Gait Analysis

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance. This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i.e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system

Background: Losing a limb can negatively impact balance and gait, leading to reduced quality of life.Aim: This study aimed to examine correlations between spatiotemporal gait and balance with scores of the Quality of Life questionnaire.Materials and Methods: 30 veterans with unilateral below-knee amputations underwent gait analysis to measure step length, stride length, stance time, cadence, velocity, and walking symmetry index by step length. Static balance was assessed via the center of pressure path length by Wii Balance Board during standing. The Timed Up and Go test evaluated dynamic balance. The QoL scores were recorded using the Quality of Life questionnaire for amputees, Trinity Amputation, and Prosthesis Experience Scales (TAPES). Pearson’s correlation coefficients were calculated between spatiotemporal of gait, balance scores, and TAPES domain outcomes.Results: Stride length (r=0.660), prosthetic step length (r=0.648) and intact step length (r=0.618) showed significant moderate positive correlations with psychosocial adjustment, while amputation limb stance time (r=-0.409) correlated weakly. Activity limitations negatively correlated with step/stride lengths (r range -0.781 to -0.784) and cadence (r=-0.538). Moreover, prosthesis satisfaction was associated with improved walking symmetry (r=0.445) and prosthetic stance time (r=0.388).Conclusion: Optimizing gait and balance in below-knee amputation can improve quality of life. Biomechanical factors should be targeted in rehab programs to enhance well-being.