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Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time (T

The most common form of blood pressure (BP) monitoring outside of clinical settings is an oscillometric cuff; however, such cuffs tend to be cumbersome and inconvenient. Less obtrusive systems requiring minimal effort from the user would allow for cardiovascular monitoring seamlessly in daily life and relieve much of the burden of regular blood pressure monitoring. In this work, we leverage the concept of pulse transit time (PTT) to track BP using a weighing scale-based system on which a subject simply stands quietly to obtain a measurement. To extract the proximal and distal timing references for PTT, a force plate measured the initial mechanical motion of the body in response to cardiac ejection while an optical sensor array mounted directly on the force place measured the pulse wave arrival at the sole. To evaluate the method, we recruited 16 young and healthy subjects. Seated and standing measurements first verified the effectiveness of the sensor array to extract a reliable distal timing reference. Then, subjects underwent various physiological perturbations to establish a correlation curve between measured BP and a best-case estimation of BP using PTT. By using the BCG and array of PPG sensors, PTT-estimated BP tracked changes well in diastolic ( ${r}={0.80} \pm {0.12}$ ) and systolic ( ${r}={0.77} \pm {0.23}$ ) pressures. This best-case estimation resulted in an error of 2.6 ± 1.2 mmHg for diastolic and 5.6 ± 2.7 mmHg for systolic pressures. Our work allows the user to passively monitor BP during common weighing scale measurement, potentially improving the ease of longitudinal at-home care.

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by $q$ -values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

BACKGROUND Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities. OBJECTIVE In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)–based device for monitoring BP in a diverse population. METHODS We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP. RESULTS Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness. CONCLUSIONS Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity.

The ballistocardiogram (BCG), a measurement of cardiogenic whole body movements, is a technique that enables non-invasive cardiovascular monitoring. A main challenge of the BCG signal is that its morphology and amplitude are sensitive to the posture of the subject during the recording period. This work elucidates effects of posture on bed-based BCG recordings by (1) creating templates for standing BCG signals obtained from subjects in a prior study, and (2) quantifying the distance between these templates and BCG waveforms obtained in different body postures on the bed for a new set of subjects. The signal quality index (SQI), defined in previous work and corresponding to the inverse of the distance to the templates, was the highest for the supine posture and the lowest for the lateral postures. A previously-reported system identification approach to correct for distortions in the lateral, prone, and seated postures was further validated. The system identification algorithm significantly improved the signal quality and correlation to the reference morphology - the supine and standing BCG. This work has implications for robust signal processing that allows for accurate physiological interpretation of the BCG obtained in a variety of postures from a subject in bed.

Human-computer interaction (HCI) technology, and the automatic classification of a person’s mental state, are of interest to multiple industries. In this work, the fusion of sensing modalities that monitor the oxygenation of the human prefrontal cortex (PFC) and cardiovascular physiology was evaluated to differentiate between rest, mental arithmetic and N-back memory tasks. A flexible headband to measure near-infrared spectroscopy (NIRS) for quantifying PFC oxygenation, and forehead photoplethysmography (PPG) for assessing peripheral cardiovascular activity was designed. Physiological signals such as the electrocardiogram (ECG) and seismocardiogram (SCG) were collected, along with the measurements obtained using the headband. The setup was tested and validated with a total of 16 human subjects performing a series of arithmetic and N-back memory tasks. Features extracted were related to cardiac and peripheral sympathetic activity, vasomotor tone, pulse wave propagation, and oxygenation. Machine learning techniques were utilized to classify rest, arithmetic, and N-back tasks, using leave-one-subject-out cross validation. Macro-averaged accuracy of 85%, precision of 84%, recall rate of 83%, and F1 score of 80% were obtained from the classification of the three states. Statistical analyses on the subject-based results demonstrate that the fusion of NIRS and peripheral cardiovascular sensing significantly improves the accuracy, precision, recall, and F1 scores, compared to using NIRS sensing alone. Moreover, the fusion significantly improves the precision compared to peripheral cardiovascular sensing alone. The results of this work can be used in the future to design a multi-modal wearable sensing system for classifying mental state for applications such as acute stress detection.

Objective Exacerbated autonomic responses to acute stress are prevalent in posttraumatic stress disorder (PTSD). The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to acute stress in patients with PTSD. The authors hypothesized tcVNS would reduce the sympathetic response to stress compared to a sham device. Methods Using a randomized double-blind approach, we studied the effects of tcVNS on physiological responses to stress in patients with PTSD (n = 25) using noninvasive sensing modalities. Participants received either sham (n = 12) or active tcVNS (n = 13) after exposure to acute personalized traumatic script stress and mental stress (public speech, mental arithmetic) over a three-day protocol. Physiological parameters related to sympathetic responses to stress were investigated. Results Relative to sham, tcVNS paired to traumatic script stress decreased sympathetic function as measured by: decreased heart rate (adjusted β = −5.7%; 95% CI: ±3.6%, effect size d = 0.43, p, Highlights • We studied the effects of tcVNS on physiological responses to stress in patients with posttraumatic stress disorder (PTSD). • TcVNS modulates physiologic reactivity to acute traumatic and mental stress. • Peripheral autonomic measures could serve as real-time measures to evaluate the therapy response in longitudinal settings.

ObjectiveExacerbated autonomic responses to acute stress are prevalent in posttraumatic stress disorder (PTSD). The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to acute stress in patients with PTSD. The authors hypothesized tcVNS would reduce the sympathetic response to stress compared to a sham device.MethodsUsing a randomized double-blind approach, we studied the effects of tcVNS on physiological responses to stress in patients with PTSD (n=25) using noninvasive sensing modalities. Participants received either sham or active tcVNS after exposure to acute personalized traumatic script stress and mental stress (public speech, mental arithmetic) over a three-day protocol. Physiological parameters related to sympathetic responses to stress were investigated.ResultsRelative to sham, tcVNS paired to traumatic script stress decreased sympathetic function as measured by: decreased heart rate (adjusted ß=-5.7%; 95% CI: ±3.6%, effect size d=0.43, pConclusiontcVNS attenuates sympathetic arousal associated with stress related to traumatic memories as well as mental stress in patients with PTSD, with effects persisting throughout multiple traumatic stress and stimulation testing days. These findings show that tcVNS has beneficial effects on the underlying neurophysiology of PTSD. Such autonomic metrics may also be evaluated in daily life settings in tandem with tcVNS therapy to provide closed-loop delivery and measure efficacy.ClinicalTrials.gov Registration # NCT02992899HighlightsWe studied the effects of tcVNS on physiological responses to stress in patients posttraumatic stress disorder (PTSD).tcVNS modulates physiologic reactivity to traumatic and mental stress in PTSD, and modulates autonomic tone when applied without acute stress.Repeated tcVNS enhances resilience in the face of repeated stress in PTSD as quantified by peripheral autonomic measures which potentially could serve as real-time measures to evaluate the therapy response in longitudinal settings.

Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to: 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.

Background: Stress is associated with activation of the sympathetic nervous system, and can lead to lasting alterations in autonomic function and in extreme cases symptoms of posttraumatic stress disorder (PTSD). Vagal nerve stimulation (VNS) is a potentially useful tool as a modulator of autonomic nervous system function, however currently available implantable devices are limited by cost and inconvenience. Objective: The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to stress. Methods: Using a double-blind approach, we investigated the effects of active or sham tcVNS on peripheral cardiovascular and autonomic responses to stress using wearable sensing devices in 24 healthy human participants with a history of exposure to psychological trauma. Participants were exposed to acute stressors over a three-day period, including personalized scripts of traumatic events, public speech, and mental arithmetic tasks. Results: tcVNS relative to sham applied immediately after traumatic stress resulted in a decrease in sympathetic function and modulated parasympathetic/sympathetic autonomic tone as measured by increased pre-ejection period (PEP) of the heart (a marker of cardiac sympathetic function) of 4.2 ms (95% CI 1.6–6.8 ms, p

A new actuator structure applicable for the near field recording (NFR) system is studied. In the structure, the optical flying head (OFH) is coupled to a conventional four-wire type actuator generally found in CD and DVD. The major purposes of the design of the actuator structure are the application of active focusing, the elimination of ramp-load mechanism and the tracking performance enhancement in the NFR system. The OFH with a magneto-optical (MO) coil to provide the necessary magnetic field for MO recording, an air bearing slider having a transparent optical section to provide optical path through the slider, and a solid immersion lens (SIL) for the use of near field is integrated with the bobbin and the voice coil assembly of the four-wire type voice coil motor (VCM) actuator through suspension. With a view to the realization of the suggested actuator, the flying characteristics of the designed OFH are evaluated by computer simulation. In addition, the flying stability of the OFH during the focusing and tracking motion of the actuator is experimentally evaluated.

This paper deals with a new tracking control method for piezoelectric actuators. When actuating in an open-loop manner, in order to compensate for the creep effect of the piezoelectric transducer as well as hysteresis, a new concept of `voltage creep' is proposed, which is represented by the two parameters V0 and γv. It is shown that these two parameters have the properties of wiping out and congruency, which make it possible to derive parameter values appropriate to control piezoelectric actuators using the Preisach model. Finally, a tracking control experiment of piezoelectric actuators for an arbitrary desired trajectory is performed giving greatly improved results compared to other open-loop actuating methods.

A major limitation of piezoelectric translator (PZT) actuators is their lack of accuracy originated from the hysteresis and creep. Nevertheless the creep phenomenon is an important factor in many applications of PZT actuators, but it has been investigated less frequently in comparison with the displacement hysteresis. In this article, we present a basic creep model with some parameters that have hysteresis properties which make it possible to predict an open loop response of PZT actuators based on these properties.

This paper presents the new MO coil design scheme and the new measuring method for the magnetic field intensity of the MO coil

In equi-force mode among the AFM scanning modes, the scanning data of a sample is acquired by Z-axis PZT input voltages. But, PZT actuator has a hysteresis and creep between the input voltages and the output displacements. These nonlinearities make a distortion in scanning images. We propose that the creep effect of stack-type PZT shows hysteresis property and classical preisach model can be applied to AFM scanning data according to this property. Finally we will prove that AFM image is improved by the classical preisach model application.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In a small-form-factor (SFF) optical disk drive (ODD), the optical flying head (OFH) on a thin-cover-layered plastic disk requires not only uniformity of the cover layer thickness of the disk media, but also the flying stability of OFH on the plastic disk surface with low flying height variation, as well as precise tolerance management in the OFH assembly. Therefore, in order to develop a practical optical pickup head interfaced to the plastic disk, we designed the OFH with a parallel-plate electrostatic actuator for the vertical positioning of the objective lens for better flying characteristics. The flying height of the OFH is designed to be 200 nm to increase air-bearing stiffness. On the other hand, a UV resin cover layer is adopted as a protective layer and a hard and stiff plastic substrate is also adopted to increase the durability of the disk. In the evaluation of the stability of the OFH and the durability of the disk, we introduce computational and experimental results on the flying height variation of the OFH, additionally we investigated the characteristics of the durability of various plastic disks.