31,275 research outputs found
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approache
Latent Markov model for longitudinal binary data: An application to the performance evaluation of nursing homes
Performance evaluation of nursing homes is usually accomplished by the
repeated administration of questionnaires aimed at measuring the health status
of the patients during their period of residence in the nursing home. We
illustrate how a latent Markov model with covariates may effectively be used
for the analysis of data collected in this way. This model relies on a not
directly observable Markov process, whose states represent different levels of
the health status. For the maximum likelihood estimation of the model we apply
an EM algorithm implemented by means of certain recursions taken from the
literature on hidden Markov chains. Of particular interest is the estimation of
the effect of each nursing home on the probability of transition between the
latent states. We show how the estimates of these effects may be used to
construct a set of scores which allows us to rank these facilities in terms of
their efficacy in taking care of the health conditions of their patients. The
method is used within an application based on data concerning a set of nursing
homes located in the Region of Umbria, Italy, which were followed for the
period 2003--2005.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS230 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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