Simultaneous Estimation of Hand Configurations and Finger Joint Angles using Forearm Ultrasound

• URL: http://arxiv.org/abs/2211.15871v1
• Date: Tue, 29 Nov 2022 02:06:19 GMT
• Title: Simultaneous Estimation of Hand Configurations and Finger Joint Angles using Forearm Ultrasound
• Authors: Keshav Bimbraw, Christopher J. Nycz, Matt Schueler, Ziming Zhang, and Haichong K. Zhang
• Abstract summary: Forearm ultrasound images provide a musculoskeletal visualization that can be used to understand hand motion. We propose a CNN based deep learning pipeline for predicting the MCP joint angles. A low latency pipeline has been proposed for estimating both MCP joint angles and hand configuration aimed at real-time control of human-machine interfaces.
• Score: 8.753262480814493
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion recognition is widely used to enable these interactions. Hand configuration classification and MCP joint angle detection is important for a comprehensive reconstruction of hand motion. sEMG and other technologies have been used for the detection of hand motions. Forearm ultrasound images provide a musculoskeletal visualization that can be used to understand hand motion. Recent work has shown that these ultrasound images can be classified using machine learning to estimate discrete hand configurations. Estimating both hand configuration and MCP joint angles based on forearm ultrasound has not been addressed in the literature. In this paper, we propose a CNN based deep learning pipeline for predicting the MCP joint angles. The results for the hand configuration classification were compared by using different machine learning algorithms. SVC with different kernels, MLP, and the proposed CNN have been used to classify the ultrasound images into 11 hand configurations based on activities of daily living. Forearm ultrasound images were acquired from 6 subjects instructed to move their hands according to predefined hand configurations. Motion capture data was acquired to get the finger angles corresponding to the hand movements at different speeds. Average classification accuracy of 82.7% for the proposed CNN and over 80% for SVC for different kernels was observed on a subset of the dataset. An average RMSE of 7.35 degrees was obtained between the predicted and the true MCP joint angles. A low latency (6.25 - 9.1 Hz) pipeline has been proposed for estimating both MCP joint angles and hand configuration aimed at real-time control of human-machine interfaces.

Related papers

• Hand Gesture Classification Based on Forearm Ultrasound Video Snippets Using 3D Convolutional Neural Networks [2.1301560294088318]
  Forearm ultrasound offers detailed information about muscle morphology changes during hand movement which can be used to estimate hand gestures. Previous work has focused on analyzing 2-Dimensional (2D) ultrasound image frames using techniques such as convolutional neural networks (CNNs) This study uses 3D CNN based techniques to capture temporal patterns within ultrasound video segments for gesture recognition.
  arXiv  Detail & Related papers  (2024-09-24T19:51:41Z)
• Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense [0.6390468088226495]
  We focus on'skeletal' data represented by the body joint coordinates, from the Praxis dataset. The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease. Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8%.
  arXiv  Detail & Related papers  (2023-11-01T18:18:09Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique [2.534406146337704]
  Non-contact hand pose and static gesture recognition have received considerable attention in many applications. This article presents an efficient data collection approach and a novel technique for deep CNN training by introducing sterile'' images. Applying the proposed data collection and training methods yields an increase in classification rate of static hand gestures from $85%$ to $93%$.
  arXiv  Detail & Related papers  (2023-05-03T11:10:50Z)
• 4D-OR: Semantic Scene Graphs for OR Domain Modeling [72.1320671045942]
  We propose using semantic scene graphs (SSG) to describe and summarize the surgical scene. The nodes of the scene graphs represent different actors and objects in the room, such as medical staff, patients, and medical equipment. We create the first publicly available 4D surgical SSG dataset, 4D-OR, containing ten simulated total knee replacement surgeries.
  arXiv  Detail & Related papers  (2022-03-22T17:59:45Z)
• Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation [12.49914980193329]
  We study the inference problem of identifying the activation of specific fingers from a sequence of US images. We consider this task as an important step towards higher adoption rates of robotic prostheses among arm amputees.
  arXiv  Detail & Related papers  (2022-02-10T18:05:09Z)
• Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [65.78703941973183]
  We propose a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder. Specifically, the CD-JBF-GC can explore the motion transmission between the joint stream and the bone stream. The pose prediction based auto-encoder in the self-supervised training stage allows the network to learn motion representation from unlabeled data.
  arXiv  Detail & Related papers  (2022-02-08T16:03:15Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation [0.5635300481123077]
  Current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing and fine motion. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery.
  arXiv  Detail & Related papers  (2021-02-21T11:21:30Z)
• Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots [129.46920552019247]
  We propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
  arXiv  Detail & Related papers  (2021-02-09T10:34:32Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.