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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.

While adoption of wearable technologies is increasing, there remain questions about applicability and clinical validity for their role in the improvement of medical interventions for chronic conditions. This chapter focuses primarily on wearable smart textiles which have potential to transform medical practices. Our overarching goal is to design wearable devices with clinical accuracy and ease of use. Patient-friendliness is important such that adoption into home-based telemedicine interventions of Parkinson’s disease (PD) is fast and easy. PD is a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. With this motivation, we present the design, development, and validation of WearUP, a smart glove made of smart textiles for PD. In particular, we have carefully designed experiments on WearUP to test their repeatability, precision, and sensitivity for quantifying the motor symptoms present in the finger tapping task. WearUP was tested using a robotic hand that was programmed to make precise finger tapping movements. Later, we conducted a pilot study on nine healthy human participants who wore the WearUP glove and performed finger tapping tasks. The repeatability test on the robotic hand showed the variance of finger tapping frequency which was less than 6.00 × 10 − 5 . WearUP also performed with a high degree of accuracy and precision in human participants. When tested on healthy human participants, the variance of the finger tapping frequency was in the range of 0.001–0.1. Our developments and promising experimental results open a new door for telemedicine to adopt smart textiles because they have potential to offer patient-friendliness without compromising clinical accuracy.