Evaluation of Microgesture Recognition Using a Smartwatch

Gesture based interaction and its recognition has been an area of active research with the growing popularity of wearables. We here propose an approach to detect fine-grained finger and palm motions using inertial sensors in a commercial smartwatch. A user specific SVM based classifier is developed for 7 microgestures with a classification accuracy of 94.4%. We extend this to a user adaptive model by including a few representative instances of a new user and achieve a classification accuracy of 91.7%. Further, we are able to differentiate between variations of a microgesture using three fundamental building blocks - distance, speed and orientation. A novel regression based approach is presented to predict the distance parameter. The idea is demonstrated on a swipe gesture with an error of 14%.