Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Computer science. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Towards an EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning System

Abstract: This paper proposes a novel method of using electromyographic (EMG) potentials generated by the forearm muscles during hand and finger movements to control an artificial prosthetic hand worn by an amputee. Surface EMG sensors were used to record a sequence of forearm EMG potential signals via a PC sound card and a novel 3-D electromagnetic positioning system together with a data-glove mounted with 11 miniature electromagnetic sensors used to acquire corresponding human hand pose in real time. The synchronized measurements of hand posture and associated EMG signals stored as prototypes embody a numerical expression of the current hand shape in the form of a series of data frames, each comprising a set of postures and associated EMG data. This allows a computer generated graphical 3-D model, combined with synthesized EMG signals, to be used to evaluate the approach. This graphical user interface could also enable handicapped users to practice controlling a robotic prosthetic hand using EMG signals derived from their forearm muscles. We believe this task might be made easier using a dictionary of stored task-specific prototype data frames acquired from able-bodied users. By comparing the resulting EMG data frames with stored prototypes, the most likely data frame sequence can be identified and used to control a robotic hand so that it carries out the user's desire. We explore the feasibility of this approach by applying frequency analysis on the signal derived from a multichannel EMG measurement device and identify pattern recognition techniques in the time and frequency domains to determine plausible hand shapes. This approach offers several advantages over existing methods. First, it simplifies the classification procedure, saving computational time and the requirement for the optimization process, and second, it increases the number of recognizable hand shapes, which in turn improves the dexterity of the prosthetic hand and the quality of life for amputees. The database of EMG prototypes could be employed to optimize the accuracy of the system within a machine learning paradigm. By making a range of EMG prototype databases available, prosthetic hand users could train themselves to use their prosthesis using the visual reference afforded by the virtual hand model to provide feedback

Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An alternative approach

Abstract: Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors.

New technique of nondestructive assessment of concrete strength using artificial intelligence

Abstract: A new technique of nondestructively assessing the compression strength of concrete, which employs artificial neural networks, is presented. A data set based on results of testing concretes (with a 28-day strength of 24–105 MPa) by different nondestructive methods was used to train and test an artificial neural network. The methodology of neural identification of the strength is described. The obtained results, including those of the practical verification of the technique, are reported.