Kinematic characteristics during gait in frail older women identified by principal component analysis

Abstract

Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants’ time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.

Effects of motor-cognitive interaction based on dual-task gait analysis recognition in middle age to aging people with normal cognition and mild cognitive impairment

“…Thus, fewer unrelated variables can be used to reflect the information conveyed by a large number of relevant raw data. Wakako et al extracted 26 principal component vectors from many gait parameters by the PCA method and found that knee-angle variability and ankle-angle variability are important gait indicators for distinguishing between frail and non-frail older women ( Tsuchida et al, 2022 ). Therefore, the goal of this study is to increase the understanding of spatio-temporal parameters of MCI and normal elderly participants by using PCA.…”

Systematic Review on the Applicability of Principal Component Analysis for the Study of Movement in the Older Adult Population

“…In a longitudinal study, Shin et al aimed to group diseases classified by the International Classification of Diseases using the PCA to extract comorbidity patterns and found that the principal component 1, which included diabetes, heart disease, and hypertension, was associated with an increased hazard ratio of mortality [ 65 ]. Some authors have already studied the kinematics of gait to cluster older adults with and without specific conditions [ 28 , 66 , 67 ]. The PCA clustering could be applied to kinematic and kinetic data of different daily performance tasks of community-dwelling older adults to cluster the autonomy–disability level and mortality.…”

“…In the upcoming studies, it is also necessary to include larger sample sizes in order to fully take advantage of the potential of multivariate analysis. Furthermore, other structured reviews and meta-analyses aiming to understand the role of PCA in the biomechanical analysis of older adults, differentiating between individuals with diseases or conditions and healthy ones [ 28 , 66 , 67 ], would be beneficial, as the evidence in these topics grows.…”

Regulation of whole-body and segmental angular momentum in persons with Parkinson's disease on an irregular surface

“…Major modes of variability between the health groups on the regular and the irregular surface were identified using principal component (PC) analysis. PC analysis has been applied to time-series biomechanics data using a variety of different approaches (Bennett et al, 2010;Herr and Popovic, 2008;Chiovetto et al, 2018;Reid et al, 2010;Chester and Wrigley, 2008;Monaghan et al, 2021;Tsuchida et al, 2022). In the present study, the 12 waveforms (angular momentum for the whole body, arms, legs, and head/trunk/pelvis segments about the x, y, and z axes) were grouped together resulting in 12 matrices with dimension 18 × 101 (number of total participants × number of data points).…”