Synthetic Sensor Data for Human Activity Recognition

Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance. In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier

Syntax-directed amorphous slicing

An amorphous slice of a program is constructed with respsct to a set of variables. The amorphous slice is an executable program which preserves the behaviour of the original on the variables of interest. Unlike syntax-preserving slices, amorphous slices need not preserve a projection of the syntax of a program. This makes the task of amorphous slice construction harder, but it also often makes the result thinner and thereby preferable in applications where syntax preservation is unimportant. This paper describes an approach to the construction of amorphous slices which is based on the Abstract Syntax Tree of the program to be sliced, and does not require the construction of control flow graphs nor of program dependence graphs. The approach has some strengths and weaknesses which the paper discusses. The amorphous slicer, is part of the GUSTT slicing system, which includes syntax preserving static and conditioned slicers, a side effect removal transformation phase, slicing criterion guidance and for which much of the correctness proofs for transformation steps are mechanically verified. The system handles a subset of WSL, into which more general WSL constructs can be transformed. The paper focuses upon the way in which the GUSTT System uses dependence reduction transformation tactics. Such dependence reduction is at the heart of all approaches to amorphous slicing. The algorithms used are described and their performance is assessed with a simple empirical study of best and worst case execution times for an implementation built on top of the FermaT transformation system for maintenance and re-engineering