Synthetic data offer a number of advantages over using ground truth data when working with private and personal information about individuals. Firstly, the risk of identifying individuals is reduced considerably, which enables the sharing of data for analysis amongst more organisations. Secondly, the fine tuning of synthetic datapoints to suit particular modelling and analyses could help to build more suitable models that can avoid biases found in the original ground truth data. In this paper we explore how a probabilistic synthetic data generator can be used to model data with high enough fidelity that it can be used to develop and validate state-of-the-art machine learning models. In particular, we use a Bayesian network model trained on gestational diabetes data, generated from a mobile health app collected from a number of health trusts in the UK. These data are used to train and test an established machine learning model developed by Sensyne Health using real-world data, and the resulting performance is compared to performance on ground truth data. In addition, a clinical validation is undertaken to explore if human experts can differentiate real patients from synthetic ones. We demonstrate that the Bayesian network synthetic data generator is able to mimic the ground truth closely enough to make it difficult for a human expert to distinguish between the two. We show that the data generator captures the interactions between features and the multivariate distributions close enough to enable classifiers to be inferred that imitate the key performance characteristics of models inferred from ground truth data. What is more, we demonstrate that the discovered mis-classifications found when testing using the synthetic data, are as informative as when testing using ground truth data.

Collecting data from smartphones can provide useful information for health research, but developing mobile health (mHealth) apps requires extensive resources and implies often overlooked regulatory and privacy-related requirements. Mobile health "aggregators" like HealthKit or Google Fit and specialised platforms may allow reducing costs in mHealth research. Current platforms, however, do not efficiently exploit mHealth aggregators and provide little attention to regulations. We propose an open-source platform that can be used by health researchers without the need to develop custom apps. The platform has a strong focus on regulatory compliance, patient consent and transparency, and allows collecting data through electronic surveys and querying aggregators. We tested the feasibility of our platform in a pilot study with 18 healthy volunteers. The results show that the participants' app is usable and well-accepted and is able to frequently collect data about physical activity from both phones and connected wearables. Some limitations were identified regarding data loss because of insufficient connectivity and the impossibility of extracting data from wearables that are not compatible with aggregators.

We describe an evaluation of photoplethysmography (PPG) signals with two wavelengths channels (infrared and red) using a wrist-worn sensor for the estimation of heart rate variability (HRV) and oxygen saturation (SpO2). Five healthy subjects were equipped with a commercial wrist-worn pulse oximeter (Wavelet Health, USA) on the right hand, and both a commercial smart watch for fitness use (Huawei Watch, Series 2) and a clinically-validated transmission-mode pulse oximeter (Creative Medical PC-68B) on their left hand as a reference. Synchronised PPG signals from the Wavelet Health, the Huawei watch, and the PC-68B were recorded for approximately 10 minutes. Subjects were asked to leave the left hand in a resting state, while moving the right hand with two types of movement (periodic and random). A method is proposed to incorporate coupling information between the two wavelengths of PPGs based on the bivariate empirical mode decomposition algorithm. Our method is shown to improve the quality of red PPG allowing improved estimation of SpO2. A comparison of average heart rate (HR), HRV, and SpO2 from all devices is provided.

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

Extensive adoption of video surveillance, affecting many aspects of the daily life, alarms the concerned public about the increasing invasion into personal privacy. Therefore, to address privacy issues, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks. In this paper, we propose a subjective evaluation methodology to analyze the tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. As an example, the proposed method is used to compare several commonly employed privacy protection techniques, such as blurring, pixelization, and masking applied to indoor surveillance video. The results show that, for the test material under analysis, the pixelization filter provides the best performance in terms of balance between privacy protection and intelligibility.

In this technical demonstration, we show the application of our research on body soft biometrics. Exploiting a 3D video sensor we are able to extract semantic information that describes subjects standing in front of a camera. Semantic analysis and tracking is performed, a series of anthropometric measures are extracted and used to compute subjects' height, weight, and gender information. Possible applications of such research fall in the medical domain for monitoring elderly people; in the gaming industry for automatic avatar creation, or in smart billboards which collects demographics of the public interested by the commercial. Our algorithm allows the estimation of all these parameters in real time without requiring the computational complexity of a 3D model fitting approach.

Biometric systems have gained the attention of both the research community and the industry becoming an important topic in real application scenarios. Face recognition is, with fingerprint, among the most used techniques since it is natural for humans to recognize people from facial appearance, since the technology is mature, and because, unlike fingerprint, it is completely unintrusive. Existing systems only focus on the appearance of the subjects considering facial expressions as an obstacle to their aim. On the other hand such systems presents several limitations when dealing with variable illumination conditions, head pose, day-to-day variations (e.g. beard, glasses, or make-up), etc. Furthermore, most of the current techniques do not exploit dynamics to detect the liveness of the tested subjects. In this paper we present a study on person recognition from the dynamics of the facial feature points. The aim of this work is to demonstrate that dynamics of facial expressions could be seen as a biometric characteristic. Therefore, only dynamic characteristics are considered and the adopted features are purged of all appearance information. The results clearly show that relevant biometric information can be extracted from facial expressions and other dynamics of the face.