Facilitating text entry on smartphones with qwerty keyboard for users with parkinson's disease

Abstract

QWERTY is the primary smartphone text input keyboard confgu-ration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson's disease, can severely afect typing efciency and user experience. In this paper, we investigated Parkinson's users' typing behavior on smartphones. In particular, we identifed and compared the typing characteristics generated by users with and without Parkinson's symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct in-sertion, substitution and omission errors, while maintaining direct physical interpretation. User study results confrmed that the pro-posed algorithm outperformed baseline techniques: users reached 22.8 WPM typing speed with a signifcantly lower error rate and higher user-perceived performance and preference. We concluded that our method could efectively improve the text entry experience on smartphones for users with Parkinson's disease.