A Literature Review Informing an Operational Guideline for Inertial Sensor Propulsion Measurement in Wheelchair Court Sports

Shepherd, Jonathan B.; James, Daniel; Espinosa, Hugo G.; Thiel, David V.; Rowlands, D. D.

doi:10.3390/sports6020034

Cited by 18 publications

(14 citation statements)

References 31 publications

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“…Three synchronised identical 9DOF inertial sensors (SABELSense, Griffith University, Nathan Australia) [40][41][42] with properties (250 Hz sampling frequency, ±16 g accelerometer, ±2000 deg/s gyroscope, ±7 Gauss magnetometer, 23 g weight) dimensions 55 mm × 30 mm × 13 mm (L × W × H) (Figure 2) were used in this study. Two sensors were attached underneath the boxing glove of the left and right wrists using Velcro™ bands and one sensor was placed into a specially designed sports harness with a sensor pocket located in close proximity to the third thoracic vertebrae (T3) on the upper spine (Figure 3).…”

Section: Methodsmentioning

confidence: 99%

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

Worsey

Espinosa

Shepherd

et al. 2020

IoT

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Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

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Section: Methodsmentioning

confidence: 99%

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

Worsey

Espinosa

Shepherd

et al. 2020

IoT

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“…Wearable technology often consists of global positioning system (GPS) units with inbuilt inertial measurement unit (IMU) sensors. IMU sensors generally consist of three axis accelerometers, gyroscopes, and magnetometers, and have sample rates that are greater than 100 Hz [20][21][22][23]. Many elite sports codes (e.g., soccer, rugby, field hockey etc.)…”

Section: Assessments Of Wearable Technologymentioning

confidence: 99%

Assessment of Head Impacts and Muscle Activity in Soccer Using a T3 Inertial Sensor and a Portable Electromyography (EMG) System: A Preliminary Study

et al. 2020

Self Cite

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Heading the ball is an important skill in soccer. Head impacts are of concern because of the potential adverse health effects. Many elite players now wear GPS (that include inertial monitoring units) on the upper spine for location tracking and workload measurement. By measuring the maximum acceleration of the head and the upper spine, we calculated the acceleration ratio as an attenuation index for participants (n = 8) of different skill levels during a front heading activity. This would allow for in-field estimates of head impacts to be made and concussive events detected. For novice participants, the ratio was as high as 8.3 (mean value 5.0 ± 1.8), whereas, for experienced players, the mean ratio was 3.2 ± 1.5. Elite players stiffen the neck muscles to increase the ball velocity and so the torso acts as a support structure. Electromyography (EMG) signals that were recorded from the neck and shoulder before and after a training intervention showed a major increase in mean average muscle activity (146%, p = 3.39 × 10−6). This was accompanied by a major decrease in acceleration ratio (34.41%, p = 0.008). The average head-ball impact velocity was 1.95 ± 0.53 m/s determined while using optical motion capture. For this low velocity, the impact force was 102 ± 19 N, 13% of the published concussive force. The voluntary action of neck muscles decreases isolated head movements during heading. Coaches and trainers may use this evidence in their development of junior players.

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“…The relation with the capability of PM generated by the shoulder and elbow is also unknown. In this line, technological advancement can offer new alternatives to improve knowledge applied to functional classification in wheelchair sports´ specific skills [ 6 , 30 ], as WB is.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Sprint Velocity and Peak Moment at Shoulder and Elbow in Elite Wheelchair Basketball Players

Villacieros

Pérez-Tejero

Garrido

et al. 2020

IJERPH

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Specific wheelchair basketball (WB) skills on the court have been poorly analyzed in relation to improving players’ performance according to their functional class. The purpose of this study was to evaluate the associations between maximum velocity (Vmax) and peak moment (PM) in the shoulder and elbow joints in specific WB skills and to compare performance between the main two groups by functional class. Twelve male WB players, divided in categories A (functional classes 1.0–2.5) and B (class 3.0–4.5), performed a sprint test battery composed by four tests (with and without ball) and isokinetic tests. A significant relationship between PM of the internal and external shoulder rotation and the flexion and extension elbow with Vmax (p < 0.05) was found. During a 5 m backward sprint test, category B was faster in the last three meters than category A (p < 0.05) and also for the rest of the test but p = NS. Category B showed higher PM than category A for internal shoulder rotation (ISR) at 60 °/s and at 180 °/s on the dominant side (DS) (p < 0.05). In conclusion, ISR on the DS was found different for both groups and showed significant relationship with Vmax in all of the tests performed. Moreover, at higher speeds the elbow flexion and extension in DS were correlated with Vmax in all the actions with ball (passing, bouncing, and braking) reflected the importance of these joints movements in acquiring speed when performing sport specific WB skills.

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A Literature Review Informing an Operational Guideline for Inertial Sensor Propulsion Measurement in Wheelchair Court Sports

Cited by 18 publications

References 31 publications

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

Assessment of Head Impacts and Muscle Activity in Soccer Using a T3 Inertial Sensor and a Portable Electromyography (EMG) System: A Preliminary Study

Relationship between Sprint Velocity and Peak Moment at Shoulder and Elbow in Elite Wheelchair Basketball Players

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