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

Machine Learning Techniques for Gait Analysis in Skiing

We investigate the use of supervised machine learning on data from ski-poles equipped with force sensors, with the goal of auto- matically identifying which sub-technique the skier is using. Our first contribution is a demonstration that sub-technique identification can be done with high accuracy using only sensors in the pole. Secondly, we also compare different machine learning algorithms (LSTM neural networks and random forests) and highlight their respective strengths and weaknesses, providing practitioners working with sports data some guidance for choice of machine learning algorithms

Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks

Spartan Daily, December 4, 1970

Volume 58, Issue 47https://scholarworks.sjsu.edu/spartandaily/5337/thumbnail.jp

Nathan D. Hale v. Boyne USA, Inc. : Brief of Appellant

APPEAL FROM THE DISTRICT COURT OF THE THIRD JUDICIAL DISTRICT IN AND FOR SALT LAKE COUNTY HONORABLE JAMES S. SAWAYA PRESIDIN

IMU Data-based Recognition for Sports Exercises: An Enhanced Distance Optimization Approach for Repetition Counting ​Across Activities

In the field of human activity recognition (HAR), inertial measurement units (IMUs) are a commonly used method to record movement patterns. In the study presented in this paper, IMU captured seven different sports exercises performed by 21 participants. In the preliminary data analysis phase, an exercise classification was conducted using the Long Short Term Memory (LSTM) Network and Temporal Convolutional Network (TCN). The LSTM achieved an accuracy rate of 94.2 % for training and 90.8 % for testing. Similarly, the TCN demonstrated rates of 95.5 % for training and 91.6 % for testing. The subsequent stage was centered on quantifying the number of completed repetitions. A distance value was derived which showed promising results for exercise-independent counting without the need for manual feature selection. For further improvement, a range-to-mean ratio of the standard deviation was calculated and used for feature selection. Combined with a local extrema analysis of the modified distance values the accuracy of counting repetitions was significantly improved, especially for exercises that show irregularities in the signal course

Class of 1970 & 1971 – 50th Reunion Golden Granite

https://scholars.unh.edu/granite_yearbook/1107/thumbnail.jp

The New Hampshire, Vol. 68, No. 29 (Jan. 27, 1978)

The student publication of the University of New Hampshire