Auxetic cTPU Soft Strain Sensor Augmented with Artificial Intelligence for Human Gait Analytics

Abstract

An inclined demand for flexible and wearable electronics, driven by progress in artificial intelligence technologies, underscores the significance of auxetic sensors in healthcare, medical rehabilitation, soft robotics, and humanmachine interfaces. To achieve widespread adoption, these sensors should exhibit high sensitivity, exceptional stretchability, and long-lasting durability. This paper delves into the potential for developing such wearable soft strain sensors based on conductive thermoplastic polyurethane (cTPU) 3D printed as auxetic soft metamaterials. Simultaneous empirical mechanical and electrical measurements for the auxetic soft sensor designs were compared with numerical simulations. Following the auxetic cTPU soft strain sensor was tested in human gait analytics to predict the gait type of the wearer by employing a single auxetic cTPU soft strain sensor. The results suggest that such soft sensors based on metamaterials are genuine candidates for applications in robotic, healthcare and human-robot interfaces when realized with 3D printing and artificial intelligence.