E Watelain1, F Barbier, P Allard, A Thevenon, J C Angué. 1. Laboratoire d' Automatique et de Mécanique Industrielles et Humaines, Université de Valenciennes et du Hainaut-Cambrésis, France.
Abstract
OBJECTIVES: To distinguish the gait patterns of young subjects from those of elderly men using three-dimensional (3D) gait data, to determine if elderly subjects displayed other than a typical gait pattern, and to identify which parameters best describe them. DESIGN: Nonrandomized study in which video and force plate data were collected at the subject's own free walking speed and used in a 3D inverse dynamic model. Cluster analysis was chosen to identify the gait families, and analyses of variance were performed to determine which parameters were different. SETTING: A gait laboratory. PARTICIPANTS: The sample of convenience involved a single but mixed group consisting of 16 able-bodied elderly subjects (mean age, 62yrs) and 16 able-bodied young subjects aged between 20 and 35 years. MAIN OUTCOME MEASURES: Phasic and temporal gait parameters, as well as the 3D muscle powers developed in the joints of the right lower limb during the gait cycle. RESULTS: The walking patterns in elderly subjects were found to be different from those of the young adults. Three elderly gait families or groups forming a specific gait pattern were identified, and differences were found in the phasic and temporal parameters as well as in 6 peak muscle powers. Four of the peak powers occurred in the sagittal plane, and half of them were related to the hip. CONCLUSIONS: Biomechanical parameters can be used to classify the gait patterns of young and elderly men using cluster analysis rather than age alone. The muscle powers in elderly subjects are perturbed throughout the gait cycle and not only at push-off. It appears that the plane in which the peak powers occurred was related to their occurrence in the gait cycle. Variability in the gait patterns of elderly subjects could reflect natural adaptations or compensations. These should not be indicative of a deficient gait or be misconstrued as some age-related pathology.
OBJECTIVES: To distinguish the gait patterns of young subjects from those of elderly men using three-dimensional (3D) gait data, to determine if elderly subjects displayed other than a typical gait pattern, and to identify which parameters best describe them. DESIGN: Nonrandomized study in which video and force plate data were collected at the subject's own free walking speed and used in a 3D inverse dynamic model. Cluster analysis was chosen to identify the gait families, and analyses of variance were performed to determine which parameters were different. SETTING: A gait laboratory. PARTICIPANTS: The sample of convenience involved a single but mixed group consisting of 16 able-bodied elderly subjects (mean age, 62yrs) and 16 able-bodied young subjects aged between 20 and 35 years. MAIN OUTCOME MEASURES: Phasic and temporal gait parameters, as well as the 3D muscle powers developed in the joints of the right lower limb during the gait cycle. RESULTS: The walking patterns in elderly subjects were found to be different from those of the young adults. Three elderly gait families or groups forming a specific gait pattern were identified, and differences were found in the phasic and temporal parameters as well as in 6 peak muscle powers. Four of the peak powers occurred in the sagittal plane, and half of them were related to the hip. CONCLUSIONS: Biomechanical parameters can be used to classify the gait patterns of young and elderly men using cluster analysis rather than age alone. The muscle powers in elderly subjects are perturbed throughout the gait cycle and not only at push-off. It appears that the plane in which the peak powers occurred was related to their occurrence in the gait cycle. Variability in the gait patterns of elderly subjects could reflect natural adaptations or compensations. These should not be indicative of a deficient gait or be misconstrued as some age-related pathology.