A Three-dimensional Finger Motion Measurement System of a Thumb and an Index Finger Without a Calibration Process

Park, Yeongyu; Bae, Joonbum

doi:10.3390/s20030756

Cited by 23 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can also control the signing speed, frames-per-second of videos and image dimension. The range of variations is constrained to be within anatomical statistical limits, specifically we used those reported by [Park and Bae, 2020]. Models can be trained with the resulting skeletal images ("wireframes") constructed from the pose coordinates following this augmentation stage.…”

Section: Pre-processing Techniquesmentioning

confidence: 99%

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Aleem¹,

Kumar²,

Little³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

View full text Add to dashboard Cite

Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.

show abstract

Section: Pre-processing Techniquesmentioning

confidence: 99%

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Aleem¹,

Kumar²,

Little³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

View full text Add to dashboard Cite

show abstract

“…Nevertheless, these methods are limited by hand gestures signifi cantly. For instance, it is hard to recognize one fi nger when it is shielded by another [3]. Moreover, multiple devices fi lming or photographing hand motions are not suitable in some cases.…”

Section: Introductionmentioning

confidence: 99%

A Fabric-Based Integrated Sensor Glove System Recognizing Hand Gesture

Han

Miao

Liu

et al. 2021

Autex Research Journal

View full text Add to dashboard Cite

The research on wearable glove sensor system has been increasing over recent years because of the need for portability and comfort. This study presents a fabric-based integrated sensor glove system with five sensing zones. Five sensors are knitted by silver-plated nylon yarn and embedded into glove directly using intarsia technology. Various parameters including sensor elasticity, sizes of embedded sensor as well as glove are discussed, respectively. Further, selected or chosen integrated sensor glove is manufactured and tested for recognizing gestures. Results show that elasticity affects effective sensing range of knitted sensors, size has significant influence on sensors’ sensitivity, and appropriate glove size helps avoiding nonlinear sensing phenomenon. Finally, the glove system, by extracting feature data, can distinguish Chinese number gestures very well and has also the potential to recognize more hand gestures in the future.

show abstract

“…Such being the case, miniaturised position (angle) measurement sensors have been proposed by scientists that directly integrate into joints of robotic applications [5]. The article [8] shows an angle measurement sensor fabrication combining a hall sensor and a single bar magnet. The hall sensor measures magnetic flux intensity variations while the magnet is being rotated, converting them to voltage variations [9] [8].…”

Section: Introductionmentioning

confidence: 99%

“…The article [8] shows an angle measurement sensor fabrication combining a hall sensor and a single bar magnet. The hall sensor measures magnetic flux intensity variations while the magnet is being rotated, converting them to voltage variations [9] [8]. Through calibration voltages can be converted to physical quantities, and rotation angles can be estimated.…”

Section: Introductionmentioning

confidence: 99%

An Optical Joint Angle Measurement Sensor based on an Optoelectronic Sensor for Robot Manipulators

Osman

et al. 2020

2020 8th International Conference on Control, Mechatronics and Automation (ICCMA)

View full text Add to dashboard Cite

this paper presents the design of a joint angle measurement sensor using optoelectronic reflective sensors for use with robotic applications. It offers a minimised solution to shape sensing that can easily be integrated into robotic joints. A mathematical model used to characterize sensor behaviour is presented, and validated experimentally. A single unit of a robotic jointed finger is designed, and the sensing principle is tested with rotational motion. Results are presented and compared to simulated values. While the mathematical model can accurately estimate output signal with varying displacements, limitations of angle range means an updated sensor model is required, with addition of real electrical properties such as saturation of signal and resistance of electrical components. Regression fitting method is therefore done and a third order function was able to accurately estimate the output signal over a simulated sweep of joint angle values.

show abstract

A Three-dimensional Finger Motion Measurement System of a Thumb and an Index Finger Without a Calibration Process

Cited by 23 publications

References 17 publications

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

A Fabric-Based Integrated Sensor Glove System Recognizing Hand Gesture

An Optical Joint Angle Measurement Sensor based on an Optoelectronic Sensor for Robot Manipulators

Contact Info

Product

Resources

About