Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 |
| Seiten | 512-515 |
| Seitenumfang | 4 |
| Publikationsstatus | Veröffentlicht - 2014 |
Publikationsreihe
| Name | IEEE Workshop on Statistical Signal Processing Proceedings |
|---|
Abstract
In sport activities and rehabilitation it is of great benefit to provide an autonomous and individual assessment system of motor activity. Providing quantitive data about the movement is not only valid for characterising the courses of training processes, it will also help the athlete to improve the technique or to enhance learning in motor rehabilitation. In this regard, significant features are required to monitor and score the movement-pattern. We analyzed different rowing parameters including grip pull out, grip force, slinding seat position and foot-rest force to derive relevant rowing features. An indoor rower was used to record several movement parameters of elite and novice athletes. Different features were extracted for each rowing parameter by assessing the difference of each feature value on elite and novice athletes. The most relevant features as well as rowing parameters were selected based on the signal-to-noise-ratio ranking. The efficiency of Naive Bayes classifier to differentiate the rowing activity between elite and novice athletes was investigated. According to the results five features are sufficient to achieve a high classification rate of 100%. Using the proposed feature set a grading system was introduced enabling to rank and score the quality related to motor activity in rowing.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Mathematik (insg.)
- Angewandte Mathematik
- Informatik (insg.)
- Signalverarbeitung
- Informatik (insg.)
- Angewandte Informatik
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2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. 2014. S. 512-515 6884688 (IEEE Workshop on Statistical Signal Processing Proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On the efficiency of an autonomous cyclic motion grading system
AU - Moghaddamnia, S.
AU - Peissig, J.
AU - Schmitz, G.
AU - Effenberg, A.O.
N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - In sport activities and rehabilitation it is of great benefit to provide an autonomous and individual assessment system of motor activity. Providing quantitive data about the movement is not only valid for characterising the courses of training processes, it will also help the athlete to improve the technique or to enhance learning in motor rehabilitation. In this regard, significant features are required to monitor and score the movement-pattern. We analyzed different rowing parameters including grip pull out, grip force, slinding seat position and foot-rest force to derive relevant rowing features. An indoor rower was used to record several movement parameters of elite and novice athletes. Different features were extracted for each rowing parameter by assessing the difference of each feature value on elite and novice athletes. The most relevant features as well as rowing parameters were selected based on the signal-to-noise-ratio ranking. The efficiency of Naive Bayes classifier to differentiate the rowing activity between elite and novice athletes was investigated. According to the results five features are sufficient to achieve a high classification rate of 100%. Using the proposed feature set a grading system was introduced enabling to rank and score the quality related to motor activity in rowing.
AB - In sport activities and rehabilitation it is of great benefit to provide an autonomous and individual assessment system of motor activity. Providing quantitive data about the movement is not only valid for characterising the courses of training processes, it will also help the athlete to improve the technique or to enhance learning in motor rehabilitation. In this regard, significant features are required to monitor and score the movement-pattern. We analyzed different rowing parameters including grip pull out, grip force, slinding seat position and foot-rest force to derive relevant rowing features. An indoor rower was used to record several movement parameters of elite and novice athletes. Different features were extracted for each rowing parameter by assessing the difference of each feature value on elite and novice athletes. The most relevant features as well as rowing parameters were selected based on the signal-to-noise-ratio ranking. The efficiency of Naive Bayes classifier to differentiate the rowing activity between elite and novice athletes was investigated. According to the results five features are sufficient to achieve a high classification rate of 100%. Using the proposed feature set a grading system was introduced enabling to rank and score the quality related to motor activity in rowing.
KW - Statistical signal analysis
KW - classification
KW - feature extraction
KW - grading scheme and rowing
UR - http://www.scopus.com/inward/record.url?scp=84907415189&partnerID=8YFLogxK
U2 - 10.1109/ssp.2014.6884688
DO - 10.1109/ssp.2014.6884688
M3 - Conference contribution
SN - 9781479949755
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 512
EP - 515
BT - 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
ER -