A wireless sensor system for monitoring the performance of a swimmer’s tumble turn

Abstract

A wireless sensor system has been used to calculate measurable performance parameters throughout a swimmer’s tumble turn. The parameters to be measured have been specified by the users (coaches, biomechanists and swimmers) of the system. The findings suggest that the wireless sensor network can be used to determine the approach time, contact time, glide time, kick time and stroke time to within 0.07 ± 0.16 s of the results obtained using manual digitisation of video images obtained from an underwater camera. It is concluded that it is possible to determine the swimmer’s time, orientation and velocity throughout the turn using the implemented inertial navigation system.

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

“…Researchers at Loughborough University went on to describe a method by which these different phases of the frontcrawl turn can be extracted from accelerometry signals [ 41 , 45 , 68 , 69 , 132 ]. The accelerometer was positioned and orientated in a similar manner to Lee, Leadbetter, Ohgi, Theil, Burkett and James [ 56 ] ( Figure 27 ).…”

“…This algorithm advanced the examination of turns using sensor based systems as a temporal analysis of the different phases of a turn was now possible, albeit without the corresponding distance measurements. Variables such as time to rotation, wall contact time, glide time and stroke initiation time were measured with a high degree of accuracy, with an average difference from criterion measures of under 0.15 s [ 132 ]. Lacking from these works however is an examination of the features for other turn styles for the remaining swimming strokes, and with large groups of swimmers, as well as a lack of feature extraction methodologies to determine relevant parameters such as speed or distance.…”

Assessing wireless inertia measurement units for monitoring athletics sprint performance

“…Positional accuracy determined from accelerometers is known to be limited by integration errors. However previous research [10] and [11] has shown it possible to correct for integration drift by using knowledge about when the IMU is stationary. The positive results suggest that with an increase in sampling rate and removal of the systematic errors within the data the IMU could be used to assist athletes and coaches in their pursuit to optimise performance.…”

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

“…For non-cyclic tasks, temporal parameters were identified to measure critical temporal events (blade–puck contact time in ice hockey [ 206 , 309 ], cricket bowling [ 227 ] and, more specifically, ball release [ 288 ]), or detect task phases and critical events (in ski jumping [ 90 , 91 ], half-pipe snowboard [ 159 ], bowling [ 180 ], baseball swing [ 146 , 179 ], instep kick [ 228 ], karate front kick [ 273 ], diving trampoline jumps [ 161 ], artistic gymnastics springboard jumps [ 187 ], golf [ 171 ], javelin throw [ 270 ], soccer turning manoeuvres [ 241 ], swimming tumble turn [ 192 , 197 , 285 ] and start [ 193 ] and cricket bowling [ 264 ]).…”

“…Subject specific models were also developed to estimate CoM velocity during running, based on step rate measures [ 242 ]. The errors entailed in instantaneous velocity estimation can be partially overcome by limiting the velocity analysis to average values in cyclic sports, for example, for each swimming stroke [ 104 , 105 , 106 , 108 , 290 ] turning action [ 192 ], lane [ 63 , 80 , 323 ], running cycle [ 162 , 328 ], or mean velocity in running [ 56 , 160 ]. The average velocity of progression was also obtained from ski IMUs, removing drift and assuming zero-velocity during a portion of the ski thrust phase, in cross-country skiing [ 120 , 237 ] and in uphill mountaineering [ 121 ].…”