{"doi":"10.1145/3586183.3606760","title":"TouchType-GAN: Modeling Touch Typing with Generative Adversarial Network","abstract":"Models that can generate touch typing tasks are important to the development of touch typing keyboards. We propose TouchType-GAN, a Conditional Generative Adversarial Network that can simulate locations and time stamps of touch points in touch typing. TouchType-GAN takes arbitrary text as input to generate realistic touch typing both spatially (i.e., (x, y) coordinates of touch points) and temporally (i.e., timestamps of touch points). TouchType-GAN introduces a variational generator that estimates Gaussian Distributions for every target letter to prevent mode collapse. Our experiments on a dataset with 3k typed sentences show that TouchType-GAN outperforms existing touch typing models, including the Rotational Dual Gaussian model [36] for simulating the distribution of touch points, and the Finger-Fitts Euclidean Model [30] for simulating typing time. Overall, our research demonstrates that the proposed GAN structure can learn the distribution of user typed touch points, and the resulting TouchType-GAN can also estimate typing movements. TouchType-GAN can serve as a valuable tool for designing and evaluating touch typing input systems.","journal":null,"year":2023,"id":393756,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.951,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":805877,"name":"Yan Ma","orcid":"0000-0001-8264-3103","position":1,"is_corresponding":false},{"id":553855,"name":"Shumin Zhai","orcid":"0000-0003-0752-2090","position":2,"is_corresponding":false},{"id":730394,"name":"Xianfeng Gu","orcid":"0000-0001-8226-5851","position":3,"is_corresponding":false},{"id":553856,"name":"Xiaojun Bi","orcid":"0000-0002-9716-7709","position":4,"is_corresponding":false},{"id":1161552,"name":"Jeremy Chu","orcid":"0000-0002-0173-2153","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:19:05.913428Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}