Machine Learning Techniques for Gait Analysis in Skiing
Preprint, 2021

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


Moa Johansson

Chalmers, Computer Science and Engineering (Chalmers), Formal methods

Dan Kuylenstierna

Chalmers, Microtechnology and Nanoscience (MC2), Microwave Electronics, Microwave Electronics

David Larsson


Julia Ortheden


Markus Pettersson

Data Science and AI

Areas of Advance

Information and Communication Technology

Subject Categories

Sport and Fitness Sciences

Computer Science

More information

Latest update