Abstract
Wearable inertial measurement units incorporating accelerometers and gyroscopes are increasingly used for activity analysis and recognition. In this paper an activity classification algorithm is presented which includes a novel multi-step refinement with the aim of improving the classification accuracy of traditional approaches. To do so, after the classification takes place, information is extracted from the confusion matrix to focus the computational efforts on those activities with worse classification performance. It is argued that activities differ diversely from each other, therefore a specific set of features may be informative to classify a specific set of activities, but such informativeness should not necessarily be extended to a different activity set. This approach has shown promising results, achieving important classification accuracy improvements.
Highlights
Human activity recognition (HAR) is a challenging research area extensively investigated in the field of Ambient Assisted Living (AAL) (Suryadevara and Mukhopadhyay 2014)
Analysis of variance (ANOVA) K-best, principal component analysis (PCA) and singular value decomposition (SVD) are computed to find out the optimal subset of features/components for the description of the data set
These three feature reduction techniques are examined on the three different classifiers proposed; random forest, K-nearest neighbours (KNN) and support vector machines (SVM)
Summary
Human activity recognition (HAR) is a challenging research area extensively investigated in the field of Ambient Assisted Living (AAL) (Suryadevara and Mukhopadhyay 2014). Self-assessment of daily activities has shown to be subjective and variable, as a subject’s own assessment can differ from that of an expert in the field (Smith et al 2005). This fact explains the increasing attention on the development of automatic activities tracking systems for subject monitoring. Different sensor platforms are utilized with the aim of automatically monitoring a person in a home environment. Research efforts are currently shifting towards wearable solutions, which avoid occlusion and the privacy concerns related to the use of video cameras in a home environment. Previous surveys regarding the acceptability of the use of wearable devices showed positive results, in adults and within the elderly population (Nelson et al 2016; O’Brien et al 2015)
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