Also, step leg ratio and 10-M walk test was used to assess gait parameters. Weight-bearing asymmetry and functional balance were assessed using the percentage body weight asymmetry and Berg balance scale. Postural sway was measured under wide base eyes open, wide base eyes closed, narrow base eyes open, and narrow base eyes closed conditions. Twenty participants were recruited based on the inclusion and exclusion criteria and were divided into two groups group A (LLD ≤ 1 cm) and group B (LLD > 1 cm). So, the present study aimed to evaluate influence of limb length discrepancy (LLD) on balance and gait parameters in patients with stroke. The proposed method gives the classification accuracy at a very encouraging level of 99.55%.Ī frequent complaint by stroke patients presenting for physiotherapy was perceived shortness of the affected lower limb and inability to weight bear onto the affected side. The proposed footprint biometric identification will correctly identify or classify the person by training the system with patterns of the interested subjects using an artificial neural network model specially designed for this task. Furthermore, descriptive statistics are generated from all the above mentioned footprint features and concatenated to create the final feature vector. The algorithm enhances the footprint matching performance by selecting the three sets of local invariant feature detectors-histogram of gradients, maximally stable external regions, speed up robust features local binary pattern as texture descriptor, corner point detector, and PCA. The paper proposes one of the low-cost hardware to scan the biometric human footprints that utilise image pre-processing and enhancement capabilities for obtaining the features. The philosophy of this study focuses on human footprint identification applicable for high-security applications such as the safety of public places, crime scene investigation, impostor identification, biotech labs and blue-chip labs, identification of infants in hospitals.
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