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WO2009090584A3 - Method and system for activity recognition and its application in fall detection - Google Patents

Method and system for activity recognition and its application in fall detection Download PDF

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Publication number
WO2009090584A3
WO2009090584A3 PCT/IB2009/050093 IB2009050093W WO2009090584A3 WO 2009090584 A3 WO2009090584 A3 WO 2009090584A3 IB 2009050093 W IB2009050093 W IB 2009050093W WO 2009090584 A3 WO2009090584 A3 WO 2009090584A3
Authority
WO
WIPO (PCT)
Prior art keywords
activity
unknown
classifying
feature vector
class classifier
Prior art date
Application number
PCT/IB2009/050093
Other languages
French (fr)
Other versions
WO2009090584A2 (en
Inventor
Ningjiang Chen
Xin Chen
Original Assignee
Koninklijke Philips Electronics N.V.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics N.V. filed Critical Koninklijke Philips Electronics N.V.
Publication of WO2009090584A2 publication Critical patent/WO2009090584A2/en
Publication of WO2009090584A3 publication Critical patent/WO2009090584A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The invention relates to an activity recognition method, which comprises the steps of deriving (120) a feature vector characterizing an activity from sensing data associated with the activity; classifying (130) the activity on the basis of the feature vector and at least one one-class classifier model relating to known activities so as to determine whether the activity is a known activity or an unknown activity; and training (140) a temporary one-class classifier model relating to an unknown activity when the activity is determined as an unknown activity. In an embodiment, the method further comprises a step (150) of classifying the activity on the basis of the feature vector and a multi-class classifier to identify the group to which the known activity belongs, when the activity is determined as a known activity. By identifying an activity as a known or unknown activity, and further training an identified unknown activity, the method reduces the probability of classifying a new activity as a known activity by mistake.
PCT/IB2009/050093 2008-01-18 2009-01-12 Method and system for activity recognition and its application in fall detection WO2009090584A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN200810003518 2008-01-18
CN200810003518.5 2008-01-18

Publications (2)

Publication Number Publication Date
WO2009090584A2 WO2009090584A2 (en) 2009-07-23
WO2009090584A3 true WO2009090584A3 (en) 2009-10-29

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2009/050093 WO2009090584A2 (en) 2008-01-18 2009-01-12 Method and system for activity recognition and its application in fall detection

Country Status (1)

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WO (1) WO2009090584A2 (en)

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8249830B2 (en) * 2009-06-19 2012-08-21 Xerox Corporation Method and system for automatically diagnosing faults in rendering devices
US20140089024A1 (en) * 2011-05-26 2014-03-27 Koninklijke Philips N.V. Control device for resource allocation
US20140244209A1 (en) * 2013-02-22 2014-08-28 InvenSense, Incorporated Systems and Methods for Activity Recognition Training
CN103398843B (en) * 2013-07-01 2016-03-02 西安交通大学 Based on the epicyclic gearbox sun gear Fault Classification of many classification Method Using Relevance Vector Machines
CA2939633A1 (en) 2014-02-14 2015-08-20 3M Innovative Properties Company Activity recognition using accelerometer data
CN103984921B (en) * 2014-04-29 2017-06-06 华南理工大学 A kind of three axle Feature fusions for human action identification
CA2961370A1 (en) 2014-09-15 2016-03-24 3M Innovative Properties Company Impairment detection with environmental considerations
EP3193715B1 (en) 2014-09-15 2024-05-15 Attenti Electronic Monitoring Ltd. Impairment detection
WO2017039684A1 (en) * 2015-09-04 2017-03-09 Hewlett Packard Enterprise Development Lp Classifier
CN107527016B (en) * 2017-07-25 2020-02-14 西北工业大学 User identity identification method based on motion sequence detection in indoor WiFi environment
CN108008151A (en) * 2017-11-09 2018-05-08 惠州市德赛工业研究院有限公司 A kind of moving state identification method and system based on 3-axis acceleration sensor
US10545578B2 (en) 2017-12-22 2020-01-28 International Business Machines Corporation Recommending activity sensor usage by image processing
CN108960056B (en) * 2018-05-30 2022-06-03 西南交通大学 Fall detection method based on attitude analysis and support vector data description
CN109086667A (en) * 2018-07-02 2018-12-25 南京邮电大学 Similar active recognition methods based on intelligent terminal
CN110428016A (en) * 2019-08-08 2019-11-08 中国联合网络通信集团有限公司 Feature vector generation method and system, user's identification model generation method and system
CN110659624A (en) * 2019-09-29 2020-01-07 上海依图网络科技有限公司 Group personnel behavior identification method and device and computer storage medium
EP3828855A1 (en) 2019-11-29 2021-06-02 Koninklijke Philips N.V. Personalized fall detector

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Patent Citations (1)

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Publication number Priority date Publication date Assignee Title
US20020135484A1 (en) * 2001-03-23 2002-09-26 Ciccolo Arthur C. System and method for monitoring behavior patterns

Non-Patent Citations (5)

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Title
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TAO XIANG ET AL: "Video Behavior Profiling for Anomaly Detection", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 30, no. 5, 25 June 2007 (2007-06-25), IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, pages 893 - 908, XP011224141, ISSN: 0162-8828 *
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