Iterative Learning for Human Activity Recognition from Wearable Sensor Data

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.