{"doi":"10.1145/3411764.3445081","title":"BackSwipe: Back-of-device Word-Gesture Interaction on Smartphones","abstract":"Back-of-device interaction is a promising approach to interacting on smartphones. In this paper, we create a back-of-device command and text input technique called BackSwipe, which allows a user to hold a smartphone with one hand, and use the index finger of the same hand to draw a word-gesture anywhere at the back of the smartphone to enter commands and text. To support BackSwipe, we propose a back-of-device word-gesture decoding algorithm which infers the keyboard location from back-of-device gestures, and adjusts the keyboard size to suit the gesture scales; the inferred keyboard is then fed back into the system for decoding. Our user study shows BackSwipe is feasible and a promising input method, especially for command input in the one-hand holding posture: users can enter commands at an average accuracy of 92% with a speed of 5.32 seconds/command. The text entry performance varies across users. The average speed is 9.58 WPM with some users at 18.83 WPM; the average word error rate is 11.04% with some users at 2.85%. Overall, BackSwipe complements the extant smartphone interaction by leveraging the back of the device as a gestural input surface.","journal":"PubMed","year":2021,"id":213694,"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":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9586,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":207171,"name":"Suwen Zhu","orcid":null,"position":1,"is_corresponding":false},{"id":805824,"name":"Zhi Li","orcid":"0000-0002-3194-5176","position":2,"is_corresponding":false},{"id":805825,"name":"Zheer Xu","orcid":"0009-0003-5604-8556","position":3,"is_corresponding":false},{"id":326311,"name":"Xing-Dong Yang","orcid":"0000-0002-6732-6748","position":4,"is_corresponding":false},{"id":387746,"name":"I. V. Ramakrishnan","orcid":"0000-0002-1768-7043","position":5,"is_corresponding":false},{"id":553856,"name":"Xiaojun Bi","orcid":"0000-0002-9716-7709","position":6,"is_corresponding":false},{"id":805780,"name":"Wenzhe Cui","orcid":"0000-0001-8968-846X","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":null,"created_at":"2026-07-18T23:52:36.886828Z","pmid":"35237772","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":[]}