Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
Artificial neural network model for the generation of muscle activation patterns for human locomotion
Autore:
Prentice, SD; Patla, AE; Stacey, DA;
Indirizzi:
Univ Waterloo, Dept Kinesiol, Gait & Posture Lab, Waterloo, ON N2L 3G1, Canada Univ Waterloo Waterloo ON Canada N2L 3G1 ab, Waterloo, ON N2L 3G1, Canada Univ Guelph, Dept Comp & Informat Sci, Guelph, ON N1G 2W1, Canada Univ Guelph Guelph ON Canada N1G 2W1 rmat Sci, Guelph, ON N1G 2W1, Canada
Titolo Testata:
JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
fascicolo: 1, volume: 11, anno: 2001,
pagine: 19 - 30
SICI:
1050-6411(200102)11:1<19:ANNMFT>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
SPINAL STEPPING GENERATOR; GAIT; WALKING; COORDINATION; SIMULATION; LAMPREY;
Keywords:
kinematics; electromyography; gait modifications; locomotor control; internal model;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Citazioni:
38
Recensione:
Indirizzi per estratti:
Indirizzo: Prentice, SD Univ Waterloo, Dept Kinesiol, Gait & Posture Lab, Waterloo, ON N2L 3G1, Canada Univ Waterloo Waterloo ON Canada N2L 3G1 ON N2L 3G1, Canada
Citazione:
S.D. Prentice et al., "Artificial neural network model for the generation of muscle activation patterns for human locomotion", J ELECTROMY, 11(1), 2001, pp. 19-30

Abstract

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that theCNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robustmodel that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms ofcadence, stride length, stance width or required foot clearance. The finalnetwork was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine. (C) 2001 Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 28/03/20 alle ore 13:41:40