Towards IoT-Enabled Technologies: Foot-Type Classification Based on Artificial Intelligence Methods

Abstract

The Internet of Things (IoT) is a promising technology used for several connectivity applications to enable technologies. IoT includes connectivity protocols, sensors, communication technologies, and data processing methods that will allow IoT devices to collect, process, and analyze large amounts of data. Many types of foot distortion, such as bunions, hammer toe, flatfoot, and others, can either be congenital or acquired. These deformities are considered significant contributors to body imbalance, leading to fatigue and discomfort during everyday activities. Timely identification of flat feet and the development of treatment plans are very important to mitigate or eliminate complications. Thus, IoT technology can enhance patient care, improve diagnostic accuracy, increase efficiency, and support connected doctors and clinical staff in caring for their patients. Therefore, the objective of the present manuscript is to design and implement an IoT application that can detect and diagnose the flatfoot types. It is a user-friendly application that can be managed by both patients and physicians to record the stages of flat feet and perform a preliminary check for possible gait problems in daily life. The proposed work uses a segmentation method that partitions and analyzes a digital image into discrete clusters of pixels. The proposed method classifies foot distortion types into flat feet, concave, and normal, and finds hidden patterns in the dataset. The finding of this study is to enable early disease detection, enable real-time patient monitoring, and thus personalize treatment plans.