Data acquired by a single object sensor for the detection and quality evaluation of table tennis forehand strokes

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

doi:10.1016/j.dib.2020.106504

Cited by 11 publications

(13 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A set of experiments was applied to the self-collected dataset to evaluate the proposed method's performance. As seen in Table 1 , the current study consists of four main sections, namely, (1) hardware setup, (2) data gathering [ 20 ], (3) preprocessing [ 17 ], and (4) processing. The first three phases are in charge of sample collection and preparation.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, based on the defined protocols, samples were gathered, scored, and labeled. All details of the data gathering phase are published in the authors' recent article [ 20 ], and the collected samples are made publicly available in [ 19 ]. In the following subsections, these steps and characteristics of the participants are briefly described.…”

Section: Methodsmentioning

confidence: 99%

“…The ML algorithms were trained and tested based on the coach evaluation scores to assess the quality of the performed strokes. These scores were collected and calculated during the data acquisition phase [ 20 ]. Thus, to evaluate each type of Forehand strokes (Basic, Topspin, and Push), three separate ML models are needed.…”

Section: Methodsmentioning

confidence: 99%

“…e dataset generated and analyzed during the current study is publicly available, and the data and their gathering procedures have been published earlier. Previously reported data were used to support this study, and these prior studies (and datasets) are cited at relevant places within the text as references [19][20][21].…”

Section: Data Availabilitymentioning

confidence: 99%

“…The three main contributions of this paper are as follows: (1) the authors introduce the self-collected dataset, which is made publicly available at [ 19 ], consisting of segmented sensor data (accelerometer, gyroscope, magnetometer, and Euler angles) from one miniaturized, inexpensive, and nonintrusive inertial sensor (BNO055) mounted on a standard Table Tennis racket. Moreover, the study's data collection phase's details and steps were released in the authors' last work [ 20 ]. (2) We examine and compare the ML models' performance when their parameters were tuned based on the dataset.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

Tennis shot side-view and top-view data set for player analysis in Tennist

Lai,

Huang,

Lin

et al. 2024

Data in Brief

View full text Add to dashboard Cite

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Data acquired by a single object sensor for the detection and quality evaluation of table tennis forehand strokes

Cited by 11 publications

References 3 publications

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

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

Tennis shot side-view and top-view data set for player analysis in Tennist

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

Contact Info

Product

Resources

About