In:
Sports Engineering, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2021-12)
Abstract:
Speed skating takes place on ice rinks and is, therefore, dependent on seasonal conditions. To be able to train all year round, training in the summer months, when no ice rinks are available, consists mainly of athletics and endurance training as well as imitation drills. Imitation drills are exercises, e.g. on a slide board, which imitate the actual skating movement. To objectively evaluate the quality of the execution of these exercises, key performance indicators such as push-off forces need to be quantified. The aim of this work was to determine the push-off forces during speed skating imitation drills using pressure insoles in combination with machine-learning methods. A slide board is usually not instrumented. Here, the slide board was equipped with force plates to record the target variables, i.e. the push-off forces. The input variables to determine the push-off forces were recorded using plantar pressure insoles and triaxial accelerometers. Seven participants took part in the study. Two different machine-learning algorithms were compared. A non-linear deep neural network model and a linear multiple variable regression model. The models were trained using the obtained force–time curves. The linear regression model proved sufficient to predict the push-off forces. The relative difference between the measured and modelled maximum push-off force remained below 5%. This approach, based on a mobile and low-cost measurement system, allows a quantitative analysis of the athlete’s technique/performance. Therefore, we expect the instrument to be a helpful tool for the training of speed skaters.
Type of Medium:
Online Resource
ISSN:
1369-7072
,
1460-2687
DOI:
10.1007/s12283-021-00362-1
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2021
detail.hit.zdb_id:
2020956-3
SSG:
31
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