Robust Human Pose Estimation and Action Recognition Utilizing Feature Extraction

Sheng Luo; Rashid Abbasi; Hao Wang; Jinhua Xu; Dongyang Lyu; Fan Zhang; Farhan Amin; Isabel de la Torre; Gerardo Mendez Mezquita; Henry Fabian Gongora

doi:10.32604/cmes.2026.075080

January 1, 2026Computer Modeling in Engineering & SciencesOpen Access

Robust Human Pose Estimation and Action Recognition Utilizing Feature Extraction

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Key Points

This research aims to enhance human pose estimation and action recognition through the integration of inertial measurement units and monocular vision methods.
Utilized inertial measurement units (IMUs) alongside monocular vision techniques for data collection.
Applied feature extraction techniques to enhance the accuracy of human pose estimation.
Conducted comparative analysis of the proposed method against existing techniques.
Demonstrated improved accuracy in pose estimation compared to traditional methods.
Achieved reliable action recognition with minimal latency, enhancing real-time applications.
Highlighted the potential for integration in diverse fields, including healthcare and human-computer interaction.

Implication

Research integrates inertial measurement units for improved pose estimation and action recognition, suggesting advanced applications in various fields.

Abstract

Human pose estimation is crucial across diverse applications, from healthcare to human–computer interaction. Integrating inertial measurement units (IMUs) with monocular vision methods holds great potential for leveraging c... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Luo et al. (2026) studied this question.

synapsesocial.com/papers/69cd7ac55652765b073a83db https://doi.org/10.32604/cmes.2026.075080

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Robust Human Pose Estimation and Action Recognition Utilizing Feature Extraction

Key Points

Implication

Abstract

Discussion

Cite This Study

Robust Human Pose Estimation and Action Recognition Utilizing Feature Extraction

Key Points

Implication

Abstract

Discussion

Cite This Study

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