BLAINDER-A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data.

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

Analysis and Quantification of Repetitive Motion in Long-Term Rehabilitation.

Objective assessment in long-term rehabilitation under real-life recording conditions is a challenging task. We propose a data-driven method to evaluate changes in motor function under uncontrolled, long-term conditions with the low-cost Microsoft Kinect sensor. Instead of using human ratings as ground truth data, we propose kinematic features of hand motion, healthy reference trajectories derived by principal component regression, and methods taken from machine learning to analyze the progression of motor function. We demonstrate the capability of this approach on datasets with repetitive unrestrained bi-manual drumming movements in three-dimensional space of stroke survivors, patients suffering of Parkinson's disease, and a healthy control group. We present processing steps to eliminate the influence of varying recording setups under real-life conditions and offer visualization methods to support clinicians in the evaluation of treatment effects.

Reversible binding of dioxygen by the copper(I) complex with tris(2-dimethylaminoethyl)amine (Me6tren) ligand.

At low temperatures, the mononuclear copper(I) complex of the tetradentate tripodal aliphatic amine Me(6)tren (Me(6)tren = tris(2-dimethylaminoethyl)amine) [Cu(I)(Me(6)tren)(RCN)](+) first reversibly binds dioxygen to form a 1:1 Cu-O(2) species which further reacts reversibly with a second [Cu(I)(Me(6)tren)(RCN)](+) ion to form the dinuclear 2:1 Cu(2)O(2) adduct. The reaction can be observed using low temperature stopped-flow techniques. The copper superoxo complex as well as the peroxo complex were characterized by resonance Raman spectroscopy. The spectral characteristics and full kinetic and thermodynamic results for the reaction of [Cu(I)(Me(6)tren)(RCN)](+) with dioxygen are reported.