Experimental validation of a quasi-realtime human respiration detection method via UWB radar

In this paper, we propose a quasi-realtime human respiration detection method via UWB radar system in through-wall or similar condition. With respect to the previous proposed automatic detection method, the new proposed method assures competitive performance in the human respiration motion detection and effective noise/clutter rejection, which have been proved by experimental results in actual scenario. This new method has also been implemented in a UWB through-wall life-detection radar prototype, and its time consuming is about 2 s, which can satisfy the practical requirement of quasi-realtime for through-wall sequential vital sign detection. Therefore, it can be an alternative for through-obstacles static human detection in antiterrorism or rescue scenarios

An FPGA-Integrated Time-to-Digital Converter Based on a Ring Oscillator for Programmable Delay Line Resolution Measurement

We describe the architecture of a time-to-digital converter (TDC), specially intended to measure the delay resolution of a programmable delay line (PDL). The configuration, which consists of a ring oscillator, a frequency divider (FD), and a period measurement circuit (PMC), is implemented in a field programmable gate array (FPGA) device. The ring oscillator realized in loop containing a PDL and a look-up table (LUT) generates periodic oscillatory pulses. The FD amplifies the oscillatory period from nanosecond range to microsecond range. The time-to-digital conversion is based on counting the number of clock cycles between two consecutive pulses of the FD by the PMC. Experiments have been conducted to verify the performance of the TDC. The achieved relative errors for four PDLs are within 0.50%-1.21% and the TDC has an equivalent resolution of about 0.4 ps

Human Respiration Localization Method Using UWB Linear Antenna Array

Human respiration is the basic vital sign in remote monitoring. There has been remarkable progress in this area, but some challenges still remain to obtain the angle-of-arrival (AOA) and distinguish the individual signals. This paper presents a 2D noncontact human respiration localization method using Ultra-Wideband (UWB) 1D linear antenna array. The imaging reconstruction based on beamforming is used to estimate the AOA of the human chest. The distance-slow time 2D matrix at the estimated AOA is processed to obtain the distance and respiration frequency of the vital sign. The proposed method can be used to isolate signals from individual targets when more than one human object is located in the surveillance space. The feasibility of the proposed method is demonstrated via the simulation and experiment results

Evaluating large language models in pediatric fever management: a two-layer study

BackgroundPediatric fever is a prevalent concern, often causing parental anxiety and frequent medical consultations. While large language models (LLMs) such as ChatGPT, Perplexity, and YouChat show promise in enhancing medical communication and education, their efficacy in addressing complex pediatric fever-related questions remains underexplored, particularly from the perspectives of medical professionals and patients’ relatives.ObjectiveThis study aimed to explore the differences and similarities among four common large language models (ChatGPT3.5, ChatGPT4.0, YouChat, and Perplexity) in answering thirty pediatric fever-related questions and to examine how doctors and pediatric patients’ relatives evaluate the LLM-generated answers based on predefined criteria.MethodsThe study selected thirty fever-related pediatric questions answered by the four models. Twenty doctors rated these responses across four dimensions. To conduct the survey among pediatric patients’ relatives, we eliminated certain responses that we deemed to pose safety risks or be misleading. Based on the doctors’ questionnaire, the thirty questions were divided into six groups, each evaluated by twenty pediatric relatives. The Tukey post-hoc test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results.ResultsIn the doctors’ questionnaire, ChatGPT3.5 and ChatGPT4.0 outperformed YouChat and Perplexity in all dimensions, with no significant difference between ChatGPT3.5 and ChatGPT4.0 or between YouChat and Perplexity. All models scored significantly better in accuracy than other dimensions. In the pediatric relatives’ questionnaire, no significant differences were found among the models, with revisits revealing some reasons for these results.ConclusionsInternet searches (YouChat and Perplexity) did not improve the ability of large language models to answer medical questions as expected. Patients lacked the ability to understand and analyze model responses due to a lack of professional knowledge and a lack of central points in model answers. When developing large language models for patient use, it's important to highlight the central points of the answers and ensure they are easily understandable

Association of obesity with osteoporotic fracture risk in individuals with bone metabolism-related conditions: a cross sectional analysis

IntroductionThis study aimed to investigate the individual and composite associations of different indices of obesity on osteoporotic fractures at three different sites among individuals affected by conditions influencing bone metabolism.MethodsParticipants were included from the National Health and Nutrition Examination Survey (NHANES), a national cross-sectional survey. BMI and WC were used separately and in combination to evaluate the presence of obesity. Obesity was defined as BMI ≥ 30 kg/m2, WC ≥ 88 cm in females, and WC ≥ 102 cm in males. Associations between obesity and osteoporotic fractures were assessed using multivariable logistic regression and OR curves. Associations modified by age, sex, race, and alcohol consumption were also evaluated.ResultsA total of 5377 participants were included in this study. In multivariable logistic regression analyses, we found that BMI, WC, BMI defining obesity, and WC defining obesity were negatively associated with hip fracture (all p < 0.05). However, harmful associations between WC and BMI defining obesity and spine fracture were found (all p < 0.05). OR curves revealed that BMI and WC had a linear relationship with hip and spine fractures (all P for non-linearity >0.05). Further analyses showed that the highest WC quartile was harmfully associated with a higher risk of spine fractures (p < 0.05). Obese participants diagnosed by both BMI and WC were less likely to have hip fractures but more likely to have spine fractures (all P for trend <0.05). A significant interaction between age (Ref: age < 50 years) and BMI and WC was detected for hip fractures (all P for interaction <0.05).DiscussionIn people with conditions influencing bone metabolism, obesity diagnosed by BMI and WC was associated with a lower risk of hip fracture, while obesity diagnosed by BMI and the highest WC quartile were associated with a higher risk of spine fracture

Exploring the prevalence and chest CT predictors of Long COVID in children: a comprehensive study from Shanghai and Linyi

IntroductionCOVID-19 constitutes a pandemic of significant detriment to human health. This study aimed to investigate the prevalence of Long COVID following SARS-CoV-2 infection, analyze the potential predictors of chest CT for the development of Long COVID in children.MethodsA cohort of children who visited the respiratory outpatient clinics at Shanghai Children's Medical Center or Linyi Maternal and Child Health Care Hospital from December 2022 to February 2023 and underwent chest CT scans within 1 week was followed up. Data on clinical characteristics, Long COVID symptoms, and chest CT manifestations were collected and analyzed. Multivariate logistic regression models and decision tree models were employed to identify factors associated with Long COVID.ResultsA total of 416 children were included in the study. Among 277 children who completed the follow-up, the prevalence of Long COVID was 23.1%. Chronic cough, fatigue, brain fog, and post-exertional malaise were the most commonly reported symptoms. In the decision tree model for Long COVID, the presence of increased vascular markings, the absence of normal CT findings, and younger age were identified as predictors associated with a higher likelihood of developing Long COVID in children. However, no significant correlation was found between chest CT abnormality and the occurrence of Long COVID.DiscussionLong COVID in children presents a complex challenge with a significant prevalence rate of 23.1%. Chest CT scans of children post-SARS-CoV-2 infection, identified as abnormal with increased vascular markings, indicate a higher risk of developing Long COVID