Prediction of active peak force using a multilayer perceptron
Niemelä, M., Kulmala, J.-P., Kauppi, J.-P., Kosonen, J., & Äyrämö, S. (2017). Prediction of active peak force using a multilayer perceptron. Sports Engineering, 20(3), 213-219. https://doi.org/10.1007/s12283-017-0236-z
Published inSports Engineering
© International Sports Engineering Association 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and complexity of the analysis. In this study, the machine learning model for vertical GRF magnitude prediction based on running motion information of 128 healthy adults is proposed. The predicted outputs of a multilayer perceptron model were compared with the actual force platform measurements. The results were evaluated with Pearson’s correlation coefficient through a tenfold cross validation. The mean standard error of the estimate was 0.107 body weights showing that our method is sufficiently accurate to identify abnormalities in running technique among recreational runners. ...
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity Girka, Anastasiia; Kulmala, Juha-Pekka; Äyrämö, Sami (Taylor & Francis, 2020)Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a ...
The more you move, the more action you construct : a motion capture study on head and upper-torso movements in constructed action in Finnish Sign Language narratives Jantunen, Tommi; De Weerdt, Danny; Burger, Birgitta; Puupponen, Anna (John Benjamins Publishing Company, 2021)This paper investigates, with the help of motion capture data processed on corpus principles, the characteristics of head and upper-torso movements in constructed action and regular narration (i.e., signing without constructed ...
Cronin, Neil J. (Elsevier BV, 2021)Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such ...
Ground reaction forces, neuromuscular and metabolic responses to combined strength and endurance loading in recreational endurance athletes Sorvisto, Juha (2015)Among recreational and elite endurance athletes strength and endurance loadings are often performed concurrently to improve neuromuscular capacity in order to enhance running economy and maximal running velocity (i.e. ...
What Comes First : Combining Motion Capture and Eye Tracking Data to Study the Order of Articulators in Constructed Action in Sign Language Narratives Jantunen, Tommi; Puupponen, Anna; Burger, Birgitta (European Language Resources Association, 2020)We use synchronized 120 fps motion capture and 50 fps eye tracking data from two native signers to investigate the temporal order in which the dominant hand, the head, the chest and the eyes start producing overt constructed ...