X-Factor HMMs for detecting falls in the absence of fall-specific training data

Shehroz S. Khan, Michelle E. Karg, Dana Kulić, Jesse Hoey

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

17 Citations (Scopus)


Detection of falls is very important from a health and safety perspective. However, falls occur rarely and infrequently, which leads to either limited or no training data and thus can severely impair the performance of supervised activity recognition algorithms. In this paper, we address the problem of identification of falls in the absence of training data for falls, but with abundant training data for normal activities. We propose two ‘X-Factor’ Hidden Markov Model (XHMMs) approaches that are like normal HMMs, but have “inflated” output covariances (observation models), which can be estimated using cross-validation on the set of ‘outliers’ in the normal data that serve as proxies for the (unseen) fall data. This allows the XHMMs to be learned from only normal activity data. We tested the proposed XHMM approaches on two real activity recognition datasets that show high detection rates for falls in the absence of training data.

Original languageEnglish
Title of host publicationAmbient Assisted Living and Daily Activities
Subtitle of host publication6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014 Proceedings
EditorsLeandro Pecchia, Liming Luke Chen, Chris Nugent, José Bravo
Place of PublicationCham Switzerland
Number of pages9
ISBN (Electronic)9783319131054
ISBN (Print)9783319131047
Publication statusPublished - 2014
Externally publishedYes
EventInternational Work-Conference on Ambient Assisted Living 2014 - Belfast, United Kingdom
Duration: 2 Dec 20145 Dec 2014

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Work-Conference on Ambient Assisted Living 2014
Abbreviated titleIWAAL 2014
Country/TerritoryUnited Kingdom


  • Fall Detection
  • Hidden Markov Models
  • Outlier Detection
  • X-Factor

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