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Influence of frame rate and shutter speed values on a high speed recording of a beach volleyball serve at 90 km/h. Shutter speed values of 1/250 s and 1/1000 s for (a) frame rate 30 fps and (b) frame rate 240 fps.
Source publication
Pueo, B. (2016). High speed cameras for motion analysis in sports science. J. Hum. Sport Exerc., 11(1), 53-73. Video analysis can be a qualitative or quantitative process to analyze motion occurring in a single plane using one camera (two-dimensional or 2D) or in more than one plane using two or more cameras simultaneously (three-dimensional or 3D)...
Context in source publication
Context 1
... blur = 25 m/s x 1/250 s = 10 cm For this low shutter speed, motion blur will be so high that it will cover 10 cm of ball movement, which may difficult analysts from taking accurate observations. VOLUME 11 | ISSUE 1 | 2016 | 59 In Figure 4, frame rate and shutter speed are combined for the examples above. Note that different number of sampled images can be acquired depending on the frame rate value for the first 70 ms of ball hit. ...
Citations
... Additionally, the take-off and landing phases of a vertical jump, which exhibit the maximum velocity values, are typically undersampled in slow-motion videos [13]. Shutter speeds, which are usually not user-operable and only reach their fastest speed in brightly lit scenes, can result in slightly blurred images of feet during the take-off and landing phases in most indoor or poorly lit environments [14]. These factors can affect measurement accuracy due to uncertainty in selecting the correct frame. ...
Jump height tests are employed to measure lower-limb muscle power of athletic and non-athletic populations. The most popular instruments for this purpose are jump mats and, in recent years, smartphone apps, which compute jump height through the manual annotation of video recordings and recently automatically using the sound produced during the jump to extract the flight time. In a previous work, the afore-mentioned sound systems were presented by the authors in which the take-off and landing events from the audio recordings of jump executions were obtained using classical signal processing. In this work, a more precise, noise-immune, and robust system, capable of working in the most unfavorable environments, is presented. The system uses a deep neural network trained specifically for this purpose. More than 300 jumps were recorded to train and validate the network performance. The ground truth was a jump mat, providing a slightly better accuracy in quiet and medium quiet environments but excellent accuracy in noisy and complicated ones. The developed audio-based system is a trustworthy instrument for measuring jump height accurately in any kind of environment, providing a perfect measurement tool that can be accessed through a mobile phone in the form of an app.
... The ICC values found were high, demonstrating excellent reliability between both methods: 0.98 for height, 0.98 for take-off velocity, and 0.99 for impulse. The minimum detectable change (MDC) was also calculated for different variables: jump height, take-off velocity, and impulse, which were 1.34 cm, 1.15 m/s, and 2.93Ns, respectively [19]. Lastly, Kinovea was also used to correlate with other methods of measuring jump height, finding good ICC values (> 0.85) but with consistent results [20]. ...
The individual determination of force-velocity and power-velocity profiles during sprint is of great interest to coaches and sports physiotherapists. As a very short action, sprint evaluation requires a sufficiently accurate and reliable system. The aim of this study was to analyze the reliability of the free software Kinovea®, compared to the MySprint App (Apple Inc, USA). Thirty-one soccer players were evaluated and a comparative study was carried out, where 62 sprints of 30-m were analyzed by two rates: experienced and non-experienced. Vertical poles were placed at 2.5, 5, 7.5, 10, 15, 20, 25 and 30 m. All the sprints were recorded in slow motion and HD image quality. Comparisons of partial and total times were made, in addition to force, velocity and power outputs. No differences were shown between the two measurement methods for the different sprint times (ICC = 0.676–0.941, P < 0.001). The intra-rater reliability of total time in the experienced rater was almost perfect: ICC = 0.993 for Kinovea and 0.984 for the MySprint app; the intra-rater reliability for non-experienced one was 0.833 for Kinovea and 0.862 for the MySprint app. Comparing both methods, the ICC was 0.896. There were no significant differences between the variables force, velocity and power (P > 0.05). This study shows that Kinovea + Excel spreadsheet is a reliable method, also an accessible and low-cost option for sport professionals. However, experience using the software is required, but not for the use of the MySprint app, which is an advantage for non-experienced testers.
... between them, creating redundant data, yet with the same computational expense per frame. This is in addition to the fact that high-framerate cameras are expensive, require more memory, and consume more power [142]. ...
This dissertation explores the emerging field of event-based vision, a significant innovation in visual sensing technology that represents a marked departure from traditional frame-based imaging. Inspired by the biological processes of the human retina, event-based sensors operate asynchronously at the pixel level. They are characterized by their ability to capture data with high temporal resolution and exceptional dynamic range, detecting and recording changes in light intensity independently. This unique capability allows for the continuous and selective monitoring of a scene, dynamically capturing information only as necessary. The core focus of this research is to harness the potential of neuromorphic event-based vision for advancing object detection and tracking methodologies. Despite the promising attributes of event-based sensors, their integration into conventional Computer Vision (CV) architectures poses substantial challenges, primarily due to the asynchronous and sparse nature of their output. This dissertation aims to address these challenges by developing novel methodologies that leverage the unique strengths of event-based vision while overcoming its inherent limitations. A key contribution of this work is the introduction of the Multi-Modal Event-Based Vehicle Detection and Tracking (MEVDT) dataset. This pivotal resource, comprising synchronized streams of event data and grayscale images, facilitates the development and evaluation of novel event-based algorithms, particularly in automotive contexts. Building on this foundation, the dissertation presents a hybrid approach that integrates state-of-the-art frame-based detectors with novel event-based methods, achieving high temporal resolution in object detection and tracking. This approach is further refined with advanced techniques to enhance both detection accuracy and tracking robustness. A central element of this research is the Compact Spatio-Temporal Representation (CSTR). This novel representation effectively encodes event data into a format that is directly compatible with modern computer vision architectures, integrating spatial, temporal, and polarity information. The CSTR, in conjunction with a specially designed augmentation framework, significantly improves the performance of various recognition tasks. The culmination of this dissertation is a comprehensive analysis of the CSTR and other image-like event representations in the context of event-based and multi-modal object detection. Rigorous testing on two event-based multi-modal datasets demonstrates the effectiveness of these methods, offering insights into their comparative performances and the synergies between event-based and frame-based sensors. Through these comprehensive evaluations, this work underscores the importance of optimal spatio-temporal representations for event-based vision tasks. Ultimately, this dissertation represents a step towards the practical application of event-based vision, contributing to the ongoing evolution in the field of CV.
... It is for this reason that goalball players need specialized training in defense skill. Conducting research in diving direction, diving height, and detecting tured in more than one plane with two or more cameras concurrently, removing perspective fault (Pueo, 2016). Further, and as mentioned earlier, goalball is a sport involving both offensive (attack) and defensive (blocking) skills. ...
Goalball is one of the Paralympic sport disciplines designed for athletes with visual impairments (VI), and athletes wear eyeshades while they perform the sport for a fair competition. The game is played with a ball with bells inside allowing its movement to be heard by the players and therefore sport-specific attack and defense foundations needs to be developed for this Paralympic sport discipline. Taking the VI into account, movement phases of attack and defense techniques may be improved by the coach with proper training programs and with this, athletic ability of the athlete with VI may be enhanced. This is especially significant for novice goalball athletes as mastering fundamental skills properly may lead to better performance on the court while minimizing the risk of injury. In contrast, there is a scarce literature on examining the kinemat-ic analysis of Paralympic goalball sport. Limited studies carried out 2-D video observational analysis for goal-ball-throwing technique and in fact goalball-throwing technique is three dimensional. Moreover, studies conducted kinematic analysis only for throwing technique. For these reasons, 3-D kinematic analysis of throwing and defense techniques is essential. In addition, development of throwing and defense techniques with precise training methods requires taking into account of kinetic chain of body segments, functional movement must be considered and evaluated completely. From this point of view, functional training methods that can contribute defense performance can be recommended as there is no study evaluating the functional performance of goalball athletes. This also can contribute to development of goalball specific test protocols to evaluate foundational goalball skills and novel training approaches can be applied in order to develop physiological and motor skills of goalball sport.
... The ICC values found were high, demonstrating excellent reliability between both methods: 0.98 for height, 0.98 for take-off velocity, and 0.99 for impulse. The minimum detectable change (MDC) was also calculated for different variables: jump height, take-off velocity, and impulse, which were 1.34cm, 1.15m/s, and 2.93Ns, respectively.[19] Lastly, Kinovea was also used to correlate with other methods of measuring jump height (based on the mentioned formula), nding good ICC values (> 0.85), although underestimating the jump height, but with consistent results).[20] ...
The individual determination of force-velocity and power-velocity profiles during sprint is of great interest to coaches and sports physiotherapists. As a very short action, sprint evaluation requires a sufficiently accurate and reliable system. The aim of this study was to analyze the reliability of the free software Kinovea®, compared to the MySprint App (Apple Inc, USA).
Thirty-one soccer players were evaluated and a comparative study was carried out, where 62 sprints of 30-meters were analyzed by two rates: experienced and non-experienced. Vertical poles were placed at 2.5, 5, 7.5, 10, 15, 20, 25 and 30 meters. All the sprints were recorded in slow motion and HD image quality. Comparisons of partial and total times were made, in addition to force, velocity and power outputs. No differences were shown between the two measurement methods for the different sprint times (ICC = 0.676–0.941, p < 0.001). The intra-rater reliability of total time in the experienced rater was almost perfect: ICC = 0.993 for Kinovea and 0.984 for the MySprint app; the intra-rater reliability for non-experienced one was 0.833 for Kinovea and 0.862 for the MySprint app. Comparing both methods, the ICC was 0.896. There were no significant differences between the variables force, velocity and power (p > 0.05).
This study shows that Kinovea + Excel spreadsheet is a reliable method, also an accessible and low-cost option for sport professionals. However, experience using the software is required, but not for the use of the MySprint app, which is an advantage for non-experienced testers.
... In a low light condition, the object color is not differentiated from the background, leading to object detection failure. One solution for the low light condition would be to increase the camera's ISO max setting 31 . Although a higher ISO setting allows the recording of the object in a low light environment, this setting will also increase the sensitivity of the camera sensors, which means more noise in the video data 31 . ...
... Frame rate is another vital factor in improving the accuracy of the system. The frame rate should be high enough for faster movements, such as ball throwing in sports 31 . Specifically, most daily activities related to goal-directed arm reaching would require a high frame rate to estimate the movement kinematics accurately. ...
... Shutter speed is also critical to reducing motion blur. We used an automatic shutter speed setting during our preliminary data collection for Experiment 3. The automatic shutter speed setting will select the slow shutter speed to maximize the light reaching the sensor 31 . Thus, the automatic shutter speed setting will increase motion blur. ...
We developed a computer vision-based three-dimension (3D) motion capture system employing two action cameras to examine fine hand motor skill by tracking an object manipulated by a hand. This study aimed to examine the accuracy and feasibility of this approach for detecting changes in a fine hand motor skill. We conducted three distinct experiments to assess the system's accuracy and feasibility. We employed two high-resolution, high-frame-rate action cameras. We evaluated the accuracy of our system in calculating the 3D locations of moving object in various directions. We also examined the system's feasibility in identifying improvement in fine hand motor skill after practice in eleven non-disabled young adults. We utilized color-based object detection and tracking to estimate the object's 3D location, and then we computed the object's kinematics, representing the endpoint goal-directed arm reaching movement. Compared to ground truth measurements, the findings demonstrated that our system can adequately estimate the 3D locations of a moving object. We also showed that the system can be used to measure the endpoint kinematics of goal-directed arm reaching movements to detect changes in fine hand motor skill after practice. Future research is needed to confirm the system's reliability and validity in assessing fine hand motor skills in patient populations.
... The present study recorded the snatch movements at 60 Hz, which was deemed sufficient for the chosen movements. High-speed cameras, coupled with fast shutter speeds and sufficient lighting, can capture high-quality video at a higher frame rate [68,69,76]. Lastly, three of the participants (M1, M3, and F1) competed in the Birmingham 2022 Commonwealth Games just before the data collection commenced, and they may have been tired from traveling and competing. ...
Traditionally, the biomechanical analysis of Olympic weightlifting movements required laboratory equipment such as force platforms and transducers, but such methods are difficult to implement in practice. This study developed a field-based method using wearable technology and videos for the biomechanical assessment of weightlifters. To demonstrate the practicality of our method, we collected kinetic and kinematic data on six Singapore National Olympic Weightlifters. The participants performed snatches at 80% to 90% of their competition one-repetition maximum, and the three best attempts were used for the analysis. They wore a pair of in-shoe force sensors loadsol® (novel, Munich, Germany) to measure the vertical ground reaction forces under each foot. Concurrently, a video camera recorded the barbell movement from the side. The kinematics (e.g., trajectories and velocities) of the barbell were extracted using a free video analysis software (Kinovea). The power–time history was calculated from the force and velocity data. The results showed differences in power, force, and barbell velocity with moderate to almost perfect reliability. Technical inconsistency in the barbell trajectories were also identified. In conclusion, this study presented a simple and practical approach to evaluating weightlifters using in-shoe wearable sensors and videos. Such information can be useful for monitoring progress, identifying errors, and guiding training plans for weightlifters.
... Finally, this investigation did not examine the effects of frame rate, motion blur, or lighting on pitchAI TM outputs. All of these variables play a role in the capture quality of video, and it is expected that reduced frame rates, increased motion blur, and reduced light quality (Pueo, 2016) would all impact the accuracy of pitchAI TM . The data collection for the validation of pitchAI TM was indoors, in a controlled laboratory environment. ...
This study sought to compare and validate baseball pitching mechanics, including joint angles and spatiotemporal parameters, from a single camera markerless motion capture solution with a 3D optical marker-based system. Ten healthy pitchers threw 2–3 maximum effort fastballs while concurrently using marker-based optical capture and pitchAITM (markerless) motion capture. Time-series measures were compared using R-squared (r²), and root mean square error (RMSE). Discrete kinematic measures at foot plant, maximal shoulder external rotation, and ball release, plus four spatiotemporal parameters were evaluated using descriptive statistics, Bland-Altman analyses, Pearson’s correlation coefficients, p-values, r², and RMSE. For time-series angles, r² ranged from 0.69 (glove arm shoulder external rotation) to 0.98 (trunk and pelvis rotation), and RMSE ranged from 4.37° (trunk lateral tilt) to 20.78° (glove arm shoulder external rotation). Bias for individual joint angle and spatiotemporal parameters ranged from −11.31 (glove arm shoulder horizontal abduction; MER) to 12.01 (ball visible). RMSE was 3.62 m/s for arm speed, 5.75% height for stride length and 21.75 ms for the ball visible metric. pitchAITM can be recommended as a markerless alternative to marker-based motion capture for quantifying pitching kinematics. A database of pitchAITM ranges should be established for comparison between systems.
... This factor can be adjusted according to the pedalling frequency of the individual. 32 Finally, the light must be sufficient to allow great illumination of the cyclist 32 Millour et al. point out that lower limb discrepancy, which affects some cyclists, can cause an asymmetry between joint angles of both lower limbs. 33 In this case, it appears mandatory to assess joint kinematics of each side of the body. ...
... This factor can be adjusted according to the pedalling frequency of the individual. 32 Finally, the light must be sufficient to allow great illumination of the cyclist 32 Millour et al. point out that lower limb discrepancy, which affects some cyclists, can cause an asymmetry between joint angles of both lower limbs. 33 In this case, it appears mandatory to assess joint kinematics of each side of the body. ...
The optimization of cycling position is essential to improve performance and prevent overuse injuries. Bike-fitting methods , based on biomechanical variables, have been proposed in the scientific literature. To facilitate and generalize their use, the bike-fitting industry has developed various technologies to study and analyze the cycling position. The vast majority of bike-fitting protocols are based on joint kinematics, which can be evaluated in laboratory with two-or three-dimensional motion analysis systems. Joint kinematics can also be assessed in outdoor conditions with inertial measurement units, but currently, these tools provide a limited number of variables compared to laboratory systems. In addition, the bike-fitting professional can analyse pedalling technique with pedal forces to understand the effects of the bike adjustments on pedalling effectiveness. To complete the biomechanical evaluation, pressure mapping sensors allow for the measurement of the pressure load and distribution on the interfaces between the cyclist and the bicycle to detect imbalances and choose bike components (e.g., saddle). To go further in the analysis, muscular activity can be assessed with surface electromyography sensors to detect imbalances or asymmetry. The aim of this literature overview is to clearly define the role of these technologies in a bike-fitting protocol and identify their characteristics and limitations while proposing perspectives for future developments. Therefore, this work is intended for bike-fitting professionals and coaches wishing to choose the most suitable technologies to study and improve the cycling position, and for the bike-fitting industry , in order to optimize existing technologies and help develop new concepts.
... Furthermore, they can be used in a variety of clinical settings because no professional knowledge is required to operate the devices. In the case of recently released high-end webcams, some products have a performance of 120 or 240 frames per second, facilitating the use in sports fields [28,29]. ...
