Dan Omcirk
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Dan Omcirk has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- James J. Tufano
Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
2 shared publications - Jan Malecek
Department of Military Physical Education, Faculty of Physical Education and Sport, Charles University in Prague, 162 52 Prague, Czech Republic
2 shared publications - Tomas Vetrovsky
Charles University
2 shared publications - Jan Padecky
Faculty of Physical Education and Sport, Charles University, 16252 Prague, Czech Republic
1 shared publication - K Sykora
Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
1 shared publication - Lubomir Privetivy
Military Department, Charles University in Prague, Faculty of Physical Education and Sport, 162 52 Prague, Czech Republic, Czech Republic
1 shared publication
Latest Publications
- Open Access
- 20 June 2023
- Published by BMJ in BMJ Military Health
- Vol. 170 (e2) , e192-e193
Test-retest reliability of two different laser-based protocols to assess handgun shooting accuracy in military personnel
- 20 June 2023
- Published by BMJ in BMJ Military Health
- Vol. 170 (e2) , e192-e193
Open AccessTest-retest reliability of two different laser-based protocols to assess handgun shooting accuracy in military personnel
- Open Access
Punch Trackers: Correct Recognition Depends on Punch Type and Training Experience
To determine the ability of different punch trackers (PT) (Corner (CPT), Everlast (EPT), and Hykso (HPT)) to recognize specific punch types (lead and rear straight punches, lead and rear hooks, and lead and rear uppercuts) thrown by trained (TR, n = 10) and untrained punchers (UNTR, n = 11), subjects performed different punch combinations, and PT data were compared to data from video recordings to determine how well each PT recognized the punches that were actually thrown. Descriptive statistics and multilevel modelling were used to analyze the data. The CPT, EPT and HPT detected punches more accurately in TR than UNTR, evidenced by a lower percentage error in TR (p = 0.007). The CPT, EPT, and HPT detected straight punches better than uppercuts and hooks, with a lower percentage error for straight punches (p < 0.001). The recognition of punches with CPT and HPT depended on punch order, with earlier punches in a sequence recognized better. The same may or may not have occurred with EPT, but EPT does not allow for data to be exported, meaning the order of individual punches could not be analyzed. The CPT and HPT both seem to be viable options for tracking punch count and punch type in TR and UNTR.
Open AccessPunch Trackers: Correct Recognition Depends on Punch Type and Training Experience
To determine the ability of different punch trackers (PT) (Corner (CPT), Everlast (EPT), and Hykso (HPT)) to recognize specific punch types (lead and rear straight punches, lead and rear hooks, and lead and rear uppercuts) thrown by trained (TR, n = 10) and untrained punchers (UNTR, n = 11), subjects performed different punch combinations, and PT data were compared to data from video recordings to determine how well each PT recognized the punches that were actually thrown. Descriptive statistics and multilevel modelling were used to analyze the data. The CPT, EPT and HPT detected punches more accurately in TR than UNTR, evidenced by a lower percentage error in TR (p = 0.007). The CPT, EPT, and HPT detected straight punches better than uppercuts and hooks, with a lower percentage error for straight punches (p < 0.001). The recognition of punches with CPT and HPT depended on punch order, with earlier punches in a sequence recognized better. The same may or may not have occurred with EPT, but EPT does not allow for data to be exported, meaning the order of individual punches could not be analyzed. The CPT and HPT both seem to be viable options for tracking punch count and punch type in TR and UNTR.