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.

Wearable sensor technologies are a key component in the design of applications for human activity recognition, in areas like healthcare, sports and safety. In this paper, we present an iterative learning method to classify human locomotion activities extracted from the Opportunity dataset by implementing a data-driven architecture. Data collected by twelve 3D acceleration sensors and seven inertial measurement units are de-noised using a wavelet filter, prior to the extraction of statistical parameters of kinematical features, such as Principal Components Analysis and Singular Value Decomposition of roll, pitch, yaw and the norm of the axial components. A novel approach is proposed to minimize the number of samples required to classify walk, stand, lie and sit human locomotion activities based on these features. The methodology consists in an iterative extraction of the best candidates for building the training dataset. The best training candidates are selected when the Euclidean distance between an input data and its cluster’s centroid is larger than the mean plus the standard deviation of all Euclidean distances between all input data and their corresponding clusters. The resulting datasets are then used to train an SVM multi-class classifier that produces the lowest prediction error. The learning method presented in this paper 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 aspect given the large size of the dataset.