{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T02:10:06Z","timestamp":1788919806501,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC2000601"],"award-info":[{"award-number":["2018YFC2000601"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024TZJK005"],"award-info":[{"award-number":["2024TZJK005"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018YFC2000601"],"award-info":[{"award-number":["2018YFC2000601"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2024TZJK005"],"award-info":[{"award-number":["2024TZJK005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Oxygen uptake (V\u02d9O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V\u02d9O2 and is often used for indirect V\u02d9O2 prediction. This study investigates the impact of HR placement on V\u02d9O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V\u02d9E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V\u02d9O2, RESP, V\u02d9E, and HR were collected and used to develop a neural network model for V\u02d9O2 prediction. The influence of HR position on prediction accuracy was assessed via Bland\u2013Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and V\u02d9E (V\u02d9O2HR+RESP+V\u02d9E) produces the most accurate V\u02d9O2 predictions (MAE: 165 mL\/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V\u02d9O2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts V\u02d9O2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in V\u02d9O2 prediction models, with RESP and V\u02d9E serving as effective compensatory factors. These findings contribute to refining indirect V\u02d9O2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.<\/jats:p>","DOI":"10.3390\/s24165412","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhihui","family":"Lu","sequence":"first","affiliation":[{"name":"School of China Football Sports, Beijing Sport University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junchao","family":"Yang","sequence":"additional","affiliation":[{"name":"Exercise Science School, Beijing Sport University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0363-9865","authenticated-orcid":false,"given":"Kuan","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Sports Engineering, Beijing Sport University, Beijing 100084, China"},{"name":"Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxin","family":"Li","sequence":"additional","affiliation":[{"name":"Exercise Science School, Beijing Sport University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqi","family":"Xu","sequence":"additional","affiliation":[{"name":"Exercise Science School, Beijing Sport University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junqiang","family":"Qiu","sequence":"additional","affiliation":[{"name":"Exercise Science School, Beijing Sport University, Beijing 100084, China"},{"name":"Beijing Sports Nutrition Engineering Research Center, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1093\/ajcn\/48.3.552","article-title":"Energy expenditure from minute-by-minute heart-rate recording: Comparison with indirect calorimetry","volume":"48","author":"Spurr","year":"1988","journal-title":"Am. 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