Wearable Data Generation Using Time-Series Generative Adversarial Networks for Hydration Monitoring

Collection of biosignals data from wearable devices for machine learning tasks can sometimes be expensive and time-consuming and may violate privacy policies and regulations. Successful and accurate generation of these signals can help in many wearable devices applications as well as overcoming the privacy concerns accompanied with healthcare data. Generative adversarial networks (GANs) have been used successfully in generating images in data-limited situations. Using GANs for generating other types of data has been actively researched in the last few years. In this paper, we investigate the possibility of using a time-series GAN (TimeGAN) to generate wearable devices data for a hydration monitoring task to predict the last drinking time of a user. Challenges encountered in the case of biosignals generation and state-of-the-art methods for evaluation of the generated signals are discussed. Results have shown the applicability of using TimeGAN for this task based on quantitative and visual qualitative metrics. Limitations on the quality of the generated signals were highlighted with suggesting ways for improvement

Generative adversarial networks in time series: a systematic literature review

Generative adversarial network (GAN) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, makingsignificantadvancements.Althoughthesecomputervisionadvanceshavegarneredmuch attention, GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high-quality, diverse, and private time series data. In this article, we review GAN variants designed for time series related applications. We propose a classification of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Here we showcase the latest and most popular literature in this field— their architectures, results, and applications. We also provide a list of the most popular evaluation metrics and their suitability across applications. Also presented is a discussion of privacy measures for these GANs and further protections and directions for dealing with sensitive data. We aim to frame clearly and concisely the latest and state-of-the-art research in this area and their applications to real-world technologies

Deep learning-based signal processing approaches for improved tracking of human health and behaviour with wearable sensors

This thesis explores two lines of research in the context of sequential data and machine learning in the remote environment, i.e., outside the lab setting - using data acquired from wearable devices. Firstly, we explore Generative Adversarial Networks (GANs) as a reliable tool for time series generation, imputation and forecasting. Secondly, we investigate the applicability of novel deep learning frameworks to sequential data processing and their advantages over traditional methods. More specifically, we use our models to unlock additional insights and biomarkers in human-centric datasets. Our first research avenue concerns the generation of sequential physiological data. Access to physiological data, particularly medical data, has become heavily regulated in recent years, which has presented bottlenecks in developing computational models to assist in diagnosing and treating patients. Therefore, we explore GAN models to generate medical time series data that adhere to privacy-preserving regulations. We present our novel methods of generating and imputing synthetic, multichannel sequential medical data while complying with privacy regulations. Addressing these concerns allows for sharing and disseminating medical data and, in turn, developing clinical research in the relevant fields. Secondly, we explore novel deep learning technologies applied to human-centric sequential data to unlock further insights while addressing the idea of environmentally sustainable AI. We develop novel deep learning processing methods to estimate human activity and heart rate through convolutional networks. We also introduce our ‘time series-to-time series GAN’, which maps photoplethysmograph data to blood pressure measurements. Importantly, we denoise artefact-laden biosignal data to a competitive standard using a custom objective function and novel application of GANs. These deep learning methods help to produce nuanced biomarkers and state-of-the-art insights from human physiological data. The work laid out in this thesis provides a foundation for state-of-the-art deep learning methods for sequential data processing while keeping a keen eye on sustain- able AI

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules