A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors

Abstract

Human activity recognition is important for many applications. This paper describes a human activity recognition framework based on feature selection techniques. The objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

“…Other wearable devices for Smart Houses have been reported [73][74][75]. However, this section is a summary of some of the sensor devices used within a Smart House.…”

Activity recognition in smart homes with self verification of assignments

“…In a PCA-SVM approach [13], PCA is used to extract the significant features and the multi-class SVM (one-versus-one) is applied for classification of activities. Using SVM for classification, the comparison of the feature selection methods, filter and wrapper based on single and sequential feature selection shows that wrapper method based on sequential feature selection performs better [44], since it also considers the redundancy of features during the selection process, unlike the other two.…”

“…Feature selection techniques have been applied in the activity recognition problem to select significant and discriminant subset of features [13,27,31,44]. Minimum Redundancy Maximum Relevance (mRMR) can be used to select the best feature subset for target classes, then SVM is used for the classification of activities [27].…”

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