2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2013
ABSTRACT A pattern recognition based classification method is proposed to estimate wrist flexion ... more ABSTRACT A pattern recognition based classification method is proposed to estimate wrist flexion angles from electrical activities of forearm muscles. Spatial movement data and multi-channel myoelectric signals from forearm muscles were collected experimentally during periodic wrist flexion and extension movements using an ultrasound based movement analyser system. The recorded marker coordinates were transformed into joint angles using OpenSim, an open source simulation tool for biomechanical analysis. EMG data were segmented according to specific ranges of the calculated wrist flexion angle to form different classes for pattern recognition. The parameter space of the used classification algorithm was explored with a selected subset of values to find the optimal parameter vector giving maximal classification performance.
2014 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), 2014
ABSTRACT A hardware platform for real-time measurement and analysis of human electromyographic si... more ABSTRACT A hardware platform for real-time measurement and analysis of human electromyographic signals (EMG) is proposed. The system is designed to have modular structure to facilitate further integration of various measurement, signal processing and actuator control tasks. To assess reachable system performance in the target application of EMG classification, two different classification methods are tested on the Xilinx Zynq-7000 All Programmable SoC using offline recorded multichannel lower arm muscle signals.
Reaching movements are often used to study the effectiveness of motor control processes with resp... more Reaching movements are often used to study the effectiveness of motor control processes with respect to the final position of arm and hand. Empirical evidence shows that different targets can be grasped with similar final position accuracy. However, movements that achieve similar accuracy at their final position may nevertheless be controlled differently. In particular, control strategies may differ in the control of the abundant degrees of freedom with respect to the task-specific costs. The objective of the present study was to investigate whether the applied control strategy was influenced by the shape of the target to be grasped. It was investigated whether mechanical constraints, imposed on final hand orientation or final hand position by the shape of the targets, affected the synergistic coordination of the kinematic degrees of freedom of the arm. Subjects were asked to grasp either a cylindrical or a spherical target, which imposed different constraints on final hand orientation and position. Besides temporal movement aspects, variability of the joint angles of the arm, as well as variability of hand orientation and hand position was analyzed over the whole time course of movement execution, using the uncontrolled manifold method. Overall movement duration differed between cylindrical and spherical target condition, due to differences in deceleration duration. Reaching movements towards the cylindrical target, which was more constraint in final hand orientation and position, took longer than movements towards the spherical target. Analysis further revealed that the degrees of freedom of the arm were synergistically coordinated to stabilize both hand orientation and hand 0167-9457/$ -see front matter Ó
2013 European Conference on Circuit Theory and Design (ECCTD), 2013
ABSTRACT A platform design for the analysis of human myoelectric signals (MES) is presented. Offl... more ABSTRACT A platform design for the analysis of human myoelectric signals (MES) is presented. Offline recorded multichannel signals of forearm muscles are processed with a Field Programmable SoC in order to classify different movement patterns to control human-assisting electromechanical systems with multiple degrees of freedom (e.g. a prosthetic hand). Benchmark results of an ANSI C implementation are shown to assess the raw performance of the built-in ARM cores of the SoC. Possible computational bottlenecks are located based on the results and custom hardware implementations are shown to fully utilize the flexibility and performance of the used hardware platform.
2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2013
ABSTRACT A pattern recognition based classification method is proposed to estimate wrist flexion ... more ABSTRACT A pattern recognition based classification method is proposed to estimate wrist flexion angles from electrical activities of forearm muscles. Spatial movement data and multi-channel myoelectric signals from forearm muscles were collected experimentally during periodic wrist flexion and extension movements using an ultrasound based movement analyser system. The recorded marker coordinates were transformed into joint angles using OpenSim, an open source simulation tool for biomechanical analysis. EMG data were segmented according to specific ranges of the calculated wrist flexion angle to form different classes for pattern recognition. The parameter space of the used classification algorithm was explored with a selected subset of values to find the optimal parameter vector giving maximal classification performance.
2014 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), 2014
ABSTRACT A hardware platform for real-time measurement and analysis of human electromyographic si... more ABSTRACT A hardware platform for real-time measurement and analysis of human electromyographic signals (EMG) is proposed. The system is designed to have modular structure to facilitate further integration of various measurement, signal processing and actuator control tasks. To assess reachable system performance in the target application of EMG classification, two different classification methods are tested on the Xilinx Zynq-7000 All Programmable SoC using offline recorded multichannel lower arm muscle signals.
Reaching movements are often used to study the effectiveness of motor control processes with resp... more Reaching movements are often used to study the effectiveness of motor control processes with respect to the final position of arm and hand. Empirical evidence shows that different targets can be grasped with similar final position accuracy. However, movements that achieve similar accuracy at their final position may nevertheless be controlled differently. In particular, control strategies may differ in the control of the abundant degrees of freedom with respect to the task-specific costs. The objective of the present study was to investigate whether the applied control strategy was influenced by the shape of the target to be grasped. It was investigated whether mechanical constraints, imposed on final hand orientation or final hand position by the shape of the targets, affected the synergistic coordination of the kinematic degrees of freedom of the arm. Subjects were asked to grasp either a cylindrical or a spherical target, which imposed different constraints on final hand orientation and position. Besides temporal movement aspects, variability of the joint angles of the arm, as well as variability of hand orientation and hand position was analyzed over the whole time course of movement execution, using the uncontrolled manifold method. Overall movement duration differed between cylindrical and spherical target condition, due to differences in deceleration duration. Reaching movements towards the cylindrical target, which was more constraint in final hand orientation and position, took longer than movements towards the spherical target. Analysis further revealed that the degrees of freedom of the arm were synergistically coordinated to stabilize both hand orientation and hand 0167-9457/$ -see front matter Ó
2013 European Conference on Circuit Theory and Design (ECCTD), 2013
ABSTRACT A platform design for the analysis of human myoelectric signals (MES) is presented. Offl... more ABSTRACT A platform design for the analysis of human myoelectric signals (MES) is presented. Offline recorded multichannel signals of forearm muscles are processed with a Field Programmable SoC in order to classify different movement patterns to control human-assisting electromechanical systems with multiple degrees of freedom (e.g. a prosthetic hand). Benchmark results of an ANSI C implementation are shown to assess the raw performance of the built-in ARM cores of the SoC. Possible computational bottlenecks are located based on the results and custom hardware implementations are shown to fully utilize the flexibility and performance of the used hardware platform.
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Papers by Bence Borbely