Deep Recurrent Neural Networks for Human Activity Recognition During Skiing

Pawlyta, Magdalena; Hermansa, Marek; Szczęsna, Agnieszka; Janiak, M.; Wojciechowski, Konrad

doi:10.1007/978-3-030-31964-9_13

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Groh et al [ 22 ] classified freestyle snowboarding tricks using inertial-magnetic measurement unit data and naïve Bayes classifiers. There has been work done to use machine learning techniques to classify alpine skiing activities using IMUs [ 23 , 24 ]. However, the algorithm proposed by Han et al [ 23 ] only classified between different activities, such as riding a chairlift, skiing groomed snow or skiing in slush; it did not classify within a skiing activity itself.…”

Section: Introductionmentioning

confidence: 99%

“…However, the algorithm proposed by Han et al [ 23 ] only classified between different activities, such as riding a chairlift, skiing groomed snow or skiing in slush; it did not classify within a skiing activity itself. Pawlyta et al [ 24 ] focused on the analysis of skiing activity recognition, such as the recognition of the turn, the leg lift, the ski orientation and body position.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units

Neuwirth¹,

Snyder

Kremser³

et al. 2020

Sensors

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In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units

Neuwirth¹,

Snyder

Kremser³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Then, they analyzed the collected data in a supervised manner to predict the status of a skier during winter sports such as alpine skiing and snowboarding [ 13 ]. In [ 14 ] three IMU sensors were attached to the skier’s chest and skis to collect data from several skiers on one slope. This dataset is analyzed using two long short-term memory (LSTM) networks for skiing activity recognition to detect left/right turn, left/right leg lift, ski orientation, and body position.…”

Section: Introductionmentioning

confidence: 99%

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Azadi

Haslgrübler

Anzengruber-Tanase

et al. 2022

Sensors

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Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward’s methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers’ skill level in any condition. Among the studied methods and settings, the best model had 99.25% accuracy.

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