Extension of HMM-Based ADL Recognition With Markov Chains of Activities and Activity Transition Cost

Ambient assisted living in smart home environments is becoming an important goal in an aging society with challenges in elderly care. A key component in such environments is the accurate recognition of activities of daily living from various sensor data. Recent research directions explored several classification methods, including hidden Markov models. This research presents a hidden Markov model-based system for activity recognition, and extends it with a second-order Markov chain model of activity sequences to achieve long-term dependency in the model. We also introduce an activity transition cost to counteract the tendency of hidden Markov models to make a large number of transitions. The proposed models are used for activity recognition, with their scores being combined using heuristically determined weights for optimal performance. We also present a modified Viterbi algorithm, which incorporates both models and the activity transition cost. We used a dataset from the CASAS project to test and evaluate the proposed models. A comparison of the results shows the potential of introducing long term dependencies and the managing the number of activity transitions. We show results regarding the modeling ability to predict activity sequences, a comparison of predicted and actual activity transitions, and final recognition accuracy results. The results show an increase of total activity recognition accuracy from 93.9 % to 94.52 % on individual activities, and from 68.89 % to 70.95 % over the combination of all concurrent activities. The results also show a reduction of predicted activity transitions from 741 to 236, whereas the number of actual activity transitions in the evaluation set is 141.