Support vector machines can classify runner’s ability using wearable sensor data from a variety of anatomical locations

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We developed and tested an algorithm to automatically classify twenty runners as novice or experienced based on their technique. Linear accelerations and angular velocities collected from six common wearable sensor locations were used to train support vector machine classifiers. The model using input data from all six sensors achieved a classification accuracy of 98.5% (10 km/h running). The classification performance of models based on single sensor data showed a 56.3-94.5% accuracy range, with sensors from the upper body giving the best results. Comparisons of kinematic variables between the two populations confirmed significant differences in upper body biomechanics throughout the stride, thus showing applied potential when aiming to compare novice runner’s technique with movement patterns more akin to those with greater experience.
Original languageEnglish
Title of host publicationISBS Proceedings Archives
Subtitle of host publication39th International Conference on Biomechanics in Sports (2021) Canberra, Australia, Sept 3-7, 2021
PublisherInternational Society of Biomechanics in Sports (ISBS)
Number of pages4
Publication statusAcceptance date - 23 Jun 2021
EventInternational Conference on Biomechanics in Sports - Canberra, Australia
Duration: 3 Sep 20217 Sep 2021
Conference number: 39th


ConferenceInternational Conference on Biomechanics in Sports
Abbreviated titleISBS 2021
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