A Scalable Hybrid Activity Recognition Approach for Intelligent Environments

Human activity recognition is a key technology for ICT-based (infomation and communication technologies) assistive applications. The most successful activity recognition systems for intelligent environments in terms of performance rely on supervised learning techniques. However, those techniques demand large labelled data sets for specific sensor deployments and monitored person. Such requirements make supervised learning techniques not to scale well to real world deployments, where different sensor infrastructures may be used to monitor different users. In this paper, we present a novel activity recognition system, based on a combination of unsupervised learning techniques and knowledge-based activity models. First, we use a domain-specific data mining algorithm previously developed by Cook et al. to extract the most frequent action sequences executed by a person. Second, we insert knowledge-based activity models in a novel matching algorithm with the aim of inferring what activities are being performed in a given action sequence. The approach results on a scalable activity recognition system, which has been tested on three real data sets. The obtained performance is comparable to supervised learning techniques.