Quantifying cardiorespiratory thorax movement with motion capture and deconvolution

Unobtrusive sensing is a growing aspect in the field of biomedical engineering. While many modalities exist, a large fraction of methods ultimately relies on the analysis of thoracic movement. To quantify cardiorespiratory induced thorax movement with spatial resolution, an approach using high-performance motion capture, electrocardiography and deconvolution is presented. In three healthy adults, motion amplitudes are estimated that correspond to values reported in the literature. Moreover, two-dimensional mappings are created that exhibit physiological meaningful relationships. Finally, the analysis of waveform data obtained via deconvolution shows plausible pulse transit behavior

Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics

Motivation: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions. Results: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset

Design and First Operation of an Active Lower Limb Exoskeleton with Parallel Elastic Actuation

The lower limb exoskeleton investigated in this work actively supports the knee and hip and is intended to provide full motion support during gait. Parallel elastic actuators are integrated into the hip joints to improve the energy efficiency in gait. The prototype was tested in sit-to-stand and gait trials, in which the actuators were cascade-controlled with position trajectories. The compliant actuation of the hip in gait experiments proved to be more efficient; the peak torque was reduced by up to 31% and the RMS power was reduced by up to 36%

Design and Analysis of a Clutched Parallel Elastic Actuator

Various actuator topologies are discussed for the purpose of powering periodic processes and particularly walking robots. The Clutched Parallel Elastic Actuator (CPEA) is proposed to reduce the energy consumption of active exoskeletons. A nonlinear model of the CPEA is presented in addition to the mechanical design. The CPEA prototype is operated with a passive load on the walking trajectory of the hip joint. The actuator is controlled with a cascaded position control and a superimposed Iterative Learning Controller (ILC). The controller was chosen to ensure comparability between active and deactivated spring operation. The application of the CPEA has the potential to increase efficiency in the design of exoskeletons