AIMC Topic: Hand

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Quick and accurate selection of hand images among radiographs from various body parts using deep learning.

Journal of X-ray science and technology
BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs...

Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.

Journal of digital imaging
Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model t...

Enhancing Precision in Gesture Detection for Hand Recovery After Injury Using Leap Motion and Machine Learning.

Studies in health technology and informatics
This paper presents an improved solution for detecting gestures with a better precision using the Leap Motion sensor and Machine Learning support. A neural network is trained to recognize a hand rotation gesture expressing the grade of recovery, with...

A Novel Underactuated Robotic Finger with Variable Stiffness Joints.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Existing robotic hands mostly consist of rigid finger mechanism with constant joint stiffness, leading to poor handling performance and even unexpected safety issues. This paper proposed a novel underactuated robotic finger with variable stiffness jo...

A Recurrent Neural Network for Hand Gesture Recognition based on Accelerometer Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
For many applications, hand gesture recognition systems that rely on biosignal data exclusively are mandatory. Usually, theses systems have to be affordable, reliable as well as mobile. The hand is moved due to muscle contractions that cause motions ...

Classification and Assessment of Hand Arthritis Stage using Support Vector Machine.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, usi...

Recurrent Neural Network as Estimator for a Virtual sEMG Channel.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurrent Neural Network (RNN) by using Long Short-Term Memory (LSTM) nodes. The virtual channel is used to classify hand postures from the publicly NinaPro dat...

Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive ...

Hand and Object Segmentation from Depth Image using Fully Convolutional Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Semantic segmentation is an important step for hand and object tracking as subsequent tracking algorithms depend heavily on the accuracy of the segmented hand and object. However, current methods for hand and object segmentation are limited in the nu...

Detection of Abnormal Segments in Finger Tapping Waveform using One-class SVM.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We have developed a finger-tapping device with magnetic sensors, UB2, for the early detection of dementia. The goal of the present study is to develop a method for detecting abnormal segments in the finger tapping waveform in an objective way using m...