98%
921
2 minutes
20
The purpose of this cross-sectional study was to determine the precision and accuracy of the measurement of finger motion with a depth camera. Fifty-five healthy adult hands were included. Measurements were done with a depth camera and compared with traditional manual goniometer measurements. Repeated measuring showed that the overall repeatability and reproducibility of extension measured with the depth camera were within 3° and 4° and that of flexion were within 13° and 14°. Compared with traditional manual goniometry, biases of extension of all finger joints and flexion of metacarpophalangeal joints were less than 5°, and the average bias of flexion of proximal and distal interphalangeal joints was 29°. We conclude that the measurement of finger extension and flexion of the metacarpophalangeal joints with a depth camera was reliable, but improvement is required in the precision and accuracy of interphalangeal joint flexion.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/17531934221138924 | DOI Listing |
J R Soc Interface
September 2025
Institute of Intelligent Systems and Robotics, Sorbonne Université, Paris, Île-de-France, France.
A number of techniques have been developed to measure the three-dimensional trajectories of protists, which require special experimental set-ups, such as a pair of orthogonal cameras. On the other hand, machine learning techniques have been used to estimate the vertical position of spherical particles from the defocus pattern, but they require the acquisition of a labelled dataset with finely spaced vertical positions. Here, we describe a simple way to make a dataset of images labelled with vertical position from a single 5 min movie, based on a tilted slide set-up.
View Article and Find Full Text PDFPLoS One
September 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing, China.
Multi-modal data fusion plays a critical role in enhancing the accuracy and robustness of perception systems for autonomous driving, especially for the detection of small objects. However, small object detection remains particularly challenging due to sparse LiDAR points and low-resolution image features, which often lead to missed or imprecise detections. Currently, many methods process LiDAR point clouds and visible-light camera images separately, and then fuse them in the detection head.
View Article and Find Full Text PDFFront Plant Sci
August 2025
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, China.
With the development of smart agriculture, fruit picking robots have attracted widespread attention as one of the key technologies to improve agricultural productivity. Visual perception technology plays a crucial role in fruit picking robots, involving precise fruit identification, localization, and grasping operations. This paper reviews the research progress in the visual perception technology for fruit picking robots, focusing on key technologies such as camera types used in picking robots, object detection techniques, picking point recognition and localization, active vision, and visual servoing.
View Article and Find Full Text PDFData Brief
October 2025
School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar 13341, Mongolia.
Perception plays a crucial role in autonomous driving and computer vision, particularly in interpreting traffic scenes from monocular cameras. In this article, we present a comprehensive collection of traffic scene datasets organized into four distinct groups: (1) Traffic Scene Datasets, (2) Top-View Datasets - both introduced in the authors' earlier research, (3) MultiHeightView Datasets and (4) Depth Datasets. The Traffic Scene Datasets include RealStreet, which captures authentic traffic scenarios, and SynthStreet, its synthetic counterpart.
View Article and Find Full Text PDFNMR Biomed
October 2025
Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.
The slow acquisition time of MRI poses a significant barrier to its widespread clinical adoption. To address this limitation, we introduce CAMERA-Net, a novel multi-stage cascaded wavelet neural network for reconstruction of accelerated MRI. CAMERA-Net incorporates a wavelet transform-based regularization network that effectively leverages wavelet transforms and convolutional neural networks to preserve critical image information.
View Article and Find Full Text PDF