Browsing by Author "Albuquerque, Daniel"
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- Dynamic digital signal processing algorithm for vital signs extraction in continuous-wave radarsPublication . Gouveia, Carolina; Albuquerque, Daniel; Vieira, José; Pinho, PedroRadar systems have been widely explored as a monitoring tool able to assess the subject's vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95%, irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment.
- Limits of WPT through the human body using Radio FrequencyPublication . Duarte, Rodrigo; Gouveia, Carolina; Pinho, Pedro; Albuquerque, DanielRecently, the medical community has been devel oping new technologies to enhance medical treatments and diagnosis means, having in mind the patients’ comfort and safety. Implantable medical devices are an example of such solutions. Nonetheless, these devices present some disadvantages, namely need of batteries. Hence, these implants have a limited lifetime, and require periodical surgical interventions to change or to recharge. In order to solve this problem, systems based on Radio Frequency (RF) has been developed to transfer energy inside the organism. However, transmitting power to inside the human body must be performed carefully, since high power levels might be prejudicial to the subject. In this context, the goal of this work is to study the performance of the Wireless Power Transfer (WPT) to inside the human body, while respecting the Specific Absorption Rate (SAR) limits. Therefore, the levels of absorbed power in different body parts were verified by simulation, in order to reach conclusions about the user’s safety. More specifically, two biological models that represent the thigh and the arm were considered. The simulation results led us to conclude that it is possible to transmit approximately 140 mW on the limbs location, while respecting the SAR limits. In turn, it is possible to receive a power superior to 93 µW inside the human body. Additionally, real tests were also carried out in three subjects to verify the power attenuation related to each body structure.
- Study of physiological and structural variability in the acquisition of vital signs with Bio RadarPublication . Soares, Beatriz Lino; Pinho, Pedro; Albuquerque, Daniel; Gouveia, Carolina Teixeira de SousaThe monitoring of vital signals is usually carried out by sensors and electrodes. However, it may not be viable or the best solution for people with burn tissues or with more delicate skin, not to mention cases with infectious diseases, where contact should be kept to a minimum. Thus, vital signs monitoring using radar (Bio-Radar) has become a hot topic of research and development. Several studies state that there is variability in vital signs between people. However, in the Bio-Radar area, these issues have not been addressed. In this regard, this dissertation intends to verify if it is possible to evaluate the gender, age, Body Mass Index (BMI), and Chest Wall Perimeter (CWP) through the use of radar signals, namely Bio-Radar, used to the vital signs acquisition. In order to achieve this goal, the vital signs of 92 people (46 females and 46 males), aged between 18 and 50 years old were acquired. With this dataset, it was possible to develop a statistical study of relevant characteristics extracted from the signals. Later, three Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), KNearest Neighbor (KNN) and Random Forest, were trained to identify gender, age, BMI and CWP. Finally, the relation between the respiratory amplitude and the respiratory rhythm is analyzed.