Detecting falls with X-Factor Hidden Markov Models

Graphical abstractDisplay Omitted HighlightsProposed new X-Factor Hidden Markov Models to identify falls in the absence of their training data.Proposed a novel cross-validation method to optimize parameters in the absence of fall data.Experimentally showed that performance of supervised classifiers deteriorate with very limited training fall data. Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs rarely and infrequently. This poses a challenge for traditional supervised classification algorithms, because there may be very little training data for falls (or none at all) to build generalizable models for falls. This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three X-Factor Hidden Markov Model (XHMMs) approaches. The XHMMs have inflated output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove outliers from the normal ADL that serves as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data is available during the training phase.