Dynamic Electromagnetic Model to Detect Human Vital Signs Based on Time-Domain Finite Integration Theorem

Detecting motor bearing faults by stator currents is of great importance as it improves the adaptability of measurement means to different environments and reduces the number of sensors. Therefore, many studies have been conducted to investigate bearing faults by constructing motor models, most of which have used signal models to simulate the dynamics of the bearings. However, the signal model may be exposed to the issue that the nonlinearities in the bearing operation are neglected, thus oversimplifying the coupling effects between the electromagnetic and dynamics models. Hence, a coupled electromagnetic–dynamic modeling method for induction motors based on multiple coupled circuit theory and the rotor-bearing dynamics model is proposed in this study to implement the coupled simulation of electromagnetic and dynamic models. The air gap length and rotor velocity are used as coupled parameters for the calculation of stator–rotor mutual inductance and ball contact deformation, respectively. The simulation results show that the proposed model can effectively implement the electromagnetic–dynamic coupled and reflect the bearing fault characteristics in the current signal. Experiments were conducted on induction motors with typical winding configurations under laboratory conditions. The comparison results verify the effectiveness of the proposed modeling method.

While research supporting the psychological benefits of equine assisted psychotherapy and learning (EAPL) is expanding, little is known about the physiological impact this therapy has on the human and horse. The physical activity for younger adults may hold promise where other therapies have failed, but can this form of exercise therapy be physically demanding on the participant? Therefore, the objective of this study was to measure physiological responses of horse and human for those participating in an EAPL outpatient collegiate recovery program for substance use disorder (SUD). This pilot study assessed humans (n = 4) and horses (n = 5) participating in a collegiate recovery community EAPL SUD program. The six-week program included riding and ground activities for 1 hr/wk. Vital signs (heart and respiratory rates and pain rating) were recorded weekly at the beginning and end of each session. Human pain rating used the Wong-Baker Faces Pain Rating Scale and equines used the Equine Utrecht University Scale for Facial Assessment of Pain (EQUUS-FAP). T-tests were performed comparing measurements at the beginning and end of each session (P = 0.05). Pearson Correlations were used to determine relationships between human and horse vital signs. While no changes were seen in human heart rate, a significant decrease in respiratory rates and pain scores was found by the end of the session (Table 1). Horse heart and respiratory rates and EQUUS-FAP scores increased. A moderate correlation was determined for human and horse respiration rates (r=0.65, P = 0.00). Correlations became weaker when evaluating the other vital signs (Heart Rates: r=0.54, P = 0.00; Pain Rating Scores: r=0.27, P = 0.17). While relationships between horse and human vital signs were limited, it is important to note the differences in how the two physiologically respond to EAPL suggesting further research beyond this pilot study may be needed to investigate the physical demand of EAPL on the horse.

Dynamic modeling method for an electro-hydraulic proportional valve coupled mechanical–electrical-electromagnetic-fluid subsystems

Over the years, several studies have been done on the benefits of jogging. Other than physical fitness, individuals who stay active in this sport get various forms of benefits. In fact, several studies have proven that jogging among men and women increase life expectancy. However, irregularities and varying frequencies in engaging in this form of physical activity may increase health risks. The study introduces a mathematical framework in assessing health risks associated in a strenuous physical activity such as jogging by testing for chaos in human vital signs. Dynamic physiological states of a jogger are interpreted whether chaotic or non-chaotic using Largest Lyapunov Exponent (LLE) test for chaos. Likewise, demographic variables such as age and gender are correlated to the occurrence of chaotic patterns using the Chi-Square Test for Independence which are used as a guide in evaluation of health risks derived from the frequency of jogging. This framework aims to provide guidelines in the reduction of risks associated to irregularities in jogging patterns. Based on results, whether a jogger is an early rise or a late rise, their vital signs indicate chaotic behavior. Whether a jogger is a female or male and of different age range, chaotic properties of their vital signs are confirmed through jogging patterns. Though a chaotic pattern is consistently exhibited throughout the jogging condition, the degree of chaos varies as reflected in changing values of LLE over the time interval.Over the years, several studies have been done on the benefits of jogging. Other than physical fitness, individuals who stay active in this sport get various forms of benefits. In fact, several studies have proven that jogging among men and women increase life expectancy. However, irregularities and varying frequencies in engaging in this form of physical activity may increase health risks. The study introduces a mathematical framework in assessing health risks associated in a strenuous physical activity such as jogging by testing for chaos in human vital signs. Dynamic physiological states of a jogger are interpreted whether chaotic or non-chaotic using Largest Lyapunov Exponent (LLE) test for chaos. Likewise, demographic variables such as age and gender are correlated to the occurrence of chaotic patterns using the Chi-Square Test for Independence which are used as a guide in evaluation of health risks derived from the frequency of jogging. This framework aims to provide guidelines in the reduction of r...

For the problem of relatively severe lateral vibration found in the vertical electrodynamic shaker experiment, an electromechanical coupling dynamic model of the electrodynamic shaker considering low-frequency lateral vibration is proposed. The reason and mechanism of the lateral vibration is explained and analyzed through this model. To establish this model, an electromagnetic force model of overall conditions is firstly built by fitting force samples with neural network method. The force samples are obtained by orthogonal test of finite element simulation, in which five factors of the moving coil including current, vertical position, flipping eccentricity angle, radial translational eccentric direction and distance are considered. Secondly, a 7-dof dynamic model of the electrodynamic shaker is developed with the consideration of the lateral vibration of the moving system. To obtain the transfer function accurately, the stiffness and damping parameters are identified. Finally, an electromechanical dynamic model is established by coupling the force model and the 7-dof dynamic model, and it is verified by experiments. The coupling model proposed can be further used for the control and optimization of the electrodynamic shaker.

Single layer of dielectric spheres is a recognized model for the basic understanding of some aspects of photonic crystals. Here we present a systematic study of the effect of compacting in the electromagnetic transmission of dielectric spheres monolayers. Experiments were performed in the microwave domain (from 10 GHz to 30 GHz) with glass spheres of high dielectric permittivity e = 7. Time Domain Finite Integration (TDFI) calculations were also accomplished. Experimental data and TDFI calculations agreement provides a double check on the lack of experimental artefacts and the correctness of simulation settings. Following the evolution of the lower frequency spectral peak with layer compacting ratio, we established three different electromagnetic regimes. For the higher and lower compacting ratio regimes, the peak frequency matches isolated sphere pure resonances, while for intermediate values of compacting, some transition between these two modes takes place. Extending the study to the complete frequency range, we find that sphere single layers transmission spectra become closer to isolated sphere scattering calculations as the compacting ratio is decreased. However as the agreement remains imperfect even for our lowest compacting measurable layer, we conclude that some structure contribution cannot be neglected even for low compact layers.

This paper presents a novel Optical Body Area Network (OBAN) for efficient transmission of multiple patients human vital signs. The primary human vital signs are body temperature, pulse rate, respiratory rate and blood pressure. These vital signs are captured from four different patients and are transmitted using Visible Light Communication (VLC). The multi-patient OBAN utilizes orthogonal codes to transmit each patient data via VLC. We employ On-Off Keying (OOK) modulation technique for transmitting the coded multi-patient human vital signs. Computer simulations were carried out and the results demonstrate that it achieves highly reliable transmissions exhibiting a Bit Error Rate (BER) better than 10−6 at a Signal to Noise Ratio (SNR) value of 12 dB.

Conventional prognostic factors are typically assessed at diagnosis in metastatic hormone-sensitive prostate cancer (mHSPC). However, variations in vital signs and laboratory parameters occur during systemic treatment and may predict patients' prognosis and anticipate organ-specific toxicity development. This single-center retrospective study included 363 patients with de novo mHSPC treated between 2014 and 2023. Clinical and laboratory data were systematically collected from the hospital data warehouse, from treatment initiation through the following seven months. Variations in vital parameters and blood test results were graded using CTCAE V5.0 (dynamic variables). Cox regression analyses were performed to explore the impact of dynamic variables on progression-free survival (PFS) and overall survival (OS). Machine learning (ML) models (Support Vector Classifier, Random Forest, and LGBM Classifier) were developed to predict single organ-specific toxicities and to identify good and poor responders based on 7-month PSA levels, PFS and OS. We compared ML model performance when trained only on baseline factors (static models) with those integrating variables generated by vital sign and blood test monitoring within 3 and 7 months from treatment start (dynamic models). Dynamic model failed to improve the prediction of single organ-specific toxicities. Univariable Cox analysis revealed that the development of hematological, liver, and kidney-related toxicity, as well as the development of electrolyte disturbances within 3 or 7 months, was associated with shorter PFS (p = 0.011, 0.007, 0.174, and 0.02, respectively) and/or OS (p = 0.001, 0.099, 0.012, and 0.001, respectively). In multivariable Cox analysis, increasing alkaline phosphatase levels (HR = 1.93, p = 0.009), decreasing albumin (HR = 1.92, p = 0.008) and development of hyponatremia (HR = 1.79, p = 0.033) were associated with a shorter OS. The combination of static and dynamic variables significantly improved the ability of ML models to identify poor responders (shorter PFS: AUC range 0.91-0.94 vs. 0.79-0.89). The integration of conventional prognostic factors with the detection of significant changes in vital signs and blood tests occurring early during systemic treatment in patients with de novo mHSPC may enhance patient stratification and improve prediction of survival outcomes. Multicenter validation studies are needed to confirm these results.

Ultra-wideband (UWB) radar plays an important role in search and rescue at disaster relief sites. Identifying vital signs and locating buried survivors are two important research contents in this field. In general, it is hard to identify a human's vital signs (breathing and heartbeat) in complex environments due to the low signal-to-noise ratio of the vital sign in radar signals. In this paper, advanced signal-processing approaches are used to identify and to extract human vital signs in complex environments. First, we apply Curvelet transform to remove the source-receiver direct coupling wave and background clutters. Next, singular value decomposition is used to de-noise in the life signals. Finally, the results are presented based on FFT and Hilbert-Huang transform to separate and to extract human vital sign frequencies, as well as the micro-Doppler shift characteristics. The proposed processing approach is first tested by a set of synthetic data generated by FDTD simulation for UWB radar detection of two trapped victims under debris at an earthquake site of collapsed buildings. Then, it is validated by laboratory experiments data. The results demonstrate that the combination of UWB radar as the hardware and advanced signal-processing algorithms as the software has potential for efficient vital sign detection and location in search and rescue for trapped victims in complex environment.

We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope=0.84 and intercept=-0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

Hybrid simulation of power systems with SVC dynamic phasor model

The electromagnetic wave propagation in passenger cars has been studied by means of simplified models for cabin and interior as well as for passengers. The simulations have been performed using the "Finite Integration Time Domain" (FITD) method. The results serve for the determination of suitable base station antenna positions for wireless incar communication systems avoiding the need for time consuming end expensive measurement campaigns.

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

Previous studies have demonstrated that the radar technique is one of the most effective ways to detect human vital signs in certain circumstances. However, the problem of the complex electromagnetic environment at an earthquake disaster site has not been fully explored in the literature to date. To characterize and comprehend the electromagnetic environment in radio frequencies in a typical field site for the task of search and rescue by using radar technology to search for human vital signs originated from buried living victims we conducted a field test to collect data by using the 400-MHz ground penetrating radar on a one-to-one scale collapsed building model at China National Training Center for Earthquake Search & Rescue (CNTCESR) operated by China Earthquake Administration (CEA) in Beijing. The physical model is a collapsed 3-storey reinforced concrete building. Both constant-offset and multi-offset reflection profiles are collected on the relatively intact roof of the building with a size of 17 × 12 m. The constant offset (monostatic) reflection profiles are collected at 10-cm spacing in-line and 20-cm spacing off-line to cover a 15 × 10 m area. Multi-offset profiles are collected at a few given locations. The time window for all radar profiles is 100 nanoseconds. We used the digital point-cloud technique to construct the 3D digital model of the collapsed building. This 3D model is the basis for conducting radar wave propagation forward modeling and inversion imaging. The awareness and comprehension of the ambient electromagnetic condition will be incorporated in the algorithm for radar detection of human vital signs for earthquake victim search and rescue in real world field practice.

For numerical dosimetric simulations of complex electromagnetic human exposure situations the method of moments (MoM) is coupled with a finite integration time domain (FITD) scheme: The MoM simulation features a simple model of the body under test and computes the field distribution on a closed surface around the body. This field information is used to define equivalent sources on the surface for a full three-dimensional FITD simulation featuring a high-resolution body under test. First numerical results are presented for this novel approach.