{"id":744916,"date":"2021-05-08T06:40:57","date_gmt":"2021-05-08T13:40:57","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=744916"},"modified":"2021-05-08T06:40:57","modified_gmt":"2021-05-08T13:40:57","slug":"foodscrap-promoting-rich-data-capture-and-reflective-food-journaling-through-speech-input","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/foodscrap-promoting-rich-data-capture-and-reflective-food-journaling-through-speech-input\/","title":{"rendered":"FoodScrap: Promoting Rich Data Capture and Reflective Food Journaling Through Speech Input"},"content":{"rendered":"<p>The factors influencing people\u2019s food decisions, such as one\u2019s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (<em>N<\/em> = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around rerecording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The factors influencing people\u2019s food decisions, such as one\u2019s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a [&hellip;]<\/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":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ACM Designing Interactive Systems (DIS)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2021-6","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-744916","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-6","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","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":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/05\/FoodScrap-DIS2021.pdf","id":"744919","title":"foodscrap-dis2021","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":744919,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/05\/FoodScrap-DIS2021.pdf"}],"msr-author-ordering":[{"type":"text","value":"Yuhan Luo","user_id":0,"rest_url":false},{"type":"text","value":"Young-Ho Kim","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Bongshin Lee","user_id":31276,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Bongshin Lee"},{"type":"text","value":"Naeemul Hassan","user_id":0,"rest_url":false},{"type":"guest","value":"eun-kyoung-choe","user_id":365921,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=eun-kyoung-choe"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[371909,379814],"msr_project":[365810],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":365810,"post_title":"Human-Data Interaction for Self-Monitoring","post_name":"human-data-interaction","post_type":"msr-project","post_date":"2017-02-24 09:52:42","post_modified":"2023-02-07 09:16:18","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/human-data-interaction\/","post_excerpt":"Empowering people to improve their lives by fully leveraging the data about themselves &nbsp; In recent years, we have witnessed rapid advancements in consumer health technologies. People are also increasingly tracking personal health data outside the clinic using wearable sensing devices and mobile health applications. For example, we have observed the rise of the Quantified Self (QS) movement, which aims to improve health, maximize work performance, and find new life experiences through self-tracking. 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