Hidden Markov Model for early prediction of the elderly's dependency evolution in ambient assisted living

The integration of information and communication technologies (ICT) can be of great utility in monitoring and evaluating the elderly's health condition and its behavior in performing Activities of Daily Living (ADL) in the perspective to avoid, as long as possible, the delays of recourse to health care institutions (e.g., nursing homes and hospitals). In this research, we propose a predictive model for detecting behavioral and health-related changes in a patient who is monitored continuously in an assisted living environment. We focus on keeping track of the dependency level evolution and detecting the loss of autonomy for an elderly person using a Hidden Markov Model based approach. In this predictive process, we were interested in including the correlation between cardiovascular history and hypertension as it is considered the primary risk factor for cardiovascular diseases, stroke, kidney failure and many other diseases. Our simulation was applied to an empirical dataset that concerned 3046 elderly persons monitored over 9 years. The results show that our model accurately evaluates person's dependency, follows his autonomy evolution over time and thus predicts moments of important changes.