Real-time Joint Angle Estimation using Mediapipe Framework and Inertial Sensors

Palani, Poongavanam; Panigrahi, Siddhant; Jammi, Sai Abhinav; Thondiyath, Asokan

doi:10.1109/bibe55377.2022.00035

Cited by 17 publications

(4 citation statements)

References 14 publications

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“…Computer vision and machine learning techniques are incredibly useful for creating affordable motion-capturing systems. Utilizing software-based processing methods can significantly decrease the expenses associated with using costly hardware but inherits challenging tasks like calibration of camera networks and data validation of using commercial setups [ 96 , 97 ]. Nevertheless, ensuring that the footage remains clear and stable may prove to be quite challenging.…”

Section: Discussionmentioning

confidence: 99%

Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players

Maduwantha,

Jayaweerage,

Kumarasinghe

et al. 2024

Sensors

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Motion Capture (MoCap) has become an integral tool in fields such as sports, medicine, and the entertainment industry. The cost of deploying high-end equipment and the lack of expertise and knowledge limit the usage of MoCap from its full potential, especially at beginner and intermediate levels of sports coaching. The challenges faced while developing affordable MoCap systems for such levels have been discussed in order to initiate an easily accessible system with minimal resources.

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Section: Discussionmentioning

confidence: 99%

Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players

Maduwantha,

Jayaweerage,

Kumarasinghe

et al. 2024

Sensors

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show abstract

“…These neural network algorithms first gained attention in 2012 ( Krizhevsky, Sutskever & Hinton, 2012 ), where it could generate meaningful inferences from audio signals and visual images by speech recognition and image classification. However, recent advancements leading to more robust and efficient neural networks have made these algorithms applicable to a broader range of real-world problems such as traffic surveillance ( Tao et al, 2017 ), crowd monitoring ( Zhao et al, 2019 ), biomedical image analysis ( Kim et al, 2020 ), crop disease detection ( Duhart et al, 2019 ), and rehabilitation tasks ( Palani et al, 2022 ). DL algorithms have also been applied in wildlife settings to study pose detection and species identification.…”

Section: Background and Related Researchmentioning

confidence: 99%

Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms

Panigrahi,

Maski,

Thondiyath

2023

PeerJ Computer Science

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Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the “capture, mark and recapture” technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem.

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“…Motion counting has been augmented with artificial intelligence technology by Sikdar et al [11]. Palani et al [12] provided real-time joint angle estimates based on the Mediapipe framework and inertia sensors for the analysis of human motion techniques. Kwon and Dongho [13] utilized posture detection during exercise in real time.…”

Section: Introductionmentioning

confidence: 99%

Ballet Form Training Based on MediaPipe Body Posture Monitoring

Lei,

Wang,

Chen

2023

J. Phys.: Conf. Ser.

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People are increasingly turning to the cloud in the context of “healthy China” to engage in online exercise. The use of artificial intelligence technology to address broad population health-related challenges has become increasingly important as information technology has matured. The MediaPipe artificial intelligence framework, which Google recently released, is used in this article to optimize video feedback and support the “cloud movement” of widespread home ballet instruction in order to examine the effects of digital technology-enabled ballet training on the general improvement of physical health. Based on the experiment’s findings, trainers can use MediaPipe video feedback as an additional method of “cloud movement” training in public homes. This allows trainers to visually reflect on any issues that arise during the exercise process and to promptly modify training according to motion monitoring in order to reduce the risk of physical injury brought on by improper motion training. It is possible to envision a day in the future when video feedback built on the MediaPipe architecture would combine artificial intelligence with exercise training to achieve training objectives that enhance the accuracy of trainers’ motions and enhance physical balance.

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Real-time Joint Angle Estimation using Mediapipe Framework and Inertial Sensors

Cited by 17 publications

References 14 publications

Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players

Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players

Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms

Ballet Form Training Based on MediaPipe Body Posture Monitoring

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