Abstract

The advancements in Ambient Assisted Living (AAL) have been prompted by the growing population of elderly individuals facing diagnoses such as Dementia or Alzheimer’s, aiming to enhance their overall quality of life. To provide support it is important to know their daily activities and aid them when needed. A large portion of research in the field of Human Activity Recognition uses black box learning approaches such as deep learning, but there are cases where model based methods, such as Virtual Stochastic Sensors (VSSs) are competitive. This is possible because the model based methods can include system structure in the modeling process if it is known. VSS’s are derived from Hidden Markov Models (HMM) and applied to single resident datasets, which are collected in apartments fitted with different types of ambient sensors. For future applications a generalization of behavior, sensors or models is necessary so that models are not just trained and used for one specific apartment and setup. In this paper we test different model and ac-tivity setups while training and testing on different apartments. The results show that a model trained on a set of similar apartments can be used for behavior reconstruction on an apartment outside of that training set.