Improving Human Activity Recognition in Smart Homes

Improving Human Activity Recognition in Smart Homes

M'Hamed Bilal Abidine (University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria), Lamya Fergani (University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria), Belkacem Fergani (University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria) and Anthony Fleury (Mines Douai, URIA, Douai, France and University of Lille, Lille, France)
Copyright: © 2015 |Pages: 19
DOI: 10.4018/IJEHMC.2015070102
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Abstract

Even if it is now simple and cheap to collect sensors information in a smart home environment, the main issue remains to infer high-level activities from these simple readings. The main contribution of this work is twofold. Firstly, the authors demonstrate the efficiency of a new procedure for learning Optimized Cost-Sensitive Support Vector Machines (OCS-SVM) classifier based on the user inputs to appropriately tackle the problem of class imbalanced data. It uses a new criterion for the selection of the cost parameter attached to the training errors. Secondly, this method is assessed and compared with the Conditional Random Fields (CRF), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (k-NN) and the traditional SVM. Several and various experimental results obtained on multiple real world human activity datasets using binary and ubiquitous sensors show that OCS-SVM outperforms the previous state-of-the-art classification approach.
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Introduction

Recognition of Human Activities is one of the most important tasks in pervasive computing applications (Tentori, 2008; Stiefmeier, 2008; Tapia, 2004; Van Kasteren, 2008). It is a thriving research field. It aims to identify high-level activities such as cooking, brushing, dressing, bathing and so on, performed in a ubiquitous environment, with real-time response. Due to an increasing number of elderly people and especially the ones living alone, wide-spread deployment of sensors has become prominent in home environments. Smart homes (Van Kasteren, 2008; Fleury, 2010) are equipped with sensor networks used, after information fusion, to automatically recognize activities of the inhabitant and to detect medical emergencies or assist sick/elderly people at home, and thus enabling them to live longer on their own. Therefore, they enable intelligent control by residential occupants to various automations. In this approach, sensors can be attached to either a person (wearable) under observation or objects that constitute the environment. When a resident moves from one room to another or uses different objects that on which area sensor, a series of firings with the corresponding timestamps are generated, allowing to automatically detect which activity the resident is currently performing, its duration and what objects are used for this activity. In this work, we focus on unobtrusive long-term monitoring of daily activities of a single person at home, on a daily basis. To perform a correct assistance, a good understanding of the current activity performed by the dweller is important. Sensor data collected needs to be analysed using data mining and machine learning techniques (Van Kasteren, 2008; Fleury, 2010) to determine which activity is taking place. As for any pattern recognition task, the keys to successful Activity Recognition are: (i) appropriately design the feature representation of the sensor data; and (ii) design a suitable classifier to infer the activity. Supervised classification algorithms are trained with labelled samples to generate a model that will be used to classify activities performed by the user. The ubiquitous computing literature describes a wide variety of creatively applied classification approaches (Duda, 2001; Shawe-Taylor, 2004). The learning of such models is usually done in a supervised manner (human labelling) and requires a large annotated dataset recorded in different settings. State of the art methods used for such recognition can be divided into two main categories: the so-called generative models and the discriminative models (Duda, 2001; Shawe-Taylor, 2004). In this work, we are interested in the supervised discriminative models for its simplicity and good performances, associated with a fast prediction speed.

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