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 naïve 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.
Nazefrad et al. REF compared the performance of HMMs and CRFs for activity recognition in a smart home environment, using real time data from motion and temperature sensors.
Conditional random fields for activity recognition in smart environments
IHI
Computer Science