Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
Abstract Recognising human activities from sensors embedded in an environment or worn on bodies is an important and challenging research topic in pervasive computing. Existing work on activity recognition is mainly concerned with identifying single user sequential activities from well-scripted or pre-segmented sequences of sensor events. However a real-world environment often contains multiple users, with each performing activities simultaneously, in their own way and with no explicit instructions to follow. Recognising multi-user concurrent activities is challenging, but essential for designing applications for real environments. This paper presents a novel Knowledge-driven approach for Concurrent Activity Recognition (KCAR). Within KCAR, we explore the semantics underlying each sensor event and use semantic dissimilarity to segment a continuous sensor sequence into fragments, each of which corresponds to one ongoing activity. We exploit the Pyramid Match Kernel, with a strength in approximate matching on hierarchical concepts, to recognise activities of varying grained constraints from a potentially noisy sensor sequence. We conduct an empirical evaluation on a large-scale real-world data set that is collected over one year and consists of 2.8 millions of sensor events. Our results demonstrate that KCAR achieves an average recognition accuracy of 91 . When given a single static picture, humans can not only interpret the instantaneous content captured by the image, but also they are able to infer the chain of dynamic events that are likely to happen in the near future. Similarly, when a human observes a short video, it is easy to decide if the event taking place in the video is normal or unexpected, even if the video depicts a an unfamiliar place for the viewer. This is in contrast with work in surveillance and outlier event detection, where the models rely on thousands of hours of video recorded at a single place in order to identify what constitutes an unusual event. In this work we present a simple method to identify videos with unusual events in a large collection of short video clips. The algorithm is inspired by recent approaches in computer vision that rely on large databases. In this work we show how, relying on large collections of videos, we can retrieve other videos similar to the query to build a simple model of the distribution of expected motions for the query. Consequently, the model can evaluate how unusual is the video as well as make event predictions. We show how a very simple retrieval model is able to provide reliable results.