{"id":776404,"date":"2021-09-20T13:12:00","date_gmt":"2021-09-20T20:12:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=776404"},"modified":"2023-05-11T10:58:35","modified_gmt":"2023-05-11T17:58:35","slug":"height-estimation-of-children-under-five-years-using-depth-images","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/height-estimation-of-children-under-five-years-using-depth-images\/","title":{"rendered":"Height Estimation of Children under Five Years using Depth Images"},"content":{"rendered":"

Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of standing children under five years from depth images collected using a smart-phone. According to the SMART Methodology Manual [5], the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved an average mean absolute error of 1.64% on 57064 test images. For 70.3% test images, we estimated height accurately within the acceptable 1.4 cm range. Thus, our proposed solution can accurately detect stunting (low height-for-age) in standing children below five years of age.<\/p>\n","protected":false},"excerpt":{"rendered":"

Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13553],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-776404","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-11-4","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Eng Med Biol Soc","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/pubmed.ncbi.nlm.nih.gov\/34892081\/","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Anusua Trivedi","user_id":40732,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Anusua Trivedi"},{"type":"text","value":"Mohit Jain","user_id":0,"rest_url":false},{"type":"text","value":"Nikhil Kumar Gupta","user_id":0,"rest_url":false},{"type":"text","value":"Markus Hinsche","user_id":0,"rest_url":false},{"type":"text","value":"Prashant Singh","user_id":0,"rest_url":false},{"type":"text","value":"Markus Matiaschek","user_id":0,"rest_url":false},{"type":"text","value":"Tristan Behrens","user_id":0,"rest_url":false},{"type":"text","value":"Mirco Militeri","user_id":0,"rest_url":false},{"type":"text","value":"Cameron Birge","user_id":0,"rest_url":false},{"type":"text","value":"Shivangi Kaushik","user_id":0,"rest_url":false},{"type":"text","value":"Archisman Mohapatra","user_id":0,"rest_url":false},{"type":"text","value":"Rita Chatterjee","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Rahul Dodhia","user_id":41401,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Rahul Dodhia"},{"type":"user_nicename","value":"Juan M. 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