Real time clinical gait analysis and foot anomalies detection using pressure sensor and Convolutional Neural Network

Abstract

This research presents a novel insight on gait disorder detection using transfer learning algorithms on sensor-acquired data based on the implementation of popular Convolutional Neural Network (CNN) models. So, the research basically deals with Deep Learning. The dissertation proposes the use of pressure sensors to extract heatmap images during gait, which are then trained and tested in various classification algorithms for gait abnormality diagnosis and detection. Gait is a biological and scientific study of body movement and locomotion that emphatically serves as a reliable parameter for inspecting the human body’s neuromuscular and skeletal systems. To build a convenient and precise classification system for possible application, synthetic data was generated in multiple preexisting CNN models, which were then evaluated using conventional performance metrics. The proposed notion yielded experimental findings that showed higher accuracies for all transfer learning schemes tested, with the Vgg16 model achieving a notable accuracy of 97.15%. As a result, the analysis demonstrated not only a significant performance in terms of accuracy, but also reduced complexity and computing time, making the approach efficient yet effective. A detailed comparative analysis of performance with all other algorithms was carried out in terms of accuracy, precision, recall, and F-1 score