Pose Estimation and Joint Angle Detection Using Mediapipe Machine Learning Solution

Mitrović, Katarina; Milošević, Dušan

doi:10.1007/978-3-031-29717-5_8

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Muscle activation can be used to determine muscle fatigue [ 8 , 9 , 10 ] or can be an indicator of an illness for diagnostic purpose [ 11 , 12 , 13 ]. Usually, this is performed using surface electromyography (sEMG), but recent studies show the possibility of utilizing other sensors, such as an inertial measurement unit (IMU) [ 14 , 15 , 16 ], or contactless options with use of motion capture, computer vision, and deep learning pose estimation [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing

Grzejszczak,

Roksela,

Poświata

et al. 2024

Sensors

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In this paper, surface electromyography (sEMG) is used to gather the activation neural signal from muscles during an indoor rowing exercise. The exercise was performed by professional athletes and amateur non-athletes. The data acquisition and processing are described to obtain a set of parameters: number of cycles, average cycle time, cycle time standard deviation, fatigue time, muscle activation time, and muscle energy. These parameters are used to draw conclusions on common non-athletes’ mistakes during exercise for better training advice and a way of statistically distinguishing an athlete from a non-athlete.

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Section: Introductionmentioning

confidence: 99%

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing

Grzejszczak,

Roksela,

Poświata

et al. 2024

Sensors

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A pose estimation for motion tracking of infants cerebral palsy

Ali,

Mohamed

2024

Multimed Tools Appl

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The General Movements Analysis (GMA) has demonstrated noteworthy promise in the early detection of infantile Cerebral Palsy (CP). However, it is subjective and requires highly trained clinicians, making it costly and time-consuming. Automation of GMA could potentially enhance accessibility and further our comprehension of infants’ full-body movements. This paper investigates the feasibility of using 2D and 3D pose estimation strategies to observe and scrutinize the infant’s comprehensive body movement attributes to improve our perspective to consider joint movement and positions over time as an alternative to GMA for early CP prediction. The study includes comprehensive movement analysis from video recordings for accurate and efficient analysis of infant movement by computing various metrics such as angle orientations at different predicted joint locations, postural information, postural variability, movement velocity, movement variability, and left–right movement coordination. Along with antigravity movements are assessed and tracked as indicators of CP. We employed a variety Machine Learning (ML) algorithms for CP classification based on a series of robust features that have been developed to enhance the interpretability of the model. The proposed approach is assessed through experimentation using the MINI-RGBD and RVI-38 datasets with a classification accuracy of 92% and 97.37% respectively. These results substantiate the efficacy of employing pose estimation techniques for the precocious prediction of infantile CP, highlighting the importance of monitoring changes in joint angles over time for accurate diagnosis and treatment planning.

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Detection of Teachers' Smoking Behavior in Classroom Based on YOLO and MediaPipe

Min,

Liu,

Liu

2024

Proceedings of the 2024 9th International Conference on Distance Education and Learning

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Pose Estimation and Joint Angle Detection Using Mediapipe Machine Learning Solution

Cited by 3 publications

References 11 publications

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing

A pose estimation for motion tracking of infants cerebral palsy

Detection of Teachers' Smoking Behavior in Classroom Based on YOLO and MediaPipe

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