{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:09:29Z","timestamp":1779887369370,"version":"3.53.1"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Province Carbon Peak Carbon Neutrality Science and Technology Innovation Special Fund Project","award":["BT2025029"],"award-info":[{"award-number":["BT2025029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-quality cardiopulmonary resuscitation (CPR) aims at saving lives in time-critical emergencies, which requires correct compression rate, depth, and hand placement. However, due to the high cost and environmental constraints of sensor-equipped manikins or dedicated hardware, it is unrealistic to deploy these devices in ordinary training settings. For monocular vision-based methods, estimating compression depth without direct depth signals and tracking hands under severe overlap are difficult. To address these problems, this paper proposes VISIOCPR, a monocular vision-based CPR training system with human-computer collaborative feedback, which provides quantitative CPR coaching using only a standard RGB camera. To address the inherent visual constraints, the system integrates a tiered compression-point detector that maintains robust tracking continuity despite severe hand overlap and motion blur. Furthermore, it recovers accurate metric depth without attached markers through a fused calibration scheme, which combines an empirical baseline, a reference-object measurement, and visible body proportions. A randomized controlled study (n=40) showed that participants trained with VISIOCPR achieved higher simultaneous compliance and reached competency faster than the control group under the tested setting.<\/jats:p>","DOI":"10.3390\/s26113388","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:31:56Z","timestamp":1779885116000},"page":"3388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VISIOCPR: Monocular Vision-Based CPR Training System with Human-Computer Collaborative Feedback"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7149-3250","authenticated-orcid":false,"given":"Ang","family":"Li","sequence":"first","affiliation":[{"name":"School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5077-407X","authenticated-orcid":false,"given":"Wei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e93","DOI":"10.1161\/CIR.0000000000001123","article-title":"Heart disease and stroke statistics\u20142023 update: A report from the American Heart Association","volume":"147","author":"Tsao","year":"2023","journal-title":"Circulation"},{"key":"ref_2","first-page":"1","article-title":"European resuscitation council guidelines for resuscitation 2015: Section 1","volume":"95","author":"Monsieurs","year":"2015","journal-title":"Exec. 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