Computer-assisted yoga training system

Chen, Hua-Tsung; He, Yu-Zhen; Hsu, Chun-Chieh

doi:10.1007/s11042-018-5721-2

Cited by 57 publications

(32 citation statements)

References 25 publications

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“…In addition, they also haven’t applied the yoga recognition system to evaluating and guiding the yoga postures. Template star skeleton [30] was used to recognize 12 yoga postures via two cameras, and visual feedback was adopted for providing guidance to subjects. Although our wearable device was less comfortable than [30], we have modeled more yoga postures including body folding and achieved a higher accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Template star skeleton [30] was used to recognize 12 yoga postures via two cameras, and visual feedback was adopted for providing guidance to subjects. Although our wearable device was less comfortable than [30], we have modeled more yoga postures including body folding and achieved a higher accuracy. Besides, the posture evaluation method via IMUs was more precise than that via cameras, since it may be hard to acquire precise joint angles via the key points and contour of yoga postures extracted from images.…”

Section: Resultsmentioning

confidence: 99%

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Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Zhang

Chen

et al. 2019

Sensors

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Currently, with the satisfaction of people’s material life, sports, like yoga and tai chi, have become essential activities in people’s daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitating from yoga video. They could not know whether they performed standardly without feedback and guidance. In this paper, we proposed a full-body posture modeling and quantitative evaluation method to recognize and evaluate yoga postures to provide guidance to the learner. Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category. The posture data on each body part was regarded as a multidimensional Gaussian variable to build a Bayesian network. The conditional probability of the Gaussian variable corresponding to each body part relative to the Gaussian variable corresponding to the connected body part was used as criterion to quantitatively evaluate the standard degree of body parts. The angular differences between nonstandard parts and the standard model could be calculated to provide guidance with an easily-accepted language, such as “lift up your left arm”, “straighten your right forearm”. To evaluate our method, a wearable device with 11 inertial measurement units (IMUs) fixed onto the body was designed to measure yoga posture data with quaternion format, and the posture database with a total of 211,643 data frames and 1831 posture instances was collected from 11 subjects. Both the posture recognition test and evaluation test were conducted. In the recognition test, 30% data was randomly picked from the database to train BP-ANN and FCM classifiers, and the recognition accuracy of the remaining 70% data was 95.39%, which is highly competitive with previous posture recognition approaches. In the evaluation test, 30% data were picked randomly from subject three, subject four, and subject six, to train the Bayesian network. The probabilities of nonstandard parts were almost all smaller than 0.3, while the probabilities of standard parts were almost all greater than 0.5, and thus the nonstandard parts of body posture could be effectively separated and picked for guidance. We also tested separately the trainers’ yoga posture performance in the condition of without and with guidance provided by our proposed method. The results showed that with guidance, the joint angle errors significantly decreased.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Zhang

Chen

et al. 2019

Sensors

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“…However, this method is an incomplete decomposition of the model and many regions of saliency are not detected [11]. The method based on diffusion geometry has achieved remarkable success in the analysis of 3D nonrigid models due to the reflection of the model's intrinsic properties [12]. The literature [13] combined the eigenfunctions of the Laplace Beltrami operator with homology consistency theory to generate a hierarchical segmentation method for the model.…”

Section: Related Workmentioning

confidence: 99%

“…The expression of the Sigmod function is shown in Equation (11), which can map the input values between 0 and 1 and can be used in the attention mechanism for weight assignment, and the Sigmod function is derivable and can be optimized with the gradient backward iteration algorithm. Its derivative is shown in Equation (12).…”

Section: Application Of Thementioning

confidence: 99%

Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction

2021

Advances in Mathematical Physics

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The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. For the combination problem of discrete skeleton points, it is proposed to take the limb parts formed between skeleton points as information carriers, construct the skeleton point association information model using vector field, and consider it as a feature, to obtain the relationship between different skeleton points by using the detection method. It is pointed out that the reorganization problem of discrete skeleton points in multiperson scenes is an NP-Hard problem, which can be simplified by decomposing it into a set of subproblems of bipartite graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. Compared with traditional two-dimensional images, audio, video, and other multimedia data, the 3D diffusion model data describe the 3D geometric morphological information of the target scene and are not affected by lighting changes, rotation, and scale transformation of the target and thus can describe the realistic scene more comprehensively and realistically. With the continuous updating of diffusion model acquisition equipment, the rapid development of 3D reconstruction technology, and the continuous enhancement of computing power, the research on the application of 3D diffusion model in the detection and extraction of a human skeleton in sports dance videos has become a hot direction in the field of computer vision and computer graphics. Among them, the feature detection description and model alignment of 3D nonrigid models are a fundamental problem with very important research value and significance and challenging at the same time, which has received wide attention from the academic community.

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“…As technology has developed, computer-aided systems have been widely applied in the education or training fields [18,19,20,21], Bačić [22] and SAP Sports One group [23] used wearable sensors to collect human motion data and train in operational skills. Until now, a lot of driver feedback programs have been designed, each trying to cover as many aspects of driving as possible.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Driver Training System Based on Real Cars

Duan

Yan

2019

Sensors

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In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. Six vehicle state parameters are identified as the critical features of the reverse parking machine learning model and represented quantitatively. A multiple-embedded-sensor net-based system mounted on a real vehicle is developed to collect the actual data of the six critical features. The data collected at the same time are bound together and encapsulated into a vector and sequenced by time with a label given by the multiple-embedded-sensor net. All vectors are evaluated by subjective assessment conclusions from experienced driving instructors and the positive ones are used as the training data of the model. The trained model can remind the driver of the next correct operation during training, and can also analyze the improvements after the training. The model has achieved good results in practical application. The experiments prove the validity and reliability of the proposed driver training model.

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Computer-assisted yoga training system

Cited by 57 publications

References 25 publications

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction

An Intelligent Driver Training System Based on Real Cars

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