A framework of human impedance recognition

A framework for recognizing the human intention of human forearm is developed. For a cooperative task, friendly and safe interaction is a key issue when humans directly interaction with the robots. Therefore, estimating the dynamics and intention of the human hand are very meaningful in the human machine interaction. A human subject puts his hand on the force sensor when a haptic device sets force in the proposed framework, the measured force, the surface electromyographic signal and the motion of the hand are employed to estimate the parameters of human forearm's impedance. The performance and feasibility of developed framework are verified

Shape-based defect classification for Non Destructive Testing

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.Comment: 5 pages, IEEE International Worksho

Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

Graphene Quantum Dot-Based Electrochemical Immunosensors for Biomedical Applications

In the area of biomedicine, research for designing electrochemical sensors has evolved over the past decade, since it is crucial to selectively quantify biomarkers or pathogens in clinical samples for the efficacious diagnosis and/or treatment of various diseases. To fulfil the demand of rapid, specific, economic, and easy detection of such biomolecules in ultralow amounts, numerous nanomaterials have been explored to effectively enhance the sensitivity, selectivity, and reproducibility of immunosensors. Graphene quantum dots (GQDs) have garnered tremendous attention in immunosensor development, owing to their special attributes such as large surface area, excellent biocompatibility, quantum confinement, edge effects, and abundant sites for chemical modification. Besides these distinct features, GQDs acquire peroxidase (POD)-mimicking electro-catalytic activity, and hence, they can replace horseradish peroxidase (HRP)-based systems to conduct facile, quick, and inexpensive label-free immunoassays. The chief motive of this review article is to summarize and focus on the recent advances in GQD-based electrochemical immunosensors for the early and rapid detection of cancer, cardiovascular disorders, and pathogenic diseases. Moreover, the underlying principles of electrochemical immunosensing techniques are also highlighted. These GQD immunosensors are ubiquitous in biomedical diagnosis and conducive for miniaturization, encouraging low-cost disease diagnostics in developing nations using point-of-care testing (POCT) and similar allusive techniques.TU Berlin, Open-Access-Mittel - 201

Olfactory receptors for a smell sensor: A comparative study of the electrical responses of rat I7 and human 17-40

In this paper we explore relevant electrical properties of two olfactory receptors (ORs), one from rat OR I7 and the other from human OR 17-40, which are of interest for the realization of smell nanobiosensors. The investigation compares existing experiments, coming from electrochemical impedance spectroscopy, with the theoretical expectations obtained from an impedance network protein analogue, recently developed. The changes in the response due to the sensing action of the proteins are correlated with the conformational change undergone by the single protein. The satisfactory agreement between theory and experiments points to a promising development of a new class of nanobiosensors based on the electrical properties of sensing proteins.Comment: 6 pages, 7 figure