This paper proposes a multi-level feature learning framework for human action recognition\u00a0using a single body-worn inertial sensor. The framework consists of three phases, respectively\u00a0designed to analyze signal-based (low-level), components (mid-level) and semantic\u00a0(high-level) information. Low-level features capture the time and frequency domain\u00a0property while mid-level representations learn the composition of the action. The Maxmargin\u00a0Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic\u00a0descriptions of latent action patterns as the output of our framework. The proposed method\u00a0achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F1 score)\u00a0respectively, on Skoda, WISDM and OPP datasets.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes a multi-level feature learning framework for human action recognition\u00a0using a single body-worn inertial sensor. The framework consists of three phases, respectively\u00a0designed to analyze signal-based (low-level), components (mid-level) and semantic\u00a0(high-level) information. Low-level features capture the time and frequency domain\u00a0property while mid-level representations learn the composition of the action. The Maxmargin\u00a0Latent Pattern Learning (MLPL) […]<\/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":"Pervasive and Mobile Computing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Pervasive and Mobile Computing","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","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":"2017-07-01","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":[13553],"msr-publication-type":[193715],"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-490904","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Pervasive and Mobile Computing","msr_affiliation":"","msr_published_date":"2017-07-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Pervasive and Mobile Computing","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":"490949","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"[2017][SCI][PAMC]Learning multi-level features for sensor-based human action recognition","viewUrl":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-content\/uploads\/2018\/06\/2017SCIPAMCLearning-multi-level-features-for-sensor-based-human-action-recognition.pdf","id":490949,"label_id":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":[],"msr-author-ordering":[{"type":"text","value":"Yan Xu","user_id":0,"rest_url":false},{"type":"text","value":"Zhengyang Shen","user_id":0,"rest_url":false},{"type":"text","value":"Xin Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Yifan Gao","user_id":0,"rest_url":false},{"type":"text","value":"Shujian Deng","user_id":0,"rest_url":false},{"type":"text","value":"Yipei Wang","user_id":0,"rest_url":false},{"type":"text","value":"Yubo Fan","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Eric Chang","user_id":31709,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Eric Chang"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170702],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":170702,"post_title":"eHuatuo: Teaching Computer to Read Medical Records","post_name":"ehuatuo-teaching-computer-to-read-medical-records","post_type":"msr-project","post_date":"2011-04-10 20:16:13","post_modified":"2019-05-16 04:27:03","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/ehuatuo-teaching-computer-to-read-medical-records\/","post_excerpt":"eHuatuo is an eHealthcare project about Teaching Computer to Read Medical Records developed by Microsoft Research Asia. 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