{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:42:22Z","timestamp":1785404542768,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["25K06395, 21K13726 and 16K16243"],"award-info":[{"award-number":["25K06395, 21K13726 and 16K16243"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"award":["25K06395, 21K13726 and 16K16243"],"award-info":[{"award-number":["25K06395, 21K13726 and 16K16243"]}],"id":[{"id":"https:\/\/ror.org\/00hhkn466","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Interpersonal synchrony is a time-dependent coordination pattern in which interacting partners\u2019 body movements become temporally aligned. This study frames interpersonal synchrony as a human motion analysis problem and presents Synchrony Vision, an RGB-D sensor-based system for real-time monitoring and event-level analysis of interpersonal motion synchrony in free dialog. The system transforms Kinect-derived skeletal positions into joint acceleration signals, applies sensor-specific conditioning, detects movement peaks, and estimates event-level phase differences between two participants within a \u00b11.0 s window. The operator-facing interface supports live RGB-D monitoring, acceleration visualization, joint selection, millisecond-scale phase-difference histograms, four synchrony metrics (Frequency, Lead\u2013lag, Width, and Strength), and exportable acceleration, timestamp, peak-pairing, and summary artifacts. We evaluated the deployed pipeline on 25 Kinect-tracked dyads engaged in unconstrained conversation. Across 200.6 min comprising 245,835 frames, the system detected 2681 synchrony events. The observed event rate exceeded circular-surrogate baselines, and dyad rankings remained stable across reasonable parameter settings. Motion Energy Analysis-style cross-correlation on the same acceleration signals also confirmed above-chance synchrony but produced different dyad rankings. These findings show that RGB-D skeletal sensing can extend human motion analysis from individual movement capture to transparent, event-level quantification of interpersonal coordination.<\/jats:p>","DOI":"10.3390\/s26144445","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:55:21Z","timestamp":1783990521000},"page":"4445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synchrony Vision: An RGB-D Sensor-Based System for Real-Time Monitoring and Event-Level Analysis of Interpersonal Motion Synchrony"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6549-1224","authenticated-orcid":false,"given":"Jinhwan","family":"Kwon","sequence":"first","affiliation":[{"name":"Department of Education, Kyoto University of Education, Kyoto 612-8522, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.1118\/1.4704644","article-title":"A real-time respiratory motion monitoring system using KINECT: Proof of concept","volume":"39","author":"Xia","year":"2012","journal-title":"Med. 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