Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network

The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods.

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.

Connecting Smart Homes to Healthcare Services for People with Neurodegenerative disorders.

The objective of this research is to provide an approach for the design of what we have called ‘connected homes’ with a study case for elderly people with dementia living alone. These homes would be connected to a center of surveillance for direct and automatic view of multiple status of the day such as patient security and general health indicators (body temperature, heart rate, blood pressure etc.), detect the intake of meals, eating motivation, humor detection prevention of falls, Alcohol consumption detection, safe use of medicines and emergency situations and other Human Activity Recognition (HAR). The model may also predict situations by using past data accumulation. The model could even send alerts in case of emergency. This service would mean that there would minimum intervention from caregivers thanks to the Artificial Intelligence. As a case study, we proposed a new approach for the conception of connected homes for people with dementia to a central office for automatic human activity detection and help and support accordingly. Such conception includes home design concepts according to standard recommendations and the implementation of new added assistive technology tools to permit the automatic surveillance without violating the ethic requirements. Two installation models will be proposed to consider the financial situation of the patient: a unit or appliance at the patient’s home or a home that is connected to a central office.