Behavioral Dynamics in Swimming: The Appropriate Use of Inertial Measurement Units

Motor control in swimming can be analyzed using low- and high-order parameters of behavior. Low-order parameters generally refer to the superficial aspects of movement (i.e., position, velocity, acceleration), whereas high-order parameters capture the dynamics of movement coordination. To assess human aquatic behavior, both types have usually been investigated with multi-camera systems, as they offer high three-dimensional spatial accuracy. Research in ecological dynamics has shown that movement system variability can be viewed as a functional property of skilled performers, helping them adapt their movements to the surrounding constraints. Yet to determine the variability of swimming behavior, a large number of stroke cycles (i.e., inter-cyclic variability) has to be analyzed, which is impossible with camera-based systems as they simply record behaviors over restricted volumes of water. Inertial measurement units (IMUs) were designed to explore the parameters and variability of coordination dynamics. These light, transportable and easy-to-use devices offer new perspectives for swimming research because they can record low- to high-order behavioral parameters over long periods. We first review how the low-order behavioral parameters (i.e., speed, stroke length, stroke rate) of human aquatic locomotion and their variability can be assessed using IMUs. We then review the way high-order parameters are assessed and the adaptive role of movement and coordination variability in swimming. We give special focus to the circumstances in which determining the variability between stroke cycles provides insight into how behavior oscillates between stable and flexible states to functionally respond to environmental and task constraints. The last section of the review is dedicated to practical recommendations for coaches on using IMUs to monitor swimming performance. We therefore highlight the need for rigor in dealing with these sensors appropriately in water. We explain the fundamental and mandatory steps to follow for accurate results with IMUs, from data acquisition (e.g., waterproofing procedures) to interpretation (e.g., drift correction).

[1]  Julien Favre,et al.  Automatic measurement of key ski jumping phases and temporal events with a wearable system , 2012, Journal of sports sciences.

[2]  K Aminian,et al.  Front-crawl stroke descriptors variability assessment for skill characterisation , 2016, Journal of sports sciences.

[3]  Kamiar Aminian,et al.  Approaching on-line estimation of swimming instantaneous velocity using a wearable IMU , 2014 .

[4]  David V. Thiel,et al.  Towards determining absolute velocity of freestyle swimming using 3-axis accelerometers , 2011 .

[5]  Neil Davey,et al.  Swimming stroke analysis using multiple accelerometer devices and tethered systems , 2008 .

[6]  Kamiar Aminian,et al.  Gaussian process framework for pervasive estimation of swimming velocity with body-worn IMU , 2013 .

[7]  G. Mavromatis,et al.  Hand orientation in hand paddle swimming. , 2008, International Journal of Sports Medicine.

[8]  E. Brookner Tracking and Kalman Filtering Made Easy , 1998 .

[9]  D J Wilson,et al.  Accuracy of digitization using automated and manual methods. , 1999, Physical therapy.

[10]  Jonathan Wheat,et al.  Measuring coordination and variability in coordination , 2006 .

[11]  Barry D. Wilson,et al.  System for determining within-stroke variations of speed in swimming (SWiSS) , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[12]  João Paulo Vilas-Boas,et al.  Individual profiles of spatio-temporal coordination in high intensity swimming. , 2012, Human movement science.

[13]  David V. Thiel,et al.  Investigating Forward Velocity and Symmetry in Freestyle Swimming Using Inertial Sensors. , 2015 .

[14]  Brendan Burkett,et al.  Quantifying freestyle kick-count and kick-rate patterns in Paralympic swimming , 2009, Journal of sports sciences.

[15]  Keith Davids,et al.  Coordination Pattern Variability Provides Functional Adaptations to Constraints in Swimming Performance , 2014, Sports Medicine.

[16]  J. Hamill,et al.  Adaptations in interlimb and intralimb coordination to asymmetrical loading in human walking. , 2006, Gait & posture.

[17]  Roman Kamnik,et al.  An inertial and magnetic sensor based technique for joint angle measurement. , 2007, Journal of biomechanics.

[18]  Kamiar Aminian,et al.  A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU , 2015, Pervasive Mob. Comput..

[19]  R. Bartlett,et al.  Movement systems as dynamical systems : The functional role of variability and its implications for sports medicine , 2003 .

[20]  K M Newell,et al.  Learning the Pedalo Locomotion Task , 2005, Journal of motor behavior.

[21]  Chikara Miyaji,et al.  Stroke phase discrimination in breaststroke swimming using a tri-axial acceleration sensor device , 2003 .

[22]  Romain Hérault,et al.  Pattern Recognition in Cyclic and Discrete Skills Performance from Inertial Measurement Units , 2014 .

[23]  T. Le Sage,et al.  Embedded programming and real-time signal processing of swimming strokes , 2011 .

[24]  A. H. Rouard,et al.  Relative contribution of arms and legs in humans to propulsion in 25-m sprint front-crawl swimming , 1999, European Journal of Applied Physiology and Occupational Physiology.

[25]  Kamiar Aminian,et al.  Front-Crawl Instantaneous Velocity Estimation Using a Wearable Inertial Measurement Unit , 2012, Sensors.

[26]  Pietro Garofalo,et al.  First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors , 2009, Medical & Biological Engineering & Computing.

[27]  Björn Eskofier,et al.  Classification of kinematic swimming data with emphasis on resource consumption , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[28]  Andrew A. West,et al.  A Multi-sensor System for Monitoring the Performance of Elite Swimmers , 2010, ICETE.

[29]  David V. Thiel,et al.  Determining maximum push-off velocity in swimming using accelerometers , 2013 .

[30]  K. Davids,et al.  The ecological dynamics of decision making in sport , 2006 .

[31]  Giuseppe Vannozzi,et al.  Wearable inertial sensors in swimming motion analysis: a systematic review , 2015, Journal of sports sciences.

[32]  Kelly de Jesus,et al.  Reconstruction Accuracy Assessment of Surface and Underwater 3D Motion Analysis: A New Approach , 2015, Comput. Math. Methods Medicine.

[33]  G. M,et al.  Motor Development in Children : Aspects of Coordination and Control , 2011 .

[34]  Guang-Zhong Yang,et al.  Swimming Stroke Kinematic Analysis with BSN , 2010, 2010 International Conference on Body Sensor Networks.

[35]  David V. Thiel,et al.  Quantifying and assessing biomechanical differences in swim turn using wearable sensors , 2011 .

[36]  Qingguo Li,et al.  Inertial Sensor-Based Methods in Walking Speed Estimation: A Systematic Review , 2012, Sensors.

[37]  B M Jolles,et al.  Functional calibration procedure for 3D knee joint angle description using inertial sensors. , 2009, Journal of biomechanics.

[38]  K. Aminian,et al.  Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement , 2006 .

[39]  William J. McDermott,et al.  Issues in Quantifying Variability From a Dynamical Systems Perspective , 2000 .

[40]  David V. Thiel,et al.  Velocity profiling using inertial sensors for freestyle swimming , 2013 .

[41]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. , 2004, Gait & posture.

[42]  Hugues Leblanc,et al.  Arm-leg coordination in recreational and competitive breaststroke swimmers. , 2009, Journal of science and medicine in sport.

[43]  Paul H. Mason,et al.  Degeneracy at Multiple Levels of Complexity , 2010 .

[44]  Keith Davids,et al.  Deconstructing Neurobiological Coordination: The Role of the Biomechanics-Motor Control Nexus , 2010, Exercise and sport sciences reviews.

[45]  Silvia Fantozzi,et al.  Assessment of three-dimensional joint kinematics of the upper limb during simulated swimming using wearable inertial-magnetic measurement units , 2016, Journal of sports sciences.

[46]  J. Kelso,et al.  Nonequilibrium phase transitions in coordinated biological motion: Critical slowing down and switching time , 1987 .

[47]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[48]  A A West,et al.  Characterizing the swimming tumble turn using acceleration data , 2012 .

[49]  Kamiar Aminian,et al.  Inertial measurement unit and biomechanical analysis of swimming: an update , 2013 .

[50]  Adriano Ferrari,et al.  ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors , 2009, Medical & Biological Engineering & Computing.

[51]  Robert Puers,et al.  Wireless Communication with Miniaturized Sensor Devices in Swimming , 2014 .

[52]  Karl M. Newell,et al.  Variability and Motor Control , 1993 .

[53]  A. Gupta,et al.  A Bayesian Approach to , 1997 .

[54]  Daniel Tik-Pui Fong,et al.  The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review , 2010, Sensors.

[55]  F. Morton Performance, Research as , 2009 .

[56]  André Longtin,et al.  Review and classification of variability analysis techniques with clinical applications , 2011, Biomedical engineering online.

[57]  Ian Jones,et al.  A Comparison of Video and Accelerometer Based Approaches Applied to Performance Monitoring in Swimming , 2009 .

[58]  Roozbeh Naemi,et al.  Hydrodynamic glide efficiency in swimming. , 2010, Journal of science and medicine in sport.

[59]  Ross H Sanders,et al.  Shoulder and hip roll changes during 200-m front crawl swimming. , 2008, Medicine and science in sports and exercise.

[60]  Daniel Arthur James,et al.  Validation trial of an accelerometer‐based sensor platform for swimming , 2008 .

[61]  Ludovic Seifert,et al.  Key Properties of Expert Movement Systems in Sport , 2013, Sports Medicine.

[62]  권기영,et al.  Inertial Measurement Unit를 이용한 관절 가동 범위 측정 , 2014 .

[63]  R Fielding,et al.  Energy Expenditure During Front Crawl Swimming: Predicting Success in Middle-Distance Events , 1985, International journal of sports medicine.

[64]  Brendan Burkett,et al.  Validity and reliability of kick count and rate in freestyle using inertial sensor technology , 2009, Journal of sports sciences.

[65]  Andrew A. West,et al.  Kalman filter design for application to an INS analysing swimmer performance , 2010, 2010 18th European Signal Processing Conference.

[66]  Claudio Cobelli,et al.  Motion analysis of front crawl swimming applying CAST technique by means of automatic tracking , 2013, Journal of sports sciences.

[67]  L Seifert,et al.  Kinematical profiling of the front crawl start. , 2010, International journal of sports medicine.

[68]  D Chollet,et al.  A New Index of Coordination for the Crawl: Description and Usefulness , 2000, International journal of sports medicine.

[69]  Pietro Cerveri,et al.  Quantitative underwater 3D motion analysis using submerged video cameras: accuracy analysis and trajectory reconstruction , 2013, Computer methods in biomechanics and biomedical engineering.

[70]  Roger Bartlett,et al.  Is movement variability important for sports biomechanists? , 2007, Sports biomechanics.

[71]  A P Hollander,et al.  Technique and energy losses in front crawl swimming. , 1997, Medicine and science in sports and exercise.

[72]  Juha Röning,et al.  Efficient accelerometer-based swimming exercise tracking , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[73]  Vassilia Hatzitaki,et al.  Bilateral inter-arm coordination in freestyle swimming: Effect of skill level and swimming speed , 2005, Journal of sports sciences.

[74]  Karl M. Newell,et al.  Constraints on the Development of Coordination , 1986 .

[75]  Adrian Lees,et al.  Accuracy of pacing during breaststroke swimming using a novel pacing device, the Aquapacer™ , 2002, Journal of sports sciences.

[76]  Keith Davids,et al.  Constraints on the Complete Optimization of Human Motion , 2009, Sports medicine.

[77]  David V. Thiel,et al.  An integrated swimming monitoring system for the biomechanical analysis of swimming strokes , 2011 .

[78]  Gerhard Tröster,et al.  Swimming performance and technique evaluation with wearable acceleration sensors , 2012, Pervasive Mob. Comput..

[79]  Y. Kwon,et al.  A CAMERA CALIBRATION ALGORITHM FOR THE UNDERWATER MOTION ANALYSIS , 1999 .

[80]  James M. Whitacre,et al.  Degeneracy: a link between evolvability, robustness and complexity in biological systems , 2009, Theoretical Biology and Medical Modelling.

[81]  C Button,et al.  Inter-individual variability in the upper-lower limb breaststroke coordination. , 2011, Human movement science.

[82]  Brendan Burkett,et al.  Optimizing kick rate and amplitude for Paralympic swimmers via net force measures , 2011, Journal of sports sciences.

[83]  Ludovic Seifert,et al.  Coordination Pattern Adaptability: Energy Cost of Degenerate Behaviors , 2014, PloS one.

[84]  Keith Davids,et al.  Expert Performance in Sport , 2015 .

[85]  Diana Hodgins,et al.  Inertial sensor-based knee flexion/extension angle estimation. , 2009, Journal of biomechanics.

[86]  Alan Godfrey,et al.  Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review , 2015, Sensors.

[87]  Bruce Abernethy,et al.  IMPLICATIONS OF AN EXPERTISE MODEL FOR SURGICAL SKILLS TRAINING , 2008, ANZ journal of surgery.

[88]  Sangjoon Park,et al.  Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted IMU , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[89]  Andrew J. Callaway Measuring Kinematic Variables in Front Crawl Swimming Using Accelerometers: A Validation Study , 2015, Sensors.

[90]  Pietro Cerveri,et al.  Comparison of different camera calibration approaches for underwater applications. , 2012, Journal of biomechanics.

[91]  M. Safan,et al.  Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009 , 2010, Theoretical Biology and Medical Modelling.

[92]  P J Beek,et al.  Biomechanics of Competitive Front Crawl Swimming , 1992, Sports medicine.

[93]  R. E. Carlson,et al.  Monotone Piecewise Cubic Interpolation , 1980 .

[94]  Hou Peiwei The Study on Swimming Exercises based on 3D Accelerometer Data Analysis , 2012 .

[95]  Ludovic Seifert,et al.  Neurobiological degeneracy: supporting stability, flexibility and pluripotentiality in complex motor skill. , 2015, Acta psychologica.

[96]  R. Bartlett,et al.  Estimating propulsive forces in swimming from three-dimensional kinematic data. , 1995, Journal of sports sciences.

[97]  Steven Gregory O'keefe,et al.  Real-time swimmers' feedback based on smart infrared (SSIR) optical wireless sensor , 2013 .

[98]  I. Mujika,et al.  World Book of Swimming: From Science to Performance , 2011 .

[99]  K. Davids,et al.  Ecological dynamics and motor learning design in sport , 2012 .

[100]  K. Davids,et al.  An Ecological Dynamics Approach to Skill Acquisition: Implications for Development of Talent in Sport , 2013 .

[101]  Ludovic Seifert,et al.  Different Profiles of the Aerial Start Phase in Front Crawl , 2010, Journal of strength and conditioning research.

[102]  Daniel A. Marinho,et al.  Biomechanics of Competitive Swimming Strokes , 2011 .

[103]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[104]  David G Lloyd,et al.  Repeatability of 3D gait kinematics obtained from an electromagnetic tracking system during treadmill locomotion. , 2007, Journal of biomechanics.

[105]  Kamiar Aminian,et al.  Towards estimation of front-crawl energy expenditure using the wearable aquatic movement analysis system (WAMAS) , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[106]  R. Naemi,et al.  Analysis of swimming technique: state of the art: applications and implications. , 2006 .

[107]  Greg Atkinson,et al.  The effects of changing pace on metabolism and stroke characteristics during high-speed breaststroke swimming , 2004, Journal of sports sciences.

[108]  Leo R. Quinlan,et al.  Application of Video-Based Methods for Competitive Swimming Analysis: A Systematic Review , 2015 .

[109]  Tim J Gabbett,et al.  The Use of Wearable Microsensors to Quantify Sport-Specific Movements , 2015, Sports Medicine.

[110]  Zhaoying Zhou,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. , 2004, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[111]  Robert Rein,et al.  Measurement Methods to Analyze Changes in Coordination During Motor Learning from a Non-linear Perspective , 2012 .

[112]  D.A. James,et al.  An accelerometer based sensor platform for insitu elite athlete performance analysis , 2004, Proceedings of IEEE Sensors, 2004..

[113]  Andrew A. West,et al.  Design and Implementation of an Integrated Performance Monitoring Tool for Swimming to Extract Stroke Information at Real Time , 2013, IEEE Transactions on Human-Machine Systems.

[114]  K. Aminian,et al.  Ambulatory measurement of 3D knee joint angle. , 2008, Journal of biomechanics.

[115]  Timothy Wei,et al.  The Fluid Dynamics of Competitive Swimming , 2014 .

[116]  V. Marozas,et al.  Inertial sensor for objective evaluation of swimmer performance , 2008, 2008 11th International Biennial Baltic Electronics Conference.

[117]  G. Edelman,et al.  Degeneracy and complexity in biological systems , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[118]  Robert J. Crutcher,et al.  The role of deliberate practice in the acquisition of expert performance. , 1993 .

[119]  Kamiar Aminian,et al.  A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[120]  P. E. di Prampero,et al.  Energy balance of human locomotion in water , 2003, European Journal of Applied Physiology.

[121]  Paul Conway,et al.  Accelerometer Profile Recognition of Swimming Strokes (P17) , 2008 .

[122]  R Taïar,et al.  Analysis of swimmers' velocity during the underwater gliding motion following grab start. , 2009, Journal of biomechanics.

[123]  James Bruce Lee,et al.  Inertial sensor, 3D and 2D assessment of stroke phases in freestyle swimming , 2011 .

[124]  Masahiro Todoh,et al.  Gait posture estimation using wearable acceleration and gyro sensors. , 2009, Journal of biomechanics.

[125]  Yuji Ohgi Microcomputer-based acceleration sensor device for sports biomechanics -stroke evaluation by using swimmer's wrist acceleration , 2002, Proceedings of IEEE Sensors.

[126]  Motomu Nakashima,et al.  Development of a swimming motion display system for athlete swimmers’ training using a wristwatch-style acceleration and gyroscopic sensor device , 2010 .

[127]  Brad Aisbett,et al.  Validation of GPS and accelerometer technology in swimming. , 2014, Journal of science and medicine in sport.

[128]  Kamiar Aminian,et al.  Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals , 2013, Journal of sports sciences.

[129]  R. Bartlett,et al.  The Interface of Biomechanics and Motor Control , 2006 .

[130]  Yuji Ohgi,et al.  Sensor Data Mining on the Kinematical Characteristics of the Competitive Swimming , 2014 .

[131]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[132]  Paul Conway,et al.  Development of a real time system for monitoring of swimming performance , 2010 .