A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB Images

Ji, Peng; Wang, Xianjian; Ma, Fengying; Feng, Jinxiang; Li, Chenglong

doi:10.3390/s22218410

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Linear regression is a classical regression model, whereas LightGBM offers improved computational efficiency, reduced memory occupancy, and enhanced classification accuracy, while preventing overfitting. It has been used earlier to estimate hand postures from RGB images [39]. The machine-learning library Scikit-learn in Python was used for model training, and the workflow of the current experiment is illustrated in Figure 6.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model

Kusunose

Inui

Nishimoto

et al. 2023

Sensors

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Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries—MediaPipe and LightGBM—were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, −15°, or −30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R2 and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model

Kusunose

Inui

Nishimoto

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…For 3D hand attitude estimation using dual-view RGB images, Ji et al Ji et al (2022) propose a method combining deep learning and ensemble learning. The approach involves constructing a 3D hand posture dataset using dual-view cameras and an IMU sensor, training CNN-based image feature extractors, and performing attitude regression using LightGBM.…”

Section: Ta B L E 1 3 Imu Datasetsmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

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This review article presents recent advancements in deep learning methodologies and applications for autonomous nav- igation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, ob- stacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limita- tions, and areas for further development. The review em- phasizes the interdisciplinary nature of autonomous naviga- tion and addresses challenges posed by dynamic and com- plex environments, uncertainty, and obstacles. With a par- ticular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the impor- tance of navigation in these domains. By synthesizing find- ings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, re- cent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable re- source for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

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Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Asgharpoor

Sabour

2023

Preprint

View full text Add to dashboard Cite

This review article presents recent advancements in deep learning method-ologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying , evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.

show abstract

A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB Images

Cited by 3 publications

References 36 publications

Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model

Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

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