Abstract
The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.
Highlights
Wearable sensor technologies are gaining interest in different research communities due to the use of significantly miniaturized electronic components, with low power consumption, which makes them ideal for applications in human activity recognition for both indoor and outdoor environments [1].These applications allow users to achieve a natural execution of any physical activity, while providing good results in multiple practical applications, such as health rehabilitation, respiratory and muscular activity assessment, sports and safety applications [2]
We present a novel method for classifying human locomotion activities, such as walk, stand, lie and sit, by implementing a data-driven architecture based on an iterative learning framework
We address the problem of classifying human locomotion by defining a learning framework based on an iterative learning multi-class classification supported by a multi-class support vector machines (SVM) classifier that incorporates the maximum-margin principle to select the best sample candidates
Summary
Wearable sensor technologies are gaining interest in different research communities due to the use of significantly miniaturized electronic components, with low power consumption, which makes them ideal for applications in human activity recognition for both indoor and outdoor environments [1]. These applications allow users to achieve a natural execution of any physical activity, while providing good results in multiple practical applications, such as health rehabilitation, respiratory and muscular activity assessment, sports and safety applications [2]. Considering these restrictions prevalent in multimodal sensor data fusion [4], which is the case of the work reported in this paper, feature extraction becomes a critical component for finding multi-variable correlations that allow the classifier to improve the model precision while producing a low misclassification rate
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