{"doi":"10.1109/ojemb.2024.3358562","title":"Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development","abstract":"<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Goal:</i> Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Methods:</i> We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1-3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Results:</i> For accuracy in the image center, all approaches had <±2.5cm average error, except CoreML which had ±5.2-6.2cm average error at 2-3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41cm and ±32cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements. <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Conclusions:</i> We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.","journal":"IEEE Open Journal of Engineering in Medicine and Biology","year":2024,"id":437692,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9478,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1247179,"name":"Mingxin Liu","orcid":"0009-0004-0101-4102","position":1,"is_corresponding":false},{"id":1247180,"name":"Diwei Sheng","orcid":"0009-0000-0587-4725","position":2,"is_corresponding":false},{"id":909152,"name":"Chen Feng","orcid":"0000-0003-3211-1576","position":3,"is_corresponding":false},{"id":909150,"name":"Todd E. Hudson","orcid":"0000-0003-4506-2670","position":4,"is_corresponding":false},{"id":988157,"name":"Junchi Feng","orcid":"0009-0008-5274-3160","position":5,"is_corresponding":false},{"id":568506,"name":"Kevin C. Chan","orcid":"0000-0003-4012-7084","position":6,"is_corresponding":false},{"id":737193,"name":"Giles Hamilton-Fletcher","orcid":"0000-0001-5903-4334","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:00:34.490087Z","pmid":"38487094","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":[]}