Action Recognition in Real Homes using Low Resolution Depth Video Data

Abstract

We report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting and storing data from real homes, and preliminary results on action recognition using low resolution depth video cameras. The data have been collected from seven apartments with one resident each over a period of two weeks. We propose a pre-processing of the depth videos by applying an Infinite Response Filter (IIR) for extracting the movements in the frames prior to classification. In this work we classify four actions: TV interaction (turn it on/ off and switch over), standing up, sitting down, and no movement. Our first results indicate that using the IIR filter for movement information extraction improves accuracy and can be an efficient method for recognizing actions. Our current implementation uses a convolutional long short-term memory (ConvLSTM) neural network, and achieved an average peak accuracy of 86%.