A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Tabrizi, Sahar S.; Pashazadeh, Saeid; Javani, Vajiheh

doi:10.1155/2021/5584756

Cited by 16 publications

(16 citation statements)

References 63 publications

(117 reference statements)

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“…In the analysis of a large number of movement patterns, the model can be widely used in foot analysis in sports and clinical medicine. Therefore, Tabrizi et al think that using the Oxford Foot Model to study the foot biomechanical characteristics of table tennis footwork can find more movement patterns between the small joints of the foot and help us deeply understand its movement mechanism, which will be a brand new attempt [ 10 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis

2022

Computational Intelligence and Neuroscience

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In order to solve the problem of athletes with lower limb movement injuries in table tennis footwork, with the increasing risk of sports, the incidence of acute injury also increased. The increase in athletic training, on the one hand, improves the skill level of the athlete; on the other hand, it increases the chances of more chronic injuries. Through the investigation of the injury site of 143 table tennis players, the researchers found that the lower limb injury accounted for 32% of the total injuries and the upper limb injury rate accounted for 33.14% of the trunk, accounting for 34.29%. The injury sites are mostly concentrated in the lumbar region, followed by the shoulders and knees. Through epidemiological research on the injuries of outstanding table tennis players, the survey results show that the probability of lower extremity injuries ranks in the top three, and most of them are acute sprains and chronic strain injuries. By applying the principles of sports biomechanics, a biomechanical analysis of the asynchronous foot movement in table tennis is proposed; from three aspects of kinematics, dynamics, and plantar pressure, it is found that the injury of table tennis is closely related to technical play connect. The risk of sports injury is inevitable in a sense, due to the long-term local overload, which causes the strain of the sports system. In order to avoid or reduce the occurrence of such sports injuries, coaches should standardize the technical movements of the players and arrange the exercise load reasonably according to the characteristics of the sports.

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Section: Literature Reviewmentioning

confidence: 99%

Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis

2022

Computational Intelligence and Neuroscience

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“…Parametric assessments evaluate domain-specific exercise characteristics using either a continuous numerical value (e.g., follow-through for table tennis strokes [51]) or by classifying them (e.g., identification of postural errors during a barbell squat exercise [52]). Such methods utilize techniques similar to those of the other two approaches, but with a focus on domain-specific parameters.…”

Section: C: Parametric Assessmentsmentioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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“…In running, Struber et al used an S-Move system consisting of five nine-axis sensors to achieve three-dimensional automatic and dynamic gait analysis [11]. In table tennis, Tabrizi et al developed a forehand stroke evaluation system with a single nine-axis sensor to provide players, particularly new players, actual practice with three forehand strokes [12].…”

Section: Introductionmentioning

confidence: 99%

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

Yen¹,

Chen²,

Chen³

et al. 2023

Computers, Materials &Amp; Continua

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A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation.

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A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Cited by 16 publications

References 63 publications

Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis

Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

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