Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware

Activity recognition in naturalistic environments using body-worn sensors

Phd ThesisThe research presented in this thesis investigates how deep learning and feature learning can address challenges that arise for activity recognition systems in naturalistic, ecologically valid surroundings such as the private home. One of the main aims of ubiquitous computing is the development of automated recognition systems for human activities and behaviour that are sufficiently robust to be deployed in realistic, in-the-wild environments. In most cases, the targeted application scenarios are people’s daily lives, where systems have to abide by practical usability and privacy constraints. We discuss how these constraints impact data collection and analysis and demonstrate how common approaches to the analysis of movement data effectively limit the practical use of activity recognition systems in every-day surroundings. In light of these issues we develop a novel approach to the representation and modelling of movement data based on a data-driven methodology that has applications in activity recognition, behaviour imaging, and skill assessment in ubiquitous computing. A number of case studies illustrate the suitability of the proposed methods and outline how study design can be adapted to maximise the benefit of these techniques, which show promising performance for clinical applications in particular.SiDE research hu