Acceleration and Magnetic Sensor Network for Shape Sensing

Abstract

This paper presents a method for real-time shape sensing of thin and flexible materials, such as fabric. A method is based on three-axial acceleration and magnetic sensor nodes that are embedded into the material and can measure local orientation data. A proposed algorithm for global shape reconstruction from local orientation measurements ensures fast computations for shape reconstruction utilizing data from large number of sensors. This provides basis for the development of portable self-contained real-time systems for free form shape sensing with high resolution that has not been demonstrated before. A method is designed for applications in new emerging fields, such as smart textile and flexible electronics, where it can be used to obtain wearers posture or shape of the device. A method can be implemented with off the shelf low-cost electronic components and conventional portable computing devices, such as smartphone. The practical implementation of the system is demonstrated, also assessment of shape reconstruction performance is done by comparison with commercial Kinect v2 sensor. Impact of different error sources is analyzed, and possible improvements of the method are discussed.