Firstly, we organize a database of accurately captured driver poses from different motion clips with filtered structures, including a global motion graph and multiple local motion graphs. Inspired by previous researches, the present work focused on testing a data driven approach for improving driver’s upper body movement reconstruction with a KinectĬamera. However, when body parts are partially occluded, the accuracy of Kinect data will drop markedly. Microsoft Kinect is one of the best candidates for monitoring driver’s posture thanks to its innovative feature of real time motion capture without use of markers and its low cost. The postural information could be used for the development of smart airbags, for detecting possible fatigue in long travel and for recognizing activities which may determine if the driver has enough time to take over the control in an intelligent vehicle when encountering hazardous situations. Monitoring driver’s postures has extensive applications. As a result, it was confirmed that a three-dimensional quantitative analysis based on sequence images is possible. Quantitative analysis results from each analysis model showed that the upper body motion prediction RMSE averaged 4.23 degrees, the head motion prediction RMSE averaged 5.18 degrees, and the shoulder and pelvis twisting angle prediction RMSE averaged 3.86 degrees. In the major swing section classification experiment, each swing section was classified with an average accuracy of about 95.44%. For the experiment, in this paper, a total of 520 times swing data were obtained using no. This classifies the major swing section, and analyzes the quantitative status of the twisting angles of the upper body, head, shoulder and pelvis for body-sway, head-up and X-factor analysis. In this paper, CNN was used to extract the appropriate features from the image of the golf frontal swing sequence, and a regression model based on Bi-LSTM was used to predict the correct information in each sequence. In this paper, the method to overcome the limitations of the existing three-dimensional golf swing analysis system by using deep learning technology, and analyze the three-dimensional quantitative information through sequence images acquired with a single camera is studied. ![]() A comparison shows that by using the strain-gage sensor and multi-class LSVM model, the golfer-swing signature is recognized accurately and effectively. ![]() The experiment results of the training accuracy, testing accuracy and training time are compared with the results of other models including decision-tree algorithms, discriminant-analysis algorithms, other support vector machine algorithms, k-nearest neighbor (KNN) classifiers, and ensemble classifiers. To classify each golfer-swing multi-class classifier is built by combining binary LSVM models with an error-correcting-output-codes multi-class strategy. Golf-swing signals are acquired by a strain-gage sensor fitted to the golf club that measures the club bend. A sensor-based golfer-swing signature-recognition method is performed by using linear support vector machine (LSVM). Based on the classification of the golfer-swing shapes, this work analyses golf-swings. ![]() This can be done through feedback information provided by a specialized equipment or by a personal coach. Recognizing golf swings of an individual golf player is essential to help improving the golf skill level. Golf performance varies from person to person because of the differences in physical features of golfer's body and skill level.
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