Evaluation of the Finis Swimsense® and the Garmin Swim™ activity monitors for swimming performance and stroke kinematics analysis.
OBJECTIVE
The study aims were to evaluate the validity of two commercially available swimming activity monitors for quantifying temporal and kinematic swimming variables.


METHODS
Ten national level swimmers (5 male, 5 female; 15.3±1.3years; 164.8±12.9cm; 62.4±11.1kg; 425±66 FINA points) completed a set protocol comprising 1,500m of swimming involving all four competitive swimming strokes. Swimmers wore the Finis Swimsense and the Garmin Swim activity monitors throughout. The devices automatically identified stroke type, swim distance, lap time, stroke count, stroke rate, stroke length and average speed. Video recordings were also obtained and used as a criterion measure to evaluate performance.


RESULTS
A significant positive correlation was found between the monitors and video for the identification of each of the four swim strokes (Garmin: X2 (3) = 31.292, p<0.05; Finis:X2 (3) = 33.004, p<0.05). No significant differences were found for swim distance measurements. Swimming laps performed in the middle of a swimming interval showed no significant difference from the criterion (Garmin: bias -0.065, 95% confidence intervals -3.828-6.920; Finis bias -0.02, 95% confidence intervals -3.095-3.142). However laps performed at the beginning and end of an interval were not as accurately timed. Additionally, a statistical difference was found for stroke count measurements in all but two occasions (p<0.05). These differences affect the accuracy of stroke rate, stroke length and average speed scores reported by the monitors, as all of these are derived from lap times and stroke counts.


CONCLUSIONS
Both monitors were found to operate with a relatively similar performance level and appear suited for recreational use. However, issues with feature detection accuracy may be related to individual variances in stroke technique. It is reasonable to expect that this level of error would increase when the devices are used by recreational swimmers rather than elite swimmers. Further development to improve accuracy of feature detection algorithms, specifically for lap time and stroke count, would also increase their suitability within competitive settings.
Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, Galway, Ireland.|Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, Galway, Ireland.
Robert
R
Mooney
Physiology, School of Medicine, NUI Galway, Galway, Ireland.|CÚRAM (SFI Centre for Research in Medical Devices), NUI Galway, Galway, Ireland.
Leo R
http://orcid.org/0000-0002-4011-6732
LR
Quinlan
Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, Galway, Ireland.|Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, Galway, Ireland.
Gavin
G
Corley
Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.
Alan
A
Godfrey
Department of Exercise & Sports Science, Manchester Metropolitan University. Crewe, United Kingdom.
Conor
C
Osborough
Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, Galway, Ireland.|Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, Galway, Ireland.|CÚRAM (SFI Centre for Research in Medical Devices), NUI Galway, Galway, Ireland.
Gearóid
G
ÓLaighin
D000293:Adolescent; D054874:Athletic Performance; D005260:Female; D006801:Humans; D008297:Male; D055595:Mechanical Phenomena; D013550:Swimming; D014741:Video Recording