{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T04:10:45Z","timestamp":1788667845767,"version":"build-2803163510"},"reference-count":35,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Medical and Health Science and Technology Program of Zhejiang Province, China","award":["2024KY441"],"award-info":[{"award-number":["2024KY441"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Ballistocardiography (BCG)-based heart rate (HR) monitoring faces accuracy degradation due to motion artifacts, limiting its practical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to enhance HR estimation reliability under motion-contaminated conditions while ensuring real-time performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A hybrid system integrating adaptive filtering and enhanced continuous wavelet transform (CWT) is developed. The framework localizes motion segments (95.1% accuracy) and employs spectral reconstruction via magnitude-frequency nullification to restore HR from contaminated windows. Computational latency was evaluated on an embedded ARM platform to verify real-time feasibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Validation using 6000\u2005min of data demonstrated that the proposed method achieved an MAE of 2.94\u2005BPM, comparable to the CNN-LSTM baseline (2.85\u2005BPM), while reducing the average processing latency from 450.2\u2005ms to 86.4\u2005ms. Compared with conventional methods, the proposed framework reduced the MAE by 53.8% and improved monitoring stability by 35.6%. Bland-Altman analysis confirmed limits of agreement within [\u22124.77, 5.23] BPM, validating clinical reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed hybrid framework provides a computationally efficient and accurate solution for non-contact HR monitoring in motion-prone and bed-based clinical environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261481871","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:22:33Z","timestamp":1788452553000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Accuracy improvement for heart rate monitoring from bed-based ballistocardiography signals with motion 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