A minimal control schema for goal-directed arm movements based on physiological inter-joint coupling

Bockemühl T, Dürr V. A minimal control schema for goal-directed arm movements based on physiological inter-joint coupling. In: Proceedings of the International Conference on Neural Computation (ICNC 2010, Valencia, Spain). 2010.Substantial evidence suggests that nervous systems simplify motor control of complex body geometries by use of higher level functional units, so called motor primitives or synergies. Although simpler, such high level functional units still require an adequate controller. In a previous study, we found that kinematic inter-joint couplings allow the extraction of simple movement synergies during unconstrained 3D catching movements of the human arm and shoulder girdle. Here, we show that there is a bijective mapping between movement synergy space and 3D Cartesian hand coordinates within the arm's physiological working range. Based on this mapping, we propose a minimal control schema for a 10-DoF arm and shoulder girdle. All key elements of this schema are implemented as artificial neural networks (ANNs). For the central controller, we evaluate two different ANN architectures: a feed-forward network and a recurrent Elman network. We show that this control schema is capable of controlling goal-directed movements of a 10-DoF arm with as few as five hidden units. Both controller variants are sufficient for the task. However, end-point stability is better in the feed-forward controller

A New Approach to Evaluation of the Material Cutting Using the Artificial Neural Networks

Article deals with the process of accurate measurements and evaluation, i.e. the monitoring of the technological process of the material cutting using the Abrasive Water Jet and process control while using the artificial neural network, and a suggestion of process steps, particularly by a thorough application of mathematical modelling principles. An important mission of this article is to document, in an easy and comprehensible way, a tool intended for the creation of good control properties for multiple axes of the material cutting head support (using the AWJ, Plasma, acetylene burner, etc.). A desired objective is to achieve the best possible or optimal quality of the surface of the cut material, while using the properties of modern cutting technologies. To achieve those objectives, one of several known options was applied, in particular the advantages of the artificial neural networks and their beneficial properties. A simple task will be used to practically document the behaviour of an artificial neural network and a selected model execution

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed