Comparative study of machine learning algorithms for activity recognition with data sequence in home-like environment

Activity recognition is a key problem in multi-sensor systems. With data collected from different sensors, a multi-sensor system identifies activities performed by the inhabitants. Since an activity always lasts a certain duration, it is beneficial to use data sequence for the desired recognition. In this work, we experiment several machine learning techniques, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Meta-Layer Network for solving this problem. We observe that (1) compare with “single-frame” activity recognition, data sequence based classification gives better performance; and (2) directly using data sequence information with a simple “mete layer” network model yields a better performance than memory based deep learning approaches.