INTRODUCTION: Principal component analysis (PCA) has been used to reduce the volume of gait data and can also be used to identify the differences between populations. This approach has not been used on stair climbing gait data. Our objective was to use PCA to compare the gait patterns between young and older adults during stair climbing. METHODS: The knee joint mechanics of 30 healthy young adults (23.9 + or - 2.6 years) and 32 healthy older adults (65.5 + or - 5.2 years) were analyzed while they ascended a custom 4-step staircase. The three-dimensional net knee joint forces, moments, and angles were calculated using typical inverse dynamics. PCA models were created for the knee joint forces, moments and angles about the three axes. The principal component scores (PC scores) generated from the model were analyzed for group differences using independent samples t-tests. A stepwise discriminant procedure determined which principal components (PCs) were most successful in differentiating the two groups. RESULTS: The number of PCs retained for analysis was chosen using a 90% trace criterion. Of the scores generated from the PCA models nine were statistically different (p < .0019) between the two groups, four of the nine PC scores could be used to correctly classify 95% of the original group. CONCLUSIONS: The PCA and discriminant function analysis applied in this investigation identified gait pattern differences between young and older adults. Identification of stair gait pattern differences between young and older adults could help in understanding age-related changes associated with the performance of the locomotor task of stair climbing. Copyright 2009 Elsevier B.V. All rights reserved.
INTRODUCTION: Principal component analysis (PCA) has been used to reduce the volume of gait data and can also be used to identify the differences between populations. This approach has not been used on stair climbing gait data. Our objective was to use PCA to compare the gait patterns between young and older adults during stair climbing. METHODS: The knee joint mechanics of 30 healthy young adults (23.9 + or - 2.6 years) and 32 healthy older adults (65.5 + or - 5.2 years) were analyzed while they ascended a custom 4-step staircase. The three-dimensional net knee joint forces, moments, and angles were calculated using typical inverse dynamics. PCA models were created for the knee joint forces, moments and angles about the three axes. The principal component scores (PC scores) generated from the model were analyzed for group differences using independent samples t-tests. A stepwise discriminant procedure determined which principal components (PCs) were most successful in differentiating the two groups. RESULTS: The number of PCs retained for analysis was chosen using a 90% trace criterion. Of the scores generated from the PCA models nine were statistically different (p < .0019) between the two groups, four of the nine PC scores could be used to correctly classify 95% of the original group. CONCLUSIONS: The PCA and discriminant function analysis applied in this investigation identified gait pattern differences between young and older adults. Identification of stair gait pattern differences between young and older adults could help in understanding age-related changes associated with the performance of the locomotor task of stair climbing. Copyright 2009 Elsevier B.V. All rights reserved.
Authors: Lauro V Ojeda; Antonia M Zaferiou; Stephen M Cain; Rachel V Vitali; Steven P Davidson; Leia A Stirling; Noel C Perkins Journal: Sensors (Basel) Date: 2017-11-17 Impact factor: 3.576