A Study on the Contribution of Medial and Lateral Longitudinal Foot Arch to Human Gait.

This study aimed to investigate the contribution of medial longitudinal arch and lateral longitudinal arch in human gait and to study the correlation between foot features and gait characteristics. The foot arch plays a significant role in human movements, and understanding its contribution to spatiotemporal gait parameters is vital in predicting and rectifying gait patterns. To serve the objectives, the study developed a new foot feature measurement system and measured the foot features and spatiotemporal gait parameters of 17 young healthy subjects without any foot structure abnormality. The foot-feature parameters were measured under three movement conditions which were sitting, standing, and one-leg standing conditions. The spatiotemporal gait parameters were measured at three speeds which were fast, preferred, and slow speeds. The correlation study showed that medial longitudinal arch characteristics were found to be associated with temporal gait parameters while lateral longitudinal arch characteristics were found to be associated with spatial gait parameters. The developed system not only eases the burden of manual measuring but also secures accuracy of the collected data. Inviting variety of subjects including athletes and people with abnormal foot structures would extend the scope of this study in the future. The findings of this study break new ground in the field of the foot- and gait-related research work.Clinical Relevance-This study demonstrated that the medial longitudinal arch and lateral longitudinal arch characteristics were related to the temporal and spatial gait parameters, respectively. These underlying findings can be applied to investigate relationships between foot abnormality and gait characteristics.

Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data.

Human gait has been regarded as a useful behavioral biometric trait for personal identification and authentication. This study aimed to propose an effective approach for classifying gait, measured using wearable inertial sensors, based on neural networks. The 3-axis accelerometer and 3-axis gyroscope data were acquired at the posterior pelvis, both thighs, both shanks, and both feet while 29 semi-professional athletes, 19 participants with normal foot, and 21 patients with foot deformities walked on the 20-meter straight path. The classifier based on the gait parameters and fully connected neural network was developed by applying 4-fold cross-validation to 80% of the total samples. For the test set that consisted of the remaining 20% of the total samples, this classifier showed an accuracy of 93.02% in categorizing the athlete, normal foot, and deformed foot groups. Using the same model validation and evaluation method, up to 98.19% accuracy was achieved from the convolutional neural network-based classifier. This classifier was trained with the gait spectrograms obtained from the time-frequency domain analysis of the raw acceleration and angular velocity data. The classification based only on the pelvic spectrograms exhibited an accuracy of 94.25% even without requiring a time-consuming and resource-intensive process for feature engineering. The notable performance and practicality in gait classification achieved by this study suggest potential applicability of the proposed approaches in the field of biometrics.