P3t (Training, Guidance and Integrated Assistance) Model-Based on Assesment Necesiity to Create PTK (Class Action Research) Article Results for Elementary School Teachers

Suyoto, '; Egar, Ngasbun; Siswanto,

doi:10.29013/ejhss-20-6-54-65

Cited by 1 publication

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“…In order to provide an interactive, immersive environment for the trainee, the virtual training simulator must be able to recognize the movements performed by the trainee [11]. For this reason, the human action recognition method was introduced into the virtual training simulation based on the Kinect-based somatosensory device [12]. So far, most of the research on virtual training simulators is based on human motion recognition of wearable motion capture suits to obtain precise information of human motions, thereby synchronizing the training content according to the motions.…”

Section: Literature Reviewmentioning

confidence: 99%

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Chen

2022

Journal of Sensors

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In order to solve the problems that the gymnastics action recognition system cannot select gymnastics training items and difficulty modes, the calculation matching degree and its threshold angle are inaccurate, the effect and efficiency of action learning are low, and this research proposes a gymnastics action recognition and training pose analysis methods based on artificial intelligence sensors. This method completes the performance improvement of the traditional human action recognition algorithm and uses the skeletal features of the Kinect sensor to discriminate sports actions. Clustering based on static K -means algorithm increases the accuracy of pose selection, and each pose is recognized by human action using artificial neural network (ANN) and hidden Markov model (HMM). The obtained results are as follows: comparison of nonstatic and proposed static K -means algorithm on the training set and the overall accuracy of the proposed method is much better than the previous method. Among the four movements, the accuracy rate of “sitting” and “standing” movements is significantly higher, reaching 100%. In the gymnastics action recognition experiment, the average recognition rate of the system in this research is 93.6%, the false rejection rate is 5%, and the false acceptance rate is only 1.4%. It is proved that the system interface designed in this research can prompt the part that needs to be corrected, display the error on the output device, more efficiently assist the user to perform targeted training on the action to be learned, and improve the effect and efficiency of action learning.

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Section: Literature Reviewmentioning

confidence: 99%

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Chen

2022

Journal of Sensors

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show abstract

P3t (Training, Guidance and Integrated Assistance) Model-Based on Assesment Necesiity to Create PTK (Class Action Research) Article Results for Elementary School Teachers

Cited by 1 publication

References 0 publications

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

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