Peter Blank

Real-Time ECG and EMG Analysis for Biking Using Android-Based Mobile Devices

We developed an application for Android-based mobile devices that enables a real-time calculation of heart rate and cadence for biking. Therefore, both ECG and EMG data are acquired in real time by Shimmer sensors and transmitted via Bluetooth, as well as processed and evaluated on the mobile device. The ECG algorithm is based on the Pan-Tompkins algorithm for QRS-Detection and offers a heart beat detection rate of more than 94%. The EMG algorithm offers a treadle detection rate of more than 91%. The applications range of features is complemented by GPS data for the calculation of speed and location information. It is available for download and can for example be used for controlling the users training status, for live training supervision and for the subsequent analysis of the various training runs.

Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing

Body-worn sensors for movement analysis in swimming have to be unobtrusive and energy-efficient. We present a swimming exercise tracker for the unobtrusive positioning at the back of the head and an energy-efficient analysis using an on-node implementation. To develop the system, we collected head kinematics from 11 subjects in two 200-m medley races comprising breaks, turns, and four swimming styles. Each subject was equipped with a 6-D inertial measurement unit and completed one session in rested and fatigued state. Data were analyzed with a classification system, whereby different classifiers, window sizes, and feature sets were evaluated. Algorithm selection for on-node processing was performed on the basis of classifier accuracy and computational cost. The algorithm with the best tradeoff in accuracy and computational cost was selected and had a classification rate of 85.4%. Energy consumption of both on-node processing and Bluetooth streaming was evaluated on the Shimmer sensor platform. The results revealed energy savings of over 60% when data were processed on the sensor node. The presented analysis approach can be easily applied to other data analysis tasks, and the presented toolchain can support the rapid development of wearable systems in sports and healthcare.

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.

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:

Unobtrusive real-time heart rate variability analysis for the detection of orthostatic dysregulation

The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the users HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinsons Disease. The results of the evaluation showed a recognition rate of 90.0 % for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.

miPod 2: a new hardware platform for embedded real-time processing in sports and fitness applications

In this paper we present the new miPod 2 hardware platform for real-time signal processing and embedded classification of pattern recognition problems in sports and fitness applications. A powerful, energy-friendly and state-of-the-art microcontroller for industry standards, different inertial measurement units, a pulse oximeter, data storages, wireless charging and a Bluetooth Low Energy radio are all integrated into a small and lightweight sensor device. This device is able to capture specified sensor data simultaneously and in addition to execute different complex data processing methods while maintaining high sampling rates and its real-time behavior. The sensor prototype is also easily integrable into different clothes, shoes or sports equipment like balls or rackets to ensure unobtrusive measurements.

Ball speed and spin estimation in table tennis using a racket-mounted inertial sensor

In this paper, we present an approach for ball speed and spin estimation in table tennis based on a single racket-mounted inertial sensor. We conducted a research study comprising eight subjects performing different stroke types resulting in various ball speeds and spins. All ball-racket impacts were recorded with a high-speed camera for further evaluation. Firstly, several assumptions and simplifications of the unknown initial ball properties and the movements of player and racket had to be made. Secondly, the racket blade velocity right after impact was calculated. This was combined with a rebound model between the ball and the rubber to predict the speed and spin of the ball. Overall, the ball speed of strokes with forward spin could be estimated with an accuracy of 79.4% and for strokes with backward spin with an accuracy of 87.4%. The spin was estimated with an accuracy of 73.5% and 75.0%, respectively. To our knowledge, this contribution is the first attempt to estimate characteristics of rebounded balls with a single racket-mounted inertial sensor considering unknown initial conditions and constraints.

Blind path obstacle detector using smartphone camera and line laser emitter

Visually impaired people find navigating within unfamiliar environments challenging. Many smart systems have been proposed to help blind people in these difficult, often dangerous, situations. However, some of them are uncomfortable, difficult to obtain or simply too expensive. In this paper, a low-cost wearable system for visually impaired people was implemented which allows them to detect and locate obstacles in their locality. The proposed system consists of two main hardware components, a laser pointer (

Sensor-based stroke detection and stroke type classification in table tennis

12) and an android smart phone, making our system relatively cheap and accessible. The collision avoidance algorithm uses image processing to measure distances to objects in the environment. This is based on laser light triangulation. This obstacle detection is enhanced by edge detection within the captured image. An additional feature of the system is to recognize and warn the user when stairs are present in the cameras field of view. Obstacles are brought to the users attention using an acoustic signal. Our system was shown to be robust, with only 5 % false alarm rate and a sensitivity of 90 % for 1 cm wide obstacles.