Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

Park, Ho-Jun; Baek, Jang-Woon; Kim, Jong-Hwan

doi:10.1109/case48305.2020.9216833

Cited by 18 publications

(4 citation statements)

References 11 publications

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“…From our own review, we conclude that OpenPose is the one that has generated more literature works and seems to have the largest community of developers. OpenPose has been used in multiple areas: sports [87], telerehabilitation [88], HAR [89][90][91], artistic disciplines [92], identification of multi-person groups [93], and VR [94]. Thus, we have selected OpenPose algorithm for this work due to the following reasons: (i) it is open source, (ii) it can be applied in real situations with new video inputs [84], (iii) there is a large number of projects available with code and examples, (iv) it is widely reported in scientific papers, (v) there is a strong developers community, and (vi) the API gives users the flexibility of selecting source images from camera fields, webcams, and others.…”

Section: Methodsmentioning

confidence: 99%

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Echeverria

Santos

2021

Sensors

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Technological advances enable the design of systems that interact more closely with humans in a multitude of previously unsuspected fields. Martial arts are not outside the application of these techniques. From the point of view of the modeling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined, or at least, bounded, and governed by the laws of Physics. Their execution must be learned after continuous practice over time. Literature suggests that artificial intelligence algorithms, such as those used for computer vision, can model the movements performed. Thus, they can be compared with a good execution as well as analyze their temporal evolution during learning. We are exploring the application of this approach to model psychomotor performance in Karate combats (called kumites), which are characterized by the explosiveness of their movements. In addition, modeling psychomotor performance in a kumite requires the modeling of the joint interaction of two participants, while most current research efforts in human movement computing focus on the modeling of movements performed individually. Thus, in this work, we explore how to apply a pose estimation algorithm to extract the features of some predefined movements of Ippon Kihon kumite (a one-step conventional assault) and compare classification metrics with four data mining algorithms, obtaining high values with them.

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Section: Methodsmentioning

confidence: 99%

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Echeverria

Santos

2021

Sensors

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“…Chen et al [11] studied falling behavior among the elderly, considering the standing-up mechanism of the human body after a fall. Park et al [12] developed a real-time push-up counter that can distinguish those performed correctly from those performed incorrectly. Jafarzadeh et al [13] analyzed, in real time, the key points for a hurdles athlete.…”

Section: Literature Reviewmentioning

confidence: 99%

Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script

Barberi,

Chillemi,

Cucinotta

et al. 2023

Sensors

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Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider’s posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the perception of risk and the handling of the motorcycle. It is important for motorcyclists to adopt a good riding posture, for their health and road safety. The aim of this work is to propose a fast, cheap, and sufficiently robust method for the 3D reconstruction of the posture assumed by a motorcyclist. The stereo vision and the application of OpenPose made it possible to obtain a 3D reconstruction of the key points, and their evolution over time. The evaluation of the distances between the 3D key points, which represent the length of the various parts of the body, appears to remain sufficiently stable over time, and faithful to the real distances, as taken on the motorcyclist themself. The 3D reconstruction obtained can be applied in different fields: ergonomics, motorsport training, dynamics, and fluid dynamics analysis.

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“…The majority of deep learning studies and startups have targeted recreational gym-goers [145,850]. For example, algorithms and models have been developed for exercise recognition [401,587,646,651,747], automatic training diary logging [359,403,774], repetition counting [27,639], adherence monitoring [103], and real-time exercise technique evaluation and correction [117,133,254,349,371,372,589,607,610,845,900].…”

Section: Deep Learning Enabled Exercise and Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

Cited by 18 publications

References 11 publications

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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