title: The effects of training set on prediction of elbow trajectory from shoulder trajectory during reaching to targets.
abstract: Patients with transhumeral amputations and C5/C6 quadriplegia may be able to use voluntary shoulder motion as command signals for powered prostheses and functional electrical stimulation, respectively. Spatio-temporal synergies exist for goal oriented reaching movements between the shoulder and elbow joints in able bodied subjects. We are using a multi-layer perceptron neural network to discover and embody these synergies. Such a network could be used as a high level functional electrical stimulation (FES) controller that could predict elbow joint kinematics from the voluntary movements of the shoulder joint. Counter-intuitively, a well-chosen reduced data set for training the network resulted in better performance than use of the whole data set against which the predictions of the network were evaluated.
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90033, USA. kaliki@aminet.usc.edu
authors.forename: Rahul R
authors.initials: RR
authors.lastname: Kaliki
authors.forename: Rahman
authors.initials: R
authors.lastname: Davoodi
authors.forename: Gerald E
authors.initials: GE
authors.lastname: Loeb
mesh_terms: D001696:Biomechanical Phenomena / Q000379:methods*; D004550:Elbow; D004551:Elbow Joint / Q000033:anatomy & histology*; D004599:Electric Stimulation Therapy / Q000379:methods; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D004867:Equipment Design; D006801:Humans; D009068:Movement; D016571:Neural Networks, Computer; D011782:Quadriplegia / Q000534:rehabilitation*; D015203:Reproducibility of Results; D012782:Shoulder; D012785:Shoulder Joint / Q000033:anatomy & histology*; D013997:Time Factors