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An Inertial Sensor-Based Method for Estimating the Athlete's Relative Joint Center Positions and Center of Mass Kinematics in Alpine Ski Racing

Overview of attention for article published in Frontiers in Physiology, November 2017
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Title
An Inertial Sensor-Based Method for Estimating the Athlete's Relative Joint Center Positions and Center of Mass Kinematics in Alpine Ski Racing
Published in
Frontiers in Physiology, November 2017
DOI 10.3389/fphys.2017.00850
Pubmed ID
Authors

Benedikt Fasel, Jörg Spörri, Pascal Schütz, Silvio Lorenzetti, Kamiar Aminian

Abstract

For the purpose of gaining a deeper understanding of the relationship between external training load and health in competitive alpine skiing, an accurate and precise estimation of the athlete's kinematics is an essential methodological prerequisite. This study proposes an inertial sensor-based method to estimate the athlete's relative joint center positions and center of mass (CoM) kinematics in alpine skiing. Eleven inertial sensors were fixed to the lower and upper limbs, trunk, and head. The relative positions of the ankle, knee, hip, shoulder, elbow, and wrist joint centers, as well as the athlete's CoM kinematics were validated against a marker-based optoelectronic motion capture system during indoor carpet skiing. For all joints centers analyzed, position accuracy (mean error) was below 110 mm and precision (error standard deviation) was below 30 mm. CoM position accuracy and precision were 25.7 and 6.7 mm, respectively. Both the accuracy and precision of the system to estimate the distance between the ankle of the outside leg and CoM (measure quantifying the skier's overall vertical motion) were found to be below 11 mm. Some poorer accuracy and precision values (below 77 mm) were observed for the athlete's fore-aft position (i.e., the projection of the outer ankle-CoM vector onto the line corresponding to the projection of ski's longitudinal axis on the snow surface). In addition, the system was found to be sensitive enough to distinguish between different types of turns (wide/narrow). Thus, the method proposed in this paper may also provide a useful, pervasive way to monitor and control adverse external loading patterns that occur during regular on-snow training. Moreover, as demonstrated earlier, such an approach might have a certain potential to quantify competition time, movement repetitions and/or the accelerations acting on the different segments of the human body. However, prior to getting feasible for applications in daily training, future studies should primarily focus on a simplification of the sensor setup, as well as a fusion with global navigation satellite systems (i.e., the estimation of the absolute joint and CoM positions).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 153 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 17%
Student > Master 19 12%
Student > Bachelor 18 12%
Researcher 15 10%
Student > Doctoral Student 8 5%
Other 18 12%
Unknown 49 32%
Readers by discipline Count As %
Engineering 32 21%
Sports and Recreations 26 17%
Nursing and Health Professions 7 5%
Computer Science 5 3%
Medicine and Dentistry 4 3%
Other 16 10%
Unknown 63 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2017.
All research outputs
#20,451,228
of 23,007,053 outputs
Outputs from Frontiers in Physiology
#9,477
of 13,760 outputs
Outputs of similar age
#286,858
of 329,170 outputs
Outputs of similar age from Frontiers in Physiology
#237
of 347 outputs
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So far Altmetric has tracked 13,760 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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