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10 pages, 5070 KiB

Open AccessArticle

An Innovative Compact System to Measure Skiing Ground Reaction Forces and Flexural Angles of Alpine and Touring Ski Boots

by Giuseppe Zullo, Pierluigi Cibin, Lorenzo Bortolan, Michele Botteon and Nicola Petrone

Sensors 2023, 23(2), 836; https://doi.org/10.3390/s23020836 - 11 Jan 2023

Cited by 2 | Viewed by 2334

Abstract

Skiing is a popular winter activity spanning various subdisciplines. Key hardware are ski boots, bindings, and skis, which are designed to withstand loads generated during skiing. Obtaining service forces and moments has always been challenging to researchers in the past. The goal of [...] Read more.

Skiing is a popular winter activity spanning various subdisciplines. Key hardware are ski boots, bindings, and skis, which are designed to withstand loads generated during skiing. Obtaining service forces and moments has always been challenging to researchers in the past. The goal of the present study is to develop and test a lightweight and compact measurement system to obtain the Ground Reaction Forces and the kinematics for ski touring and alpine ski. To do so, we adapted two six-axis load cells to fit into ski touring and alpine skis adding 20 mm height and 500 g weight to the original ski. To measure kinematics, we created custom angular sensors from rotary potentiometers. The system was tested indoors using a force platform and motion capture system before a first set of field tests in which the sensors were used to measure ski touring and alpine skis kinetics and kinematics. Validation trials showed maximum errors of 10% for kinetics and 5% for kinematics. Field tests showed data in agreement with previous findings on the topic. The results of this study show the possibility of using our system to study biomechanics and equipment performances for ski touring, alpine skiing, and possibly other disciplines. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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21 pages, 908 KiB

Open AccessArticle

Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures

by Andreas Spilz and Michael Munz

Sensors 2023, 23(1), 5; https://doi.org/10.3390/s23010005 - 20 Dec 2022

Cited by 5 | Viewed by 1587

Abstract

(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely [...] Read more.

(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely on deep learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 inertial measurement units, a dataset of four Functional Movement Screening exercises is recorded. Exercise execution is evaluated by physiotherapists using the Functional Movement Screening criteria. This dataset is used to train a neural network that assigns the correct Functional Movement Screening score to an exercise repetition. We use an architecture consisting of convolutional, long-short-term memory and dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform an extensive hyperparameter optimization. In addition, we are comparing different convolutional neural network structures that have been specifically adapted for use with inertial measurement data. To test the developed approach, it is trained on the data from different Functional Movement Screening exercises and the performance is compared on unknown data from known and unknown subjects. (3) Results: The evaluation shows that the presented approach is able to classify unknown repetitions correctly. However, the trained network is yet unable to achieve consistent performance on the data of previously unknown subjects. Additionally, it can be seen that the performance of the network differs depending on the exercise it is trained for. (4) Conclusions: The present work shows that the presented deep learning approach is capable of performing complex motion analytic tasks based on inertial measurement unit data. The observed performance degradation on the data of unknown subjects is comparable to publications of other research groups that relied on classical machine learning methods. However, the presented approach can rely on transfer learning methods, which allow to retrain the classifier by means of a few repetitions of an unknown subject. Transfer learning methods could also be used to compensate for performance differences between exercises. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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27 pages, 7070 KiB

Open AccessArticle

Automatic Shuttlecock Fall Detection System in or out of a Court in Badminton Games—Challenges, Problems, and Solutions from a Practical Point of View

by Michał Kopania, Jarosław Nowisz and Artur Przelaskowski

Sensors 2022, 22(21), 8098; https://doi.org/10.3390/s22218098 - 22 Oct 2022

Cited by 2 | Viewed by 3660

Abstract

We built an Instant Review System (IRS) for badminton, also named a Challenge System. It allows players to verify linesmen in/out decisions and makes the game fairer. Elements such as lighting, the influence of air-conditioning on the flight trajectory, or the moving mats [...] Read more.

We built an Instant Review System (IRS) for badminton, also named a Challenge System. It allows players to verify linesmen in/out decisions and makes the game fairer. Elements such as lighting, the influence of air-conditioning on the flight trajectory, or the moving mats can significantly impact the final in/out decision. Due to the construction of the shuttlecock, it behaves differently during the flight than, for example, a tennis ball. This publication discusses the problems we encountered during our work with the proposed solution. We present the evolution of the system’s architecture: the first version with the cameras mounted above the court and placed around the court close to the lines, tracking the shuttlecock in 3D; and the second, improved version with cameras placed only around the court, without 3D reconstruction. We used our system during the BWF World Senior Badminton Championships in Katowice. We present the system’s results from this tournament and compare them with linesmen’s decisions. We describe the system’s verification process by the Badminton World Federation and Polish Badminton Federation and discuss evaluation methods for such systems. Our solution is comparable to the commercial product used in the biggest badminton tournaments in regard to processing time and accuracy. Still, our architecture and algorithms make installing it much easier and faster, making the system more adaptive, reliable, flexible, and universal in relation to the practical requirements of sports halls. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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11 pages, 1633 KiB

Open AccessArticle

Impact Position Estimation for Baseball Batting with a Force-Irrelevant Vibration Feature

by Wei-Han Chen, Yang-Chih Feng, Ming-Chia Yeh, Hsi-Pin Ma, Chiang Liu and Cheng-Wen Wu

Sensors 2022, 22(4), 1553; https://doi.org/10.3390/s22041553 - 17 Feb 2022

Viewed by 2185

Abstract

In this work we propose a novel method for impact position estimation during baseball batting, which is independent of impact intensity, i.e., force-irrelevant. In our experiments, we mount a piezoelectric vibration sensor on the knob of a wooden bat to record: (1) 3600 [...] Read more.

In this work we propose a novel method for impact position estimation during baseball batting, which is independent of impact intensity, i.e., force-irrelevant. In our experiments, we mount a piezoelectric vibration sensor on the knob of a wooden bat to record: (1) 3600 vibration signals (waveforms) from ball–bat impacts in the static experiment—30 impacts from each of 40 positions (distributed 1–40 cm from the end of the barrel) and 3 intensities (drop heights at 75, 100, and 125 cm, resp.), and (2) 45 vibration signals from actual battings by three baseball players in the dynamic experiment. The results show that the peak amplitude of the signal in the time domain, and the peaks of the first, second, and third eigenfrequencies (EFs) of the bat all increase with the impact intensity. However, the ratios of peaks at these three EFs (1st/2nd, 2nd/3rd, and 1st/3rd) hardly change with the impact intensity, and the observation is consistent for both the static and dynamic experiments across all impact positions. In conclusion, we have observed that the ratios of peaks at the first three EFs are a force-irrelevant feature, which can be used to estimate the impact position in baseball batting. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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14 pages, 1714 KiB

Open AccessArticle

Foot and Lower Limb Clinical and Structural Changes in Overuse Injured Recreational Runners Using Floating Heel Shoes: Preliminary Results of a Randomised Control Trial

by Javier Gamez-Paya, Lirios Dueñas, Anna Arnal-Gómez and Josep Carles Benítez-Martínez

Sensors 2021, 21(23), 7814; https://doi.org/10.3390/s21237814 - 24 Nov 2021

Cited by 3 | Viewed by 2336

Abstract

Foot-strike and the associated load rate are factors related to overuse injuries in runners. The purpose of this study was to analyse structural and functional changes in runners using floating heel running shoes, compared with runners using conventional footwear. A randomised control trial [...] Read more.

Foot-strike and the associated load rate are factors related to overuse injuries in runners. The purpose of this study was to analyse structural and functional changes in runners using floating heel running shoes, compared with runners using conventional footwear. A randomised control trial was conducted. Twenty runners with overuse injuries were followed over a 12-week gait retraining programme using floating heel running shoes or their conventional footwear. Pain was measured with pressure pain thresholds (PPTs), structural changes were measured with ultrasonography, and severity and impact of injury was scored on the Oslo Sports Trauma Research Centre Overuse Injury Questionnaire (OSTRC-O). Statistical differences were found between groups after the intervention (p < 0.001), with a medium size effect SE = 0.8, and the floating heel running shoes group reached higher PPTs values. Participants using floating heel running shoes showed higher OSTRC-O scores than those using their conventional footwear (p < 0.05), with higher scores after the intervention (p < 0.05). A 12-week gait retraining programme using floating heel running shoes had positive effects on the injury recovery process when compared to the use of conventional footwear, with significant differences in terms of pain and impact on sports activity. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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25 pages, 11353 KiB

Open AccessArticle

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

by Christopher Brumann, Markus Kukuk and Claus Reinsberger

Sensors 2021, 21(13), 4550; https://doi.org/10.3390/s21134550 - 02 Jul 2021

Cited by 8 | Viewed by 3155

Abstract

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the [...] Read more.

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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17 pages, 14587 KiB

Open AccessArticle

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

by Mohammadreza Javadiha, Carlos Andujar, Enrique Lacasa, Angel Ric and Antonio Susin

Sensors 2021, 21(10), 3368; https://doi.org/10.3390/s21103368 - 12 May 2021

Cited by 5 | Viewed by 4552

Abstract

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a [...] Read more.

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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14 pages, 417 KiB

Open AccessArticle

Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

by Glenn Healey

Sensors 2021, 21(1), 64; https://doi.org/10.3390/s21010064 - 24 Dec 2020

Cited by 3 | Viewed by 2724

Abstract

Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These [...] Read more.

Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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11 pages, 1181 KiB

Open AccessArticle

Feasibility of Smartphone-Based Badminton Footwork Performance Assessment System

by Ya-Lan Chiu, Chia-Liang Tsai, Wen-Hsu Sung and Yi-Ju Tsai

Sensors 2020, 20(21), 6035; https://doi.org/10.3390/s20216035 - 23 Oct 2020

Cited by 8 | Viewed by 3898

Abstract

Footwork is the most fundamental skill in badminton, involving the ability of acceleration or deceleration and changing directions on the court, which is related to accurate shots and better game performance. The footwork performance in-field is commonly assessed using the total finished time, [...] Read more.

Footwork is the most fundamental skill in badminton, involving the ability of acceleration or deceleration and changing directions on the court, which is related to accurate shots and better game performance. The footwork performance in-field is commonly assessed using the total finished time, but does not provide any information in each direction. With the higher usage of the smartphones, utilizing their built-in inertial sensors to assess footwork performance in-field might be possible by providing information about body acceleration in each direction. Therefore, the purpose of this study was to evaluate the feasibility of a smartphone-based measurement system on badminton six-point footwork. The body acceleration during the six-point footwork was recorded using a smartphone fixed at the belly button and a self-developed application in thirty badminton players. The mean and maximum of the acceleration resultant for each direction of the footwork were calculated. The participants were classified into either the faster or slower group based on the finished duration of footwork. Badminton players who finished the footwork faster demonstrated a greater mean and maximum acceleration compared to those who finished slower in most directions except for the frontcourt directions. The current study found that using a smartphone’s built-in accelerometer to evaluate badminton footwork is feasible. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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15 pages, 4668 KiB

Open AccessArticle

Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification

by Hyunsung Kim, Jaehee Kim, Young-Seok Kim, Mijung Kim and Youngjoo Lee

Sensors 2020, 20(21), 6004; https://doi.org/10.3390/s20216004 - 22 Oct 2020

Cited by 10 | Viewed by 3207

Abstract

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic [...] Read more.

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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21 pages, 550 KiB

Open AccessArticle

Does the Position of Foot-Mounted IMU Sensors Influence the Accuracy of Spatio-Temporal Parameters in Endurance Running?

by Markus Zrenner, Arne Küderle, Nils Roth, Ulf Jensen, Burkhard Dümler and Bjoern M. Eskofier

Sensors 2020, 20(19), 5705; https://doi.org/10.3390/s20195705 - 07 Oct 2020

Cited by 19 | Viewed by 4836

Abstract

Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners’ performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists [...] Read more.

Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners’ performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists due to the possibility to monitor biomechanically relevant spatio-temporal parameters outside the lab in real-world environments. Researchers developed and investigated algorithms to extract various features using IMU data of different sensor positions on the foot. In this work, we evaluate whether the sensor position of IMUs mounted to running shoes has an impact on the accuracy of different spatio-temporal parameters. We compare both the raw data of the IMUs at different sensor positions as well as the accuracy of six endurance running-related parameters. We contribute a study with 29 subjects wearing running shoes equipped with four IMUs on both the left and the right shoes and a motion capture system as ground truth. The results show that the IMUs measure different raw data depending on their position on the foot and that the accuracy of the spatio-temporal parameters depends on the sensor position. We recommend to integrate IMU sensors in a cavity in the sole of a running shoe under the foot’s arch, because the raw data of this sensor position is best suitable for the reconstruction of the foot trajectory during a stride. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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13 pages, 1382 KiB

Open AccessArticle

Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

by Amr Hassan, Abdel-Rahman Akl, Ibrahim Hassan and Caroline Sunderland

Sensors 2020, 20(11), 3213; https://doi.org/10.3390/s20113213 - 05 Jun 2020

Cited by 11 | Viewed by 3898

Abstract

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship [...] Read more.

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance. Full article

(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)

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