3 research outputs found
Learning to Estimate 3D Human Pose from Point Cloud
3D pose estimation is a challenging problem in computer vision. Most of the
existing neural-network-based approaches address color or depth images through
convolution networks (CNNs). In this paper, we study the task of 3D human pose
estimation from depth images. Different from the existing CNN-based human pose
estimation method, we propose a deep human pose network for 3D pose estimation
by taking the point cloud data as input data to model the surface of complex
human structures. We first cast the 3D human pose estimation from 2D depth
images to 3D point clouds and directly predict the 3D joint position. Our
experiments on two public datasets show that our approach achieves higher
accuracy than previous state-of-art methods. The reported results on both ITOP
and EVAL datasets demonstrate the effectiveness of our method on the targeted
tasks
Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels Neuromodulatio
Measuring Behavior 2018 Conference Proceedings
These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions