title: Artificial neural network prediction using accelerometers to control upper limb FES during reaching and grasping following stroke.
abstract: This work investigates arm acceleration as a control signal for functional electrical stimulation (FES) of the upper limb during reaching and grasping. We segment the reach and grasp motion into phases and present an artificial neural network (ANN) approach that estimates the phase of the reaching cycle from accelerometer signals. We then select the stimulator command that maximizes successful triggering without unnecessary risk to the patient's safety. Our results suggest that the algorithm successfully generalizes between sessions and patients but is less successful at generalizing between different motions.
authors.affiliation: Dept. of Electr. Eng. & Electron., Salford Univ., Salford, UK. p.tresadern@salford.ac.uk
authors.forename: Phil
authors.initials: P
authors.lastname: Tresadern
authors.forename: Sibylle
authors.initials: S
authors.lastname: Thies
authors.forename: Laurence
authors.initials: L
authors.lastname: Kenney
authors.forename: David
authors.initials: D
authors.lastname: Howard
authors.forename: John Y
authors.initials: JY
authors.lastname: Goulermas
mesh_terms: D000054:Acceleration; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001132:Arm / Q000503:physiopathology; D001698:Biomedical Engineering; D004576:Electromyography; D005260:Female; D006429:Hemiplegia / Q000503:physiopathology / Q000534:rehabilitation / Q000628:therapy; D006801:Humans; D008297:Male; D008328:Man-Machine Systems; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D020521:Stroke / Q000503:physiopathology / Q000628:therapy*; D000071939:Stroke Rehabilitation; D004561:Transcutaneous Electric Nerve Stimulation