in 3 out of 5 dives as well as in dry environment, data transmission remained stable. In these cases, the subjects were able to trigger the alert system. The evaluated data showed a clear ECG signal with a QRS detection accuracy of 90 %. Thus, the proposed framework has the potential to detect and to warn of health risks. Further developments of this sample concept can imply an extension for monitoring different biomedical parameters.

International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW)

Introduction ObesiTIC is a project which aims to investigate innovative information and communication technologies resulting in a new ICT tool specifically designed for children and teenagers, in order to acquire healthy lifestyles, promoting physical activity and avoiding health and social problems associated with obesity and overweight. This is achieved through its co-design and validation with children and teens following a Living Lab approach through SPORTIS Living Lab, a European Network of Living Lab’s effective member. Objectives 1. To develop an innovative solution that would enable healthrelated behaviour changes, increase motivation, promote physical activity and reduce prolonged sedentary time in users, thanks to persuasive and ubiquitous computing techniques. 2. To be validated by SPORTIS Living Lab. Following SPORTIS aim to involve society in the innovation process, ObesiTIC will be validated by end-users (children and teenagers) combined with the development of the application and final product, in order to suit and respect all the needs and aspects of the users’ requirements. Methods A Living Lab methodology is implemented:

Automated Ski Velocity and Jump Length Determination in Ski Jumping Based on Unobtrusive and Wearable Sensors

Although ski jumping is a widely investigated sport, competitions and training sessions are rarely supported by state-of-the-art technology. Supporting technologies could focus on a continuous velocity determination and visualization for competitions as well as on an analysis of the velocity development and the jump length for training sessions. In the literature, there are several approaches for jump analysis. However, the majority of these approaches aim for a biomechanical analysis instead of a support system for frequent use. They do not fulfill the requirements of unobtrusiveness and usability that are necessary for a long-term application in competitions and training. In this paper, we propose an algorithm for ski velocity calculation and jump length determination based on the processing of unobtrusively obtained ski jumping data. Our algorithm is evaluated with data from eleven athletes in two different acquisitions. The results show an error of the velocity measurement at take-off of (which equals -3.0 % ± 4.7 % in reference to the estimated average take-off velocity) compared to a light barrier system. The error of the jump length compared to a video-based system is 0.8 m ± 2.9 m (which equals 0.9 % ± 3.4 % of the average jump length of the training jumps in this work). Although our proposed system does not outperform existing camera-based methods of jump length measurements at competitions, it provides an affordable and unobtrusive support for competitions and has the potential to simplify analyses in standard training.