The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

Fall Detection Based on Body Part Tracking Using a Depth Camera

We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method. The key feature of our method is that it jointly solves for both the skeleton and graph-node deformations based on information of the attachments between the skeleton and the graph nodes. The attachments are also updated frame by frame based on the fused surface geometry and the computed deformations. Overall, our method enables increasingly denoised, detailed, and complete surface reconstruction as well as the updating of the skeleton and attachments as the temporal depth frames are fused. Experimental results show that our method exhibits substantially improved nonrigid motion fusion performance and tracking robustness compared with previous state-of-the-art fusion methods. We also contribute a dataset for the quantitative evaluation of fusion-based dynamic scene reconstruction algorithms using a single depth camera.

BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera

An adaptable system for RGB-D based human body detection and pose estimation

This paper introduces the new robot programming language LightRocks(Light Weight Robot Coding for Skills), a domain specific language (DSL) for robot programming. The language offers three different level of abstraction for robot programming. On lowest level skills are coded by domain experts. On a more abstract level these skills are supposed to be combined by shop floor workers or technicians to define tasks. The language is designed to allow as much flexibility as necessary on the lowest level of abstraction and is kept as simple as possible with the more abstract layers. A Statechart like model is used to describe the different levels of detail. For this we apply the UML/P and the language workbench MontiCore. To this end we are able to generate code while hiding controller specific implementation details. In addition the development in LightRocks is supported by a generic graphical editor implemented as an Eclipse plugin.

A new skill based robot programming language using UML/P Statecharts

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.

3D Human Pose Estimation in RGBD Images for Robotic Task Learning

The advent of inexpensive RGB-D cameras brings new opportunities to capture a 3D environment. This paper presents a method to create a modular setup for generating a large 3D point cloud, with attention to the study of interference, the influence of a USB extension cable, and the calibration procedure. The study of interference includes the influence of the distance between the cameras, the orientation of the cameras, and the illumination. Furthermore, this paper proposes a number of evaluation metrics for similar setups.

Multi RGB-D camera setup for generating large 3D point clouds

Automatic Generation of Personalized Human Models based on Body Measurements

A growing number of robotics labs cooperate in the "Blender for Robotics" project [4], to integrate the open source computer animation program Blender into their research. In the first place as a visualisation tool, but gradually more and more as a full-fledged component in a robot simulation and programming environment. The lab in Leuven focuses on improving Blender support for the task specification and control of complex, sensor-based robot tasks, in which also the motion of the human(s) interacting with the task must be tracked.

Koen Buys

Papers

An adaptable system for RGB-D based human body detection and pose estimation

Multi RGB-D camera setup for generating large 3D point clouds

Automatic Generation of Personalized Human Models based on Body Measurements

Blender for robotics: integration into the Leuven paradigm for robot task specification and human motion estimation

On-line Generation of Customized Human Models based on Camera Measurements