Identifying cross country skiing techniques using power meters in ski poles

Power meters are becoming a widely used tool for measuring training and racing effort in cycling, and are now spreading also to other sports. This means that increasing volumes of data can be collected from athletes, with the aim of helping coaches and athletes analyse and understanding training load, racing efforts, technique etc. In this project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles. The dataset for this pilot study contained labelled time-series data from three individual skiers using four different gears recorded in varied locations and varied terrain. We systematically evaluated a number of machine learning techniques based on neural networks with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third. To achieve better generalisation to individuals not appearing in the training set more data is required, which is ongoing work.Comment: Presented at the Norwegian Artificial Intelligence Symposium 201

Learning a Physical Activity Classifier for a Low-power Embedded Wrist-located Device

This article presents and evaluates a novel algorithm for learning a physical activity classifier for a low-power embedded wrist-located device. The overall system is designed for real-time execution and it is implemented in the commercial low-power System-on-Chips nRF51 and nRF52. Results were obtained using a database composed of 140 users containing more than 340 hours of labeled raw acceleration data. The final precision achieved for the most important classes, (Rest, Walk, and Run), was of 96%, 94%, and 99% and it generalizes to compound activities such as XC skiing or Housework. We conclude with a benchmarking of the system in terms of memory footprint and power consumption.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Nonmetro Recreation Counties: Their Identification and Rapid Growth

More than 80 percent of the Nation’s 285 million people now reside in metropolitan areas. Many in this vast city and suburban population are attracted to the recreational opportunities and attractions of rural areas, such as beautiful scenery, lakes, mountains, forests, and resorts. For rural communities struggling to offset job losses from farming, mining, and manufacturing, capitalizing on the recreational appeal of an area fosters economic development, attracts new residents, and retains existing population. This article outlines a method to identify nonmetro counties with high recreation development. It then examines the linkage between such development and population change, and considers its implications for the future of rural and small-town America

Identification of Cross-Country Skiing Movement Patterns Using Micro-Sensors

This study investigated the potential of micro-sensors for use in the identification of the main movement patterns used in cross-country skiing. Data were collected from four elite international and four Australian athletes in Europe and in Australia using a MinimaxX™ unit containing accelerometer, gyroscope and GPS sensors. Athletes performed four skating techniques and three classical techniques on snow at moderate velocity. Data from a single micro-sensor unit positioned in the centre of the upper back was sufficient to visually identify cyclical movement patterns for each technique. The general patterns for each technique were identified clearly across all athletes while at the same time distinctive characteristics for individual athletes were observed. Differences in speed, snow condition and gradient of terrain were not controlled in this study and these factors could have an effect on the data patterns. Development of algorithms to process the micro-sensor data into kinematic measurements would provide coaches and scientists with a valuable performance analysis tool. Further research is needed to develop such algorithms and to determine whether the patterns are consistent across a range of different speeds, snow conditions and terrain, and for skiers of differing ability

Downhill Turn Techniques and Performance in Cross-Country Skiing: Associations with Mechanical and Physical Parameters

Svinger i nedoverbakker i langrenn blir gjennomført under varierende forhold. For å prestere godt, altså utnytte potensiell energi og akselererende krefter fra frasparkene på en effektiv måte, må løperne tilpasse inngangshastigheten, linjen gjennom svingen og bruken av teknikk. Målene med denne studien var å karakterisere hovedteknikkene som blir brukt i langrennssvinger i nedoverbakker blant kvinnelige eliteløpere og å undersøke hvordan prestasjonen blir påvirket av teknikkdistribusjon, mekaniske parameter og løpernes maksimale styrke og eksplosivitet

Are You in the Line? RSSI-based Queue Detection in Crowds

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.Comment: This work has been partially funded by the European Union's Horizon 2020 research and innovation programme within the project "Worldwide Interoperability for SEmantics IoT" under grant agreement Number 72315

Women in the 2010 Olympic and Paralympic Games: An Analysis of Participation, Leadership, and Media Opportunities

This report is the third in the series that follows the progress of women in the Olympic and Paralympic movement. The report provides the most accurate, comprehensive, and up-to-date examination of the participation trends among female Olympic and Paralympic athletes and the hiring trends of Olympic and Paralympic governing bodies with respect to the number of women who hold leadership positions in these organizations. The report also looks at newspaper and internet coverage of the 2010 Olympic and Paralympic Winter Games

Compressed Video Action Recognition

Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signal is often drowned in too much irrelevant data. Motivated by that the superfluous information can be reduced by up to two orders of magnitude by video compression (using H.264, HEVC, etc.), we propose to train a deep network directly on the compressed video. This representation has a higher information density, and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times faster than ResNet-152. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.Comment: CVPR 2018 (Selected for spotlight presentation