Machine Learning Techniques for Gait Analysis in Skiing
Paper i proceeding, 2022

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.


Savya Sachi Gupta

Student vid Chalmers

Moa Johansson

Chalmers, Data- och informationsteknik, Formella metoder

Dan Kuylenstierna

Chalmers, Mikroteknologi och nanovetenskap, Mikrovågselektronik

David Larsson

Student vid Chalmers

Julia Ortheden

Student vid Chalmers

Markus Pettersson

Chalmers, Data- och informationsteknik, Data Science och AI

Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference


International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage PACSS 2021
New York. NY, USA,


Informations- och kommunikationsteknik



Datavetenskap (datalogi)



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