Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Detection and analysis of heartbeats in seismocardiogram signals

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space)

Microstrip antenna design with improved fabrication tolerance for remote vital signs monitoring and WLAN/WPAN applications at mm-wave and THz frequencies

A novel approach is introduced to design microstrip patch antennas (MPAs) with improved fabrication tolerance for highly demanded Millimetre-wave (mm-wave) (30-300GHz) and Terahertz (THz) (0.3-3THz) frequency applications. The presented MP A designing method overcomes the challenges which exist with the fabrication and implementation of the conventional MP A designs at mm-wave and THz frequencies. The following research contributions have been added to the state-ofthe- art work: (i) designing of improved size MPAs at 60GHz, 1 OOGHz, 635GHz and 835GHz to prove the designing concept, (ii) detail measurements and analysis of Remote Vital Signs Monitoring (RVSM) with various sizes of the proposed MPA arrays at 60GHz for high detection accuracy and sensitivity, (iii) designing and tes~ing of MP As for 60GHz wireless local and personal area networks (WLAN/WP AN) in point-to-pint, point-to-multipoint and dual-band applications, (iv) implementation and testing of particular Partially Reflective Surface, Dielectric Lens and Defected Ground Structures on the proposed MP A designs with novel configurations at 60GHz for bandwidth and gain enhancement, and (v~ a comprehensive experimental study on the performance of large array designs with the proposed MP A elements for mm-wave applications. The mentioned research work is explained in the coming chapters in details. Moreover, all mentioned work has already been published