Low-cost physical sign monitoring method and system based on deep learning
The invention discloses a low-cost physical sign monitoring method and system based on deep learning. Comprising the following steps: shooting the activity of a monitored object by using an infrared camera to obtain an IRT image within a period of time; marking and pre-processing the IRT image frame...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | TAN MINYI LI JINMING HAN GUANYA |
| description | The invention discloses a low-cost physical sign monitoring method and system based on deep learning. Comprising the following steps: shooting the activity of a monitored object by using an infrared camera to obtain an IRT image within a period of time; marking and pre-processing the IRT image frame to obtain a data set with two types of target bounding box marks of the head and the chest of the monitored object; training and verifying a target detection model by using the data set; positioning the head of a monitored object by using the target detection model, and estimating a temperature change trend; and using a time filtering algorithm and an optical flow algorithm for the obtained IRT image of the monitored object to estimate the respiratory rate. The low-resolution infrared camera is used for shooting the IRT image of the monitored object, the object detection model based on deep learning is lower in application cost, the comfort of the monitored object can be guaranteed, few constraint conditions are u |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115512178A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115512178A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115512178A3</originalsourceid><addsrcrecordid>eNqNyrEKwjAQANAsDlL9h_MDOkQpupZiEREn93ImZxtI7kIvIP17HfwAp7e8tbne5F070QJ5WjQ4jKBhZEjCocgceIREZRIPyB500UIJnqjkQRg8UYZIOPM3bszqhVFp-7Myu_786C41ZRlIMzpiKkN3t7Zp7N4eT-3hn_MBH_00_g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Low-cost physical sign monitoring method and system based on deep learning</title><source>esp@cenet</source><creator>TAN MINYI ; LI JINMING ; HAN GUANYA</creator><creatorcontrib>TAN MINYI ; LI JINMING ; HAN GUANYA</creatorcontrib><description>The invention discloses a low-cost physical sign monitoring method and system based on deep learning. Comprising the following steps: shooting the activity of a monitored object by using an infrared camera to obtain an IRT image within a period of time; marking and pre-processing the IRT image frame to obtain a data set with two types of target bounding box marks of the head and the chest of the monitored object; training and verifying a target detection model by using the data set; positioning the head of a monitored object by using the target detection model, and estimating a temperature change trend; and using a time filtering algorithm and an optical flow algorithm for the obtained IRT image of the monitored object to estimate the respiratory rate. The low-resolution infrared camera is used for shooting the IRT image of the monitored object, the object detection model based on deep learning is lower in application cost, the comfort of the monitored object can be guaranteed, few constraint conditions are u</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DIAGNOSIS ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221223&DB=EPODOC&CC=CN&NR=115512178A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25555,76308</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221223&DB=EPODOC&CC=CN&NR=115512178A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>TAN MINYI</creatorcontrib><creatorcontrib>LI JINMING</creatorcontrib><creatorcontrib>HAN GUANYA</creatorcontrib><title>Low-cost physical sign monitoring method and system based on deep learning</title><description>The invention discloses a low-cost physical sign monitoring method and system based on deep learning. Comprising the following steps: shooting the activity of a monitored object by using an infrared camera to obtain an IRT image within a period of time; marking and pre-processing the IRT image frame to obtain a data set with two types of target bounding box marks of the head and the chest of the monitored object; training and verifying a target detection model by using the data set; positioning the head of a monitored object by using the target detection model, and estimating a temperature change trend; and using a time filtering algorithm and an optical flow algorithm for the obtained IRT image of the monitored object to estimate the respiratory rate. The low-resolution infrared camera is used for shooting the IRT image of the monitored object, the object detection model based on deep learning is lower in application cost, the comfort of the monitored object can be guaranteed, few constraint conditions are u</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQANAsDlL9h_MDOkQpupZiEREn93ImZxtI7kIvIP17HfwAp7e8tbne5F070QJ5WjQ4jKBhZEjCocgceIREZRIPyB500UIJnqjkQRg8UYZIOPM3bszqhVFp-7Myu_786C41ZRlIMzpiKkN3t7Zp7N4eT-3hn_MBH_00_g</recordid><startdate>20221223</startdate><enddate>20221223</enddate><creator>TAN MINYI</creator><creator>LI JINMING</creator><creator>HAN GUANYA</creator><scope>EVB</scope></search><sort><creationdate>20221223</creationdate><title>Low-cost physical sign monitoring method and system based on deep learning</title><author>TAN MINYI ; LI JINMING ; HAN GUANYA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115512178A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>TAN MINYI</creatorcontrib><creatorcontrib>LI JINMING</creatorcontrib><creatorcontrib>HAN GUANYA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>TAN MINYI</au><au>LI JINMING</au><au>HAN GUANYA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Low-cost physical sign monitoring method and system based on deep learning</title><date>2022-12-23</date><risdate>2022</risdate><abstract>The invention discloses a low-cost physical sign monitoring method and system based on deep learning. Comprising the following steps: shooting the activity of a monitored object by using an infrared camera to obtain an IRT image within a period of time; marking and pre-processing the IRT image frame to obtain a data set with two types of target bounding box marks of the head and the chest of the monitored object; training and verifying a target detection model by using the data set; positioning the head of a monitored object by using the target detection model, and estimating a temperature change trend; and using a time filtering algorithm and an optical flow algorithm for the obtained IRT image of the monitored object to estimate the respiratory rate. The low-resolution infrared camera is used for shooting the IRT image of the monitored object, the object detection model based on deep learning is lower in application cost, the comfort of the monitored object can be guaranteed, few constraint conditions are u</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN115512178A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS HUMAN NECESSITIES HYGIENE IDENTIFICATION MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY |
| title | Low-cost physical sign monitoring method and system based on deep learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T01%3A13%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=TAN%20MINYI&rft.date=2022-12-23&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115512178A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |