Horizontal jumping biomechanics among elite male handball players with and without anterior cruciate ligament reconstruction. An inertial sensor unit-based study

Setuain, Igor; Bikandi, Eder; Ruiz, Francisco Antonio Amú; Izquierdo, Mikel

doi:10.1016/j.ptsp.2019.06.009

Cited by 7 publications

(8 citation statements)

References 41 publications

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“…For example, Patterson et al [21] investigated 14 athletes post-ACL reconstruction (an average of 3.5 years after surgery) and 17 athletes as a control group in a walking scenario using two inertial sensors on the shanks and highlighted that gyroscope features were able to discriminate healthy from ACL-reconstructed individuals, which was not possible using gait temporal variables. On the contrary, Setuain et al [22,23] tested 26 elite handball players (six of them were ACL-reconstructed with an average time since surgery of 6.3 years) using an inertial sensor worn on the lower-back during horizontal and vertical jumping tasks. However, no difference was evident among the two groups.…”

Section: Introductionmentioning

confidence: 99%

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Tedesco

Crowe

Ryan

et al. 2020

Sensors

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Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.

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Section: Introductionmentioning

confidence: 99%

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Tedesco

Crowe

Ryan

et al. 2020

Sensors

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“…The heterogeneity of the rehabilitation process may have biased the long-term outcome in terms of physical activity level and sport-specific performance. However, previous studies have reported that no differences exist between reconstructions using different graft types in relation to long-term function of the knee [29]. Furthermore, the use of a single ISU placed at the trunk level limited the information collected regarding the knee joint biomechanics..…”

Section: Discussionmentioning

confidence: 99%

“…As previously mentioned, the human center of gravity is considered to be located at the L3 lumbar spine level, where the ISU was placed. The data processing description was previously published by this research group [29].…”

Section: Methodsmentioning

confidence: 99%

“…The Z - velocity signal was used to distinguish the boundaries between the different phases of both tasks and were considered positive when the subject moved upwards (corresponding to the propulsive phases of the three consecutive jumps) and negative when subject moved downwards (corresponding to the pre-loading and landing phases). The different phases of the jumping task have been described succinctly in previous studies [25–29] (Fig. 2).…”

Section: Methodsmentioning

confidence: 99%

“…In relation to handball, Myklebust et al [29] observed long-term differences in strength, jumping test scores and anterior-posterior knee joint laxity between ACL-injured and uninjured professional and recreational players after an injury. In addition, Setuain et al [28] presented a validation study that reported promising results validating the utilization of the ISU versus force plate recordings during vertical jumping tasks.…”

Section: Introductionmentioning

confidence: 99%

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Horizontal jumping biomechanics among elite female handball players with and without anterior cruciate ligament reconstruction: an ISU based study

Setuain

Bikandi²,

Ruiz

et al. 2019

BMC Sports Sci Med Rehabil

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BackgroundHandball is a strenuous body-contact team sport that places high loads on the knee joint. Anterior cruciate ligament (ACL) tear is one of the most devastating injuries that any handball player can suffer, and female athletes are at particular risk due to their intrinsic anatomical, hormonal, neuromuscular and biomechanical characteristics. The purpose of this study was to analyze the horizontal jumping biomechanics of female elite handball players with or without previous ACL reconstruction.MethodsTwenty-one female participants (6 with previous ACL reconstruction and 15 uninjured controls) were recruited. Two horizontal hopping tasks were evaluated using inertial sensor unit (ISU)-based technology to assess jumping biomechanics through a direct mechanics-based approach.ResultsThe athletes with previous ACL reconstruction demonstrated a significant (P < 0.05) reduction in the unilateral triple hop for distance compared with the healthy controls. Furthermore, during the initial propulsive phase of the unilateral cross-over hop, the control participants generated significantly (P < 0.05) higher force values in the mediolateral direction (the X axis) with their dominant limb compared with the ACL-reconstructed (ACL-R) limb of previously injured participants.ConclusionsThree-dimensional horizontal jumping biomechanics analyses using ISU-based technologies could provide clinicians with more accurate information regarding the horizontal jumping biomechanical patterns among elite handball female athletes. Furthermore, several mechanical alterations could still be observed among those players who had undergone previous ACL reconstruction, even when several years have passed since the original ACL injury.

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Females with hip-related pain display altered lower limb mechanics compared to their healthy counterparts in a drop jump task

Grosklos

Lewis

Jochimsen

et al. 2022

Clinical Biomechanics

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Horizontal jumping biomechanics among elite male handball players with and without anterior cruciate ligament reconstruction. An inertial sensor unit-based study

Cited by 7 publications

References 41 publications

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Horizontal jumping biomechanics among elite female handball players with and without anterior cruciate ligament reconstruction: an ISU based study

Females with hip-related pain display altered lower limb mechanics compared to their healthy counterparts in a drop jump task

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