An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

In our previous work, we developed an IMU (Inertial Measurement Unit) based smart ring that allows users to type characters without a physical keyboard and adopt well-known pattern recognition algorithms, such as Support Vector Machine (SVM), and Naive Bayes (NB), for keystroke recognition. However, these algorithms always require intensive computing resources or offer limited recognition accuracy. Moreover, they are often seen as black boxes incapable of providing readily comprehensible and visible clues for classification. This hampers the improvement of keystroke recognition accuracy and the ring-type virtual keyboard's character layout design. Here we present a novel algorithm to recognize keystrokes in a fast and accurate manner. Firstly, the standard feature vector, including five attitude angle features and one acceleration feature, is built to express a specific stroke. Then, the feature vector of the testing keystroke is compared with the standard features. The most similar keystroke is matched and recognized after three times of voting. Based on this algorithm, we can identify the easily confused keystrokes and understand the mechanisms behind it. With this interpretability, we will be able to achieve the customized ring-type virtual keyboard application if necessary. The performance of this algorithm was evaluated by using a dataset with 1500 keystrokes of three different subjects. The results show that our algorithm is more effective in keystroke recognition than traditional algorithms for this ring-type keyboard. In addition to its application on virtual keyboards, this algorithm can also be potentially applied on other classification tasks with easy-to-understand results.