A Wushu Posture Recognition System Based on MediaPipe

Ma, Jiewei; Lian-zhen, MA; Ruan, Wenpian; Chen, Haidong; Feng, Jinyong

doi:10.1109/tcs56119.2022.9918744

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Posture recognition technology has been extended to other fields, especially sports and fitness. A study [26] was conducted using MediaPipe Pose to recognize the Wushu posture to facilitate independent training for users.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 3 displays the four angles used in this method which correspond to specific articulation points on the body. The articulation point num ber is provided by MediaPipe Pose, and the following two types of angle, marked in Fig ure 3 as (1) and ( 2), are used in the system: (1) Pelvic angle: the angle between the line starting at the shoulder (11,12) and passing through the pelvis (23,24) to the knee (25,26). (2) Knee angle: the angle between the line starting at the pelvis (23,24) and passing through the knee (25,26) to the ankle (31,32).…”

Section: Joint Coordinate Extraction Using Mediapipe Posementioning

confidence: 99%

“…Based on the tracked head, the top, bottom, left, and right sides are cropped in an appropriate ratio. By repeating the process of taking the (1) Pelvic angle: the angle between the line starting at the shoulder (11,12) and passing through the pelvis (23,24) to the knee (25,26). (2) Knee angle: the angle between the line starting at the pelvis (23,24) and passing through the knee (25,26) to the ankle (31,32).…”

Section: Joint Coordinate Tracking Using Opencvmentioning

confidence: 99%

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Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start

Park,

Moon,

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2023

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In speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the first 100 m of a speed skating race, aiming to overcome the limitations of human vision. The method uses image recognition technology, specifically MediaPipe, to track key body joint coordinates during the skater’s motion. These coordinates are calculated into important body angles, including those from the shoulder to the knee and from the pelvis to the ankle. To quantify the skater’s motion, the study introduces generalized labeling logic (GLL), a key index derived from angle data. The GLL signal is refined using Gaussian filtering to remove noise, and the number of inflection points in the filtered GLL signal is used to determine the number of strokes. The method was designed with a focus on frontal videos and achieved an excellent accuracy of 99.91% when measuring stroke counts relative to actual counts. This technology has great potential for enhancing training and evaluation in speed skating.

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Section: Related Workmentioning

confidence: 99%

Section: Joint Coordinate Extraction Using Mediapipe Posementioning

confidence: 99%

Section: Joint Coordinate Tracking Using Opencvmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start

Park,

Moon,

Lee

2023

Electronics

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“…Because of this, we decided to perform a systematic review to see how well yoga intervention fared with other exercise programs and inactive controls in improving health-related fitness and quality of life for individuals. The crucial information such as posture, attitude, gest [7]. Contend that the usage of ICTs and apps reduces the need for traditional modes of transportation as well as the need for physical venues to house genuine places of employment.…”

Section: Introductionmentioning

confidence: 99%

Improved Yoga Pose Detection Using MediaPipe and MoveNet in a Deep Learning Model

Parashar,

Mishra,

Sharma

et al. 2023

RIA

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The escalating global embrace of yoga as a holistic approach to well-being has accentuated the demand for refined and efficient techniques in yoga posture recognition. Traditional manual methods, although valuable, have exhibited protracted timelines and vulnerability to inaccuracies. In response, we introduce an innovative solution that harnesses the capabilities of deep learning (DL) models, elevating both the precision and accuracy of posture detection. Our approach predominantly leverages the Thunder variant of the MoveNet model, renowned for its exceptional proficiency in distinguishing an array of yoga postures. This model is seamlessly amalgamated with the MediaPipe technique, facilitating adept keypoint identification and skeletonization. In our proposed framework, input images undergo initial preprocessing, followed by skeletonization achieved through keypoint extraction. This pivotal process enables the encapsulation of distinctive points intrinsic to individual yoga poses. Central to our methodology is the incorporation of the large and diverse yoga (LDY) dataset, which encompasses five distinct yoga pose categories: Downdog, Goddess, Plank, Tree, and Warrior. A thorough evaluation demonstrates our approach's outstanding accuracy of 99.50% when deployed on the LDY dataset. As maintaining precise posture is pivotal in averting immediate discomfort and mitigating long-term health complexities, the implications of this advancement are profound. It charts a course toward more meticulous and accessible mechanisms for detecting yoga poses, thus profoundly influencing the physical and mental well-being of practitioners.

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“…As seen in Figure 4, to choose the proper human pose estimation tool, there are two performance evaluation for this recognition namely-mean average precision (mAP) and Percentage of Correct Key-points (PCK). To compute for Mean average precision, four key metrics were used (Confusion Matrix, Intersection-over-Union, precision and recall) [56].…”

mentioning

confidence: 99%

Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

2023

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Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points’ distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.

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A Wushu Posture Recognition System Based on MediaPipe

Cited by 14 publications

References 11 publications

Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start

Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start

Improved Yoga Pose Detection Using MediaPipe and MoveNet in a Deep Learning Model

Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

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