Development of motion capture system using multiple depth sensors

Sakata, Ryuichiro; Kobayashi, Futoshi; Nakamoto, Hiroyuki

doi:10.1109/mhs.2017.8305200

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Finally, the joint position was calculated from the skeleton line according to the joint structure of the human body. A curve skeleton expression based on the set of cross-section centroids was presented [ 54 ]. Patil et al [ 55 ] applied multiple inertial measurement unit (IMU) sensors, which were placed at the human joints to estimate the 3D position of the joints, the Lidar data compensate for displacement drift during the initial calibration of the skeleton structure.…”

Section: Methods Of Point Cloud-based Joint Estimationmentioning

confidence: 99%

A Review: Point Cloud-Based 3D Human Joints Estimation

Jia

et al. 2021

Sensors

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Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

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Section: Methods Of Point Cloud-based Joint Estimationmentioning

confidence: 99%

A Review: Point Cloud-Based 3D Human Joints Estimation

Jia

et al. 2021

Sensors

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“…Motion capture (MoCap) technology records the movement of objects or individuals, commonly utilized in computer graphics and animation to create lifelike character motions [26], [27]. In sign language translation process, it captures and analyzes the BISINDO gestures, then uses computer vision and image processing to track key points.…”

Section: Motion Capture and Sign Language Translatementioning

confidence: 99%

Enhancing interaction and learning experience for deaf students through sign language translator

Nugraha,

Faizah,

Zaenudin

2024

IJEECS

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The study addresses persistent communication barriers faced by students with disabilities, particularly the deaf, by exploring challenges, presenting breakthroughs, and introducing an innovative solution-a sign language translator (SLT) using motion capture technology. This groundbreaking technology, deployed through the ADDIE model and validated with user acceptance testing (UAT), successfully integrates into the learning management system (LMS) at SLB Bina Insani Depok, demonstrating its efficacy in bridging communication gaps. The results suggest a notable increase in efficiency for tasks such as t2, t3, and t5, highlighting the system’s improved ability to direct users to the LMS homepage, the SLT page, and translate words into sign language, respectively. The study suggests further development in advanced animation to enhance the learning experience for deaf students and recommends progressing toward the total communication (KOMTAL) system for comprehensive communication preparation, ultimately aiming to create an inclusive and dynamic learning platform for the holistic development of deaf students.

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