Automatically Locating Sensor Position on an E-textile Garment Via Pattern Recognition

Electronic textiles are a sound platform for wearable computing. Many applications have been devised that use sensors placed on these textiles for fields such as medical monitoring and military use or for display purposes. Most of these applications require that the sensors have known locations for accurate results. Activity recognition is one application that is highly dependent on knowledge of the sensor position. Therefore, this thesis presents the design and implementation of a method whereby the location of the sensors on the electronic textile garments can be automatically identified when the user is performing an appropriate activity. The software design incorporates principle component analysis using singular value decomposition to identify the location of the sensors. This thesis presents a method to overcome the problem of bilateral symmetry through sensor connector design and sensor orientation detection. The scalability of the solution is maintained through the use of culling techniques. This thesis presents a flexible solution that allows for the fine-tuning of the accuracy of the results versus the number of valid queries, depending on the constraints of the application. The resulting algorithm is successfully tested on both motion capture and sensor data from an electronic textile garment.