{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T01:25:25Z","timestamp":1744161925376},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Tracking physical effort from physiological signals has enabled people to manage required activity levels in our increasingly sedentary and automated world.  Breathing is a physiological process that is a reactive representation of our physical effort. In this demo, we present DeepVentilation, a deep learning system to predict minute ventilation in litres of air a person moves in one minute uniquely from real-time measurement of rib-cage breathing forces. DeepVentilation has been trained on input signals of expansion and contraction of the rib-cage obtained using a non-invasive  respiratory inductance plethysmography  sensor to predict minute ventilation as observed from a face\/head mounted exercise spirometer. The system is used to track physical effort closely matching our perception of actual exercise intensity.  The source code for the demo is available here: https:\/\/github.com\/simula-vias\/DeepVentilation<\/jats:p>","DOI":"10.24963\/ijcai.2020\/753","type":"proceedings-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:12:49Z","timestamp":1594260769000},"page":"5231-5233","source":"Crossref","is-referenced-by-count":4,"title":["DeepVentilation: Learning to Predict Physical Effort from Breathing"],"prefix":"10.24963","author":[{"given":"Sagar","family":"Sen","sequence":"first","affiliation":[{"name":"Simula Research Laboratory"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Bernab\u00e9","sequence":"additional","affiliation":[{"name":"Simula Research Laboratory"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik  Johannes B.L.G.","family":"Husom","sequence":"additional","affiliation":[{"name":"University of Oslo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:17:01Z","timestamp":1594261021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/753"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/753","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}