Direct Shear Stress Mapping Using a Gallium Nitride LED-Based Tactile Sensor.

An experiment was performed to calibrate the capability of a tactile sensor, which is based on gallium nitride (GaN) nanopillars, to measure the absolute magnitude and direction of an applied shear force without the need for any post-processing of data. The force's magnitude was deduced from monitoring the nanopillars' light emission intensity. Calibration of the tactile sensor used a commercial force/torque (F/T) sensor. Numerical simulations were carried out to translate the F/T sensor's reading to the shear force applied to each nanopillar's tip. The results confirmed the direct measurement of shear stress from 3.71 to 50 kPa, which is in the range of interest for completing robotic tasks such as grasping, pose estimation, and item discovery.

Data-driven modeling of pharmacological systems using endpoint information fusion.

This study investigated the feasibility of deriving data-driven model of a class of pharmacological systems using the information fusion of endpoint responses. For a class of pharmacological systems subsuming conventional steady-state dose-response models, compartmental pharmacokinetic-pharmacodynamic models and indirect response models, a relation between multiple endpoint responses was formalized and analyzed to elucidate if this class of systems is identifiable, i.e., if the data-driven model of this class of systems can be derived from the endpoint responses alone. It was shown that this class of systems is fully identifiable in case all the responses involve effect compartments. However, it was also observed that persistently exciting dose profiles may be required in accurately deriving reliable data-driven model with low variance. The findings from the identifiability analysis were demonstrated using benchmark pharmacological system examples.

Quantification of wave reflection using peripheral blood pressure waveforms.

This paper presents a novel minimally invasive method for quantifying blood pressure (BP) wave reflection in the arterial tree. In this method, two peripheral BP waveforms are analyzed to obtain an estimate of central aortic BP waveform, which is used together with a peripheral BP waveform to compute forward and backward pressure waves. These forward and backward waves are then used to quantify the strength of wave reflection in the arterial tree. Two unique strengths of the proposed method are that 1) it replaces highly invasive central aortic BP and flow waveforms required in many existing methods by less invasive peripheral BP waveforms, and 2) it does not require estimation of characteristic impedance. The feasibility of the proposed method was examined in an experimental swine subject under a wide range of physiologic states and in 13 cardiac surgery patients. In the swine subject, the method was comparable to the reference method based on central aortic BP and flow. In cardiac surgery patients, the method was able to estimate forward and backward pressure waves in the absence of any central aortic waveforms: on the average, the root-mean-squared error between actual versus computed forward and backward pressure waves was less than 5 mmHg, and the error between actual versus computed reflection index was less than 0.03.

Subject-specific estimation of central aortic blood pressure via system identification: preliminary in-human experimental study.

This paper demonstrates preliminary in-human validity of a novel subject-specific approach to estimation of central aortic blood pressure (CABP) from peripheral circulatory waveforms. In this "Individualized Transfer Function" (ITF) approach, CABP is estimated in two steps. First, the circulatory dynamics of the cardiovascular system are determined via model-based system identification, in which an arterial tree model is characterized based on the circulatory waveform signals measured at the body's extremity locations. Second, CABP waveform is estimated by de-convolving peripheral circulatory waveforms from the arterial tree model. The validity of the ITF approach was demonstrated using experimental data collected from 13 cardiac surgery patients. Compared with the invasive peripheral blood pressure (BP) measurements, the ITF approach yielded significant reduction in errors associated with the estimation of CABP, including 1.9-2.6 mmHg (34-42 %) reduction in BP waveform errors (p < 0.05) as well as 5.8-9.1 mmHg (67-76 %) and 6.0-9.7 mmHg (78-85 %) reductions in systolic and pulse pressure (SP and PP) errors (p < 0.05). It also showed modest but significant improvement over the generalized transfer function approach, including 0.1 mmHg (2.6 %) reduction in BP waveform errors as well as 0.7 (20 %) and 5.0 mmHg (75 %) reductions in SP and PP errors (p < 0.05).

Data-driven lossy tube-load modeling of arterial tree: in-human study.

In this paper, we present and validate a data-driven method to lossy tube-load modeling of arterial tree in humans. In the proposed method, the lossy tube-load model is fitted to central aortic and peripheral blood pressure (BP) waves in the time domain. For this purpose, we employ a time-domain lossy tube-load model in which the wave propagation constant is formulated to two terms: one responsible for the alteration of wave amplitude and the other for the transport delay. Using the experimental BP data collected from 17 cardiac surgery patients, we showed that the time-domain lossy tube-load model is able to accurately represent the relation between central aortic versus upper-limb and lower-limb BP waves. In addition, the comparison of lossy versus lossless tube-load models revealed that (1) the former outperformed the latter in general with the root-mean-squared errors (RMSE) of 3.1 mm Hg versus 3.5 mm Hg, respectively (p-value < 0.05), and (2) the efficacy of the former over the latter was more clearly observed in case the normalized difference in the mean central aortic versus peripheral BP was large; when the difference was >5% of the underlying mean BP, lossy and lossless models showed the RMSE of 2.7 mm Hg and 3.7 mm Hg, respectively (p-value < 0.05).

Comparative study on tube-load modeling of arterial hemodynamics in humans.

In this paper, we assess the validity of two alternative tube-load models for describing the relationship between central aortic and peripheral arterial blood pressure (BP) waveforms in humans. In particular, a single-tube (1-TL) model and a serially connected two-tube (2-TL) model, both terminated with a Windkessel load, are considered as candidate representations of central aortic-peripheral arterial path. Using the central aortic, radial and femoral BP waveform data collected from eight human subjects undergoing coronary artery bypass graft with cardiopulmonary bypass procedure, the fidelity of the tube-load models was quantified and compared with each other. Both models could fit the central aortic-radial and central aortic-femoral BP waveform pairs effectively. Specifically, the models could estimate pulse travel time (PTT) accurately, and the model-derived frequency response was also close to the empirical transfer function estimate obtained directly from the central aortic and peripheral BP waveform data. However, 2-TL model was consistently superior to 1-TL model with statistical significance as far as the accuracy of the central aortic BP waveform was concerned. Indeed, the average waveform RMSE was 2.52 mmHg versus 3.24 mmHg for 2-TL and 1-TL models, respectively (p < 0.05); the r² value between measured and estimated central aortic BP waveforms was 0.96 and 0.93 for 2-TL and 1-TL models, respectively (p < 0.05). We concluded that the tube-load models considered in this paper are valid representations that can accurately reproduce central aortic-radial/femoral BP waveform relationships in humans, although the 2-TL model is preferred if an accurate central aortic BP waveform is highly desired.

Estimation of cardiac output and peripheral resistance using square-wave-approximated aortic flow signal.

This paper presents a model-based approach to estimation of cardiac output (CO) and total peripheral resistance (TPR). In the proposed approach, the response of cardiovascular system (CVS), described by the windkessel model, is tuned to the measurements of systolic, diastolic and mean arterial blood pressures (BP) so as to yield optimal individual- and time-specific system time constant that is used to estimate CO and TPR. Unique aspects of the proposed approach are that it approximates the aortic flow as a train of square waves and that it also assumes pressure-dependent arterial compliance, as opposed to the traditional windkessel model in which aortic flow is approximated as a train of impulses and constant arterial compliance is assumed. It was shown that the proposed model encompasses the standard windkessel model as a limiting case, and that it also yields more realistic BP waveform response than the standard windkessel model. The proposed approach has potential to outperform its standard counterpart by treating systolic, diastolic, and mean BP as independent features in estimating CO and TPR, rather than solely resorting to pulse pressure as in the case of the standard windkessel model. Experimental results from in-vivo data collected from a number of animal subjects supports the viability of the proposed approach in that it could achieve approximately 29% and 24% reduction in CO and TPR errors when compared with its standard counterpart.