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The global society is increasingly facing the challenges that reduce mobility, quality of life, and independence. Gait disorders are often both a result of, and predictor of further issues, tied to the 15 million stroke patients annually world-wide. These individuals face a number of gait abnormalities including drop foot that is a pathological condition, limiting patients' ability to lift the foot from the ground during the swing phase of walking. In this research work, we introduce a novel smart textile system, MagicSox that is woven with multiple sensors distributed over the surface of the foot. The overarching goal of MagicSox is to quantify the gait abnormalities in remote settings such as patients' homes so that clinicians and physical therapists can assess their patients on daily basis. The paper provides a detailed architecture of MagicSox that leverages the computing and communication capabilities of a modern Internet of Things (IoT) processor, the Intel Curie. We have developed an Android smart phone app that uses Bluetooth low energy (BLE) and automates the multi-sensor data collection from MagicSox. In terms of signal processing of wearable sensor data, we adopted multiplication of backward differences (MOBD) to analyze the multi-modal time series data to distinguish drop foot events from normal walking cycles. We pursued a usability study on 12 healthy participants who were asked to walk normally and also to simulate drop foot cycles. We developed support vector machine (SVM) classifiers to analyze the data. The classification resulted in the accuracy of drop foot detection varying from 73.38 % − 99.02 % . The promising results now encourage us to evaluate MagicSox on stroke patients in future studies.