CanalScan: Tongue-Jaw Movement Recognition via Ear Canal Deformation Sensing

Abstract

Human-machine interface based on tongue-jaw movements has recently become one of the major technological trends. However, existing schemes have several limitations, such as requiring dedicated hardware and are usually uncomfortable to wear. This paper presents CanalScan, a nonintrusive system for tongue-jaw movement recognition using only commodity speaker and microphone mounted on ubiquitous off-the-shelf devices (e.g., smartphones). The basic idea is to send an acoustic signal, then captures its reflections and derive unique patterns of ear canal deformation caused by tongue-jaw movements. A dynamic segmentation method with Support Vector Domain Description is used to segment tongue-jaw movements. To combat sensor position-sensitive deficiency and ear-canal-shape-sensitive deficiency in multi-path reflections, we first design algorithms to assist users in adjusting the acoustic sensors to the same valid zone. Then we propose a data transformation mechanism to reduce the impacts of diversities in ear canal shapes and relative positions between sensors and the ear canal. CanalScan explores twelve unique and consistent features and applies a Random Forest classifier to distinguish tongue-jaw movements. Extensive experiments with twenty participants demonstrate that CanalScan achieves promising recognition for six tongue-jaw movements, is robust against various usage scenarios, and can be generalized to new users without retraining and adaptation.