Principal Component Analysis of Vertical Ground Reaction Force: A Powerful Method to Discriminate Normal and Abnormal Gait and Assess Treatment

Abstract

This study aims at testing the application of principal component analysis (PCA) in the ground reaction force (GRF) in discriminating the gait pattern between normal and abnormal subjects, and assessing the rehabilitation treatment. The sample was composed by 31 subjects, organized into two groups: a control group (CG) of 25 normal and a group (FG) of six patients with lower limb fractures, which was considered before (FGB) and after (FGA) a treadmill physiotherapeutic treatment. The vertical component of GRF data was collected with an instrumentized treadmill. PCA method was applied and the first two coefficients (PCC) were obtained for the three groups. The region of CG values was separated in the PCC plane with the elliptical area of displacement and with a linear threshold between CG and FGB obtained by stepwise logistic regression. Results show that all values of FGA moved towards CG region from the corresponding FGB position, indicating the potential power of PCA in discriminating between normal and abnormal gait and objectively evaluating the effects of rehabilitation treatment.

The gait deviation index: A new comprehensive index of gait pathology

“…The normal distribution curve is shown for comparison, and a heavy vertical line indicates the control mean (GDI = 100). (6,8,9,10,TD) 471 69.8 11.1 31.7 103.8 True 8 (6,7,9,10,TD) 916 73.1 11.8 38.9 118.1 True 9 (6,7,8,10,TD) 1205 76.9 11.5 44.8 123.3 True 10 (6,7,8,9,TD) 948 81.8 11.8 44.0 126.5 True TD (6,7,8,9,10) 166 100.0 10.0 73.9 129.9 True Numbers in parentheses indicate statistically significant differences as determined from an ANOVA with p < 0.05.…”

“…A number of multivariate statistical methods have been developed for dealing with the complexity and interdependence of gait data [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. While some of these methods focus primarily on identifying gait patterns and relationships among variables, several aim to develop either joint-specific or overall indexes of gait pathology [7,8,10,[12][13][14]19].…”

“…While some of these methods focus primarily on identifying gait patterns and relationships among variables, several aim to develop either joint-specific or overall indexes of gait pathology [7,8,10,[12][13][14]19]. Among these, the Gillette Gait Index (GGI) appears to be the most extensively validated, commonly cited (based on a TM citation search), and is widely used in clinical gait research and practice [3,12,13,[21][22][23][24][25].…”

Eigensteps: A giant leap for gait recognition

“…It has also been used before in gait recognition, in particular in MV based gait recognition, for example in [9]- [14]. In [15] the authors actually apply PCA to a Floor Sensor system, but the goal in that article is to discriminate between normal and abnormal walking and not so much user identif cation or authentication.…”

“…The only time PCA was used with accelerometer based gait data was to distinguish abnormal walking behaviour [16], [17], similar to what was done in [15].…”