Automated Optimal Coordination of Multiple-DOF Neuromuscular Actions in Feedforward Neuroprostheses

• Published in: IEEE transactions on biomedical engineering Vol. 56; no. 1; pp. 179 - 187
• Main Authors: Lujan $^$, J. Luis, Crago, Patrick E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-01-2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks (ANNs); Automatic control; Biomechanical Phenomena; Control systems; coupled DOFs feedforward control; Design methodology; Design optimization; Experiments; Feedback; Force control; Hand Strength - physiology; Humans; Isometric Contraction - physiology; Models, Neurological; Muscle, Skeletal - physiology; Muscles; Nervous System Physiological Phenomena; Neural Networks, Computer; Neuromuscular; Neuromuscular Diseases - rehabilitation; neuroprostheses; Process design; Prostheses and Implants; Prosthetics; Spinal cord injuries; Spinal Cord Injuries - rehabilitation; Thumb; Thumb - physiology; Transducers
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2008.2002159

This paper describes a new method for designing feedforward controllers for multiple-muscle, multiple-DOF, motor system neural prostheses. The design process is based on experimental measurement of the forward input/output properties of the neuromechanical system and numerical optimization of stimulation patterns to meet muscle coactivation criteria, thus resolving the muscle redundancy (i.e., overcontrol) and the coupled DOF problems inherent in neuromechanical systems. We designed feedforward controllers to control the isometric forces at the tip of the thumb in two directions during stimulation of three thumb muscles as a model system. We tested the method experimentally in ten able-bodied individuals and one patient with spinal cord injury. Good control of isometric force in both DOFs was observed, with rms errors less than 10% of the force range in seven experiments and statistically significant correlations between the actual and target forces in all ten experiments. Systematic bias and slope errors were observed in a few experiments, likely due to the neuromuscular fatigue. Overall, the tests demonstrated the ability of a general design approach to satisfy both control and coactivation criteria in multiple-muscle, multiple-axis neuromechanical systems, which is applicable to a wide range of neuromechanical systems and stimulation electrodes.