conditional random fields for activity recognition in smart environments
One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by nave Bayes classifiers and hidden Markov models (HMMs). In this paper, we describe the use of another probabilistic model: Conditional Random Fields (CRFs), which is currently gaining popularity for its remarkable performance for activity recognition. Our focus is on using CRFs to recognize activities performed by an inhabitant in a smart home environment and our goal is to validate the claim of its higher or similar performance by comparing CRFs with HMMs using data collected in a real smart home.
conditional random fields activity recognition smart environments machine learning health assistance