Flakë Bajraktari

Abstract

This study explores the potential of using a sensor system and deep learning algorithm for automatic recognition and classification of human movement intentions to enhance the control of active orthopedic aids such as orthoses, prostheses, and exoskeletons. The sensor system consists of four inertial measurement units (IMU) attached to the thighs, lower back, and chest of a person, measuring acceleration and rotational velocity in three axes each. A dataset was generated from 20 healthy individuals performing various movements from a standing position, and preprocessed and segmented into time windows. The data was fed into a convolutional neural network (CNN) capable of classifying the time windows into seven classes. The network achieved a classification accuracy of up to 82 %. The results demonstrate the potential of the proposed system for successful application in the control of assistance systems, which can improve the operability and effectiveness of orthopedic aids.

Abstract

In this paper, the development of a cost-effective assistance system for venipuncture is presented. The system locates forearm veins through near-infrared imaging, depth estimation, deep learning segmentation, and 3D reconstruction. A single-board computer was integrated with two infrared cameras and two 760 nm near-infrared (NIR) LEDs to capture and process stereo images. The depth estimation was achieved through stereo triangulation. A deep learning model based on the U-Net architecture with an attention mechanism and a training dataset of 900 images from 40 participants was used for vein segmentation. Depth information and segmented veins were combined to enable a 3D visualization of the veins. The results show a Jaccard-Score of 92.80 % for vein segmentation and an average reprojection error of 0.48 pixels for the 3D reconstruction.