Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation

Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Learning algorithms. In this work, we introduce the algorithm Radar Activity Classification with Perceptual Image Transformation (RACPIT), which increases the accuracy of human activity classification while lowering the dependency on limited source data. In doing so, we focus on the augmentation of the dataset by synthetic data. We use a human radar reflection model based on the captured motion of the test subjects performing activities in the source dataset, which we recorded with a video camera. As the synthetic data generated by this model still deviates too much from the original radar data, we implement an image transformation network to bring real data close to their synthetic counterpart. We leverage these artificially generated data to train a Convolutional Neural Network for activity classification. We found that by using our approach, the classification accuracy could be increased by up to 20%, without the need of collecting more real data

Human and Artificial Intelligence

Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone. Hence, the integration between Human Intelligence and Artificial Intelligence is needed. However, several challenges make this integration complex. The aim of this Special Issue was to provide a large and varied collection of high-level contributions presenting novel approaches and solutions to address the above issues. This Special Issue contains 14 papers (13 research papers and 1 review paper) that deal with various topics related to human–machine interactions and cooperation. Most of these works concern different aspects of recommender systems, which are among the most widespread decision support systems. The domains covered range from healthcare to movies and from biometrics to cultural heritage. However, there are also contributions on vocal assistants and smart interactive technologies. In summary, each paper included in this Special Issue represents a step towards a future with human–machine interactions and cooperation. We hope the readers enjoy reading these articles and may find inspiration for their research activities

Non-Invasive Blood Glucose Monitoring Using Electromagnetic Sensors

Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e., Glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. Besides, semi-invasive continuous monitors (CGM) are currently being used to quantify the glucose analyte in interstitial fluids (ISF) using an implantable needle-like electrochemical sensors. However, the limitations and discomforts associated with these finger-pricking and implantable point-of-care devices have established a new demand for complete non-invasive pain-free and low-cost blood glucose monitors to allow for more frequent and convenient glucose checks and thereby contribute more generously to diabetes care and prevention. Towards that goal, researchers have been developing alternative techniques that are more convenient, affordable, pain-free, and can be used for continuous non-invasive blood glucose monitoring. In this research, a variety of electromagnetic sensing techniques were developed for reliably monitoring the blood glucose levels of clinical relevance to diabetes using the non-ionizing electromagnetic radiations of no hazards when penetrating the body. The sensing structures and devices introduced in this study were designed to operate in specific frequency spectrums that promise a reliable and sensitive glucose detection from centimeter- to millimeter-wave bands. Particularly, three different technologies were proposed and investigated at the Centre for Intelligent Antenna and Radio Systems (CIARS): Complementary Split-Ring Resonators (CSRRs), Whispering Gallery Modes (WGMs) sensors, and Frequency-Modulated Continuous-Wave (FMCW) millimeter-Wave Radars. Multiple sensing devices were developed using those proposed technologies in the micro/millimeter-wave spectrums of interest. A comprehensive study was conducted for the functionality, sensitivity, and repeatability analysis of each sensing device. Particularly, the sensors were thoroughly designed, optimized, fabricated, and practically tested in the laboratory with the desired glucose sensitivity performance. Different topologies and configurations of the proposed sensors were studied and compared in sensitivity using experimental and numerical analysis tools. Besides, machine learning and signal processing tools were intelligently applied to analyze the frequency responses of the sensors and reliably identify different glucose levels. The developed glucose sensors were coupled with frequency-compatible radar boards to realize small mobile glucose sensing systems of reduced cost. The proposed sensors, beside their impressive detection capability of the diabetes-spectrum glucose concentrations, are endowed with favourable advantages of simple fabrication, low-power consumption, miniaturized compact sizing, non-ionizing radiation, and minimum health risk or impact for human beings. Such attractive features promote the proposed sensors as possible candidates for development as mobile, portable/wearable gadgets for affordable non-invasive blood glucose monitoring for diabetes. The introduced sensing structures could also be employed for other vital sensing applications such as liquid type/quantity identification, oil adulteration detection, milk quality control, and virus/bacteria detection. Another focus of this thesis is to investigate the electromagnetic behavior of the glucose in blood mimicking tissues across the microwave spectrum from 200 MHz to 67 GHz using a commercial characterization system (DAK-TL) developed by SPEAG. This is beneficial to locate the promising frequency spectrums that are most responsive to slight variations in glucose concentrations, and to identify the amount of change in the dielectric properties due to different concentrations of interest. Besides, the effect of the blood typing and medication was also investigated by measuring the dielectric properties of synthetic “artificial” as well as authentic “human” blood samples of different ABO-Rh types and with different medications. Measured results have posed for other factors that may impact the developed microwave sensors accuracy and sensitivity including the patient’s blood type, pre-existing medical conditions, or other illnesses

Exploring tangible interactions with radar sensing

Research has explored miniature radar as a promising sensing technique for the recognition of gestures, objects, users’ presence and activity. However, within Human-Computer Interaction (HCI), its use remains underexplored, in particular in Tangible User Interface (TUI). In this paper, we explore two research questions with radar as a platform for sensing tangible interaction with the counting, ordering, identification of objects and tracking the orientation, movement and distance of these objects. We detail the design space and practical use-cases for such interaction which allows us to identify a series of design patterns, beyond static interaction, which are continuous and dynamic. With a focus on planar objects, we report on a series of studies which demonstrate the suitability of this approach. This exploration is grounded in both a characterization of the radar sensing and our rigorous experiments which show that such sensing is accurate with minimal training. With these techniques, we envision both realistic and future applications and scenarios. The motivation for what we refer to as Solinteraction, is to demonstrate the potential for radar-based interaction with objects in HCI and