Object-oriented modeling of thoracic fluid balance to study cardiogenic pulmonary congestion in humans.

BACKGROUND AND OBJECTIVE: We hypothesized that a biophysical computational model implemented in an object-oriented modeling language (OOML) would provide physiological information and simulative data to study the development and treatment of cardiogenic pulmonary congestion. METHODS: This work is based on the object-oriented cardiopulmonary interaction introduced in [1]. This paper describes the novel model components required to study cardiogenic pulmonary congestion: i) interstitial fluid exchange related to the Starling equation, ii) the lymphatic pump, and iii) the interconnection of these elements with the original cardiopulmonary model. The presented model succeeds in i) describing lymphatic flow at the capillary artery and venous end, ii) activation of the lymphatic pump at elevated pulmonary pressures, and iii) the simulation of the different safety factors related to lung tissue, osmotic gradient, and the lymphatic system during the development of lung congestion. RESULTS: Simulations show a qualitative correlation between model behavior and physiological data from literature. The model also demonstrates the beneficial effect of continuous positive airway pressure therapy on fluid clearance and respiratory mechanics. CONCLUSION: This study demonstrates the successful use of OOML to describe the development of cardiogenic congestion by introducing a model of the lymphatic system and the thoracic fluid balance system, as well as connecting them to the existing cardiopulmonary model.

Dynamic Modeling and Interactive Performance of PARM: A Parallel Upper-Limb Rehabilitation Robot Using Impedance Control for Patients after Stroke.

The robot-assisted therapy has been demonstrated to be effective in the improvements of limb function and even activities of daily living for patients after stroke. This paper presents an interactive upper-limb rehabilitation robot with a parallel mechanism and an isometric screen embedded in the platform to display trajectories. In the dynamic modeling for impedance control, the effects of friction and inertia are reduced by introducing the principle of virtual work and derivative of Jacobian matrix. To achieve the assist-as-needed impedance control for arbitrary trajectories, the strategy based on orthogonal deviations is proposed. Simulations and experiments were performed to validate the dynamic modeling and impedance control. Besides, to investigate the influence of the impedance in practice, a subject participated in experiments and performed two types of movements with the robot, that is, rectilinear and circular movements, under four conditions, that is, with/without resistance or impedance, respectively. The results showed that the impedance and resistance affected both mean absolute error and standard deviation of movements and also demonstrated the significant differences between movements with/without impedance and resistance (p < 0.001). Furthermore, the error patterns were discussed, which suggested that the impedance environment was capable of alleviating movement deviations by compensating the synergetic inadequacy between the shoulder and elbow joints.

An object-oriented computational model to study cardiopulmonary hemodynamic interactions in humans.

BACKGROUND AND OBJECTIVE: This work introduces an object-oriented computational model to study cardiopulmonary interactions in humans. METHODS: Modeling was performed in object-oriented programing language Matlab Simscape, where model components are connected with each other through physical connections. Constitutive and phenomenological equations of model elements are implemented based on their non-linear pressure-volume or pressure-flow relationship. The model includes more than 30 physiological compartments, which belong either to the cardiovascular or respiratory system. The model considers non-linear behaviors of veins, pulmonary capillaries, collapsible airways, alveoli, and the chest wall. Model parameters were derisved based on literature values. Model validation was performed by comparing simulation results with clinical and animal data reported in literature. RESULTS: The model is able to provide quantitative values of alveolar, pleural, interstitial, aortic and ventricular pressures, as well as heart and lung volumes during spontaneous breathing and mechanical ventilation. Results of baseline simulation demonstrate the consistency of the assigned parameters. Simulation results during mechanical ventilation with PEEP trials can be directly compared with animal and clinical data given in literature. CONCLUSIONS: Object-oriented programming languages can be used to model interconnected systems including model non-linearities. The model provides a useful tool to investigate cardiopulmonary activity during spontaneous breathing and mechanical ventilation.

Assessing regional lung mechanics by combining electrical impedance tomography and forced oscillation technique.

There is a lack of noninvasive pulmonary function tests which can assess regional information of the lungs. Electrical impedance tomography (EIT) is a radiation-free, non-invasive real-time imaging that provides regional information of ventilation volume regarding the measurement of electrical impedance distribution. Forced oscillation technique (FOT) is a pulmonary function test which is based on the measurement of respiratory mechanical impedance over a frequency range. In this article, we introduce a new measurement approach by combining FOT and EIT, named the oscillatory electrical impedance tomography (oEIT). Our oEIT measurement system consists of a valve-based FOT device, an EIT device, pressure and flow sensors, and a computer fusing the data streams. Measurements were performed on five healthy volunteers at the frequencies 3, 4, 5, 6, 7, 8, 10, 15, and 20 Hz. The measurements suggest that the combination of FOT and EIT is a promising approach. High frequency responses are visible in the derivative of the global impedance index ΔZeit(t,fos). $\Delta {Z_{{\text{eit}}}}(t,{f_{{\text{os}}}}).$ The oEIT signals consist of three main components: forced oscillation, spontaneous breathing, and heart activity. The amplitude of the oscillation component decreases with increasing frequency. The band-pass filtered oEIT signal might be a new tool in regional lung function diagnostics, since local responses to high frequency perturbation could be distinguished between different lung regions.

Model-Based Estimation of Ankle Joint Stiffness.

We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model's inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements.

Effects of the nasal passage on forced oscillation lung function measurements.

The forced oscillation technique (FOT) is a non-invasive pulmonary function test which is based on the measurement of respiratory impedance. Recently, promising results were obtained by the application of FOT on patients with respiratory failure and obstructive sleep apnea (OSA). By using a nasal mask instead of a mouthpiece, the influences of the nasal passage and upper shunt alter the measured mechanical impedance. In this paper, we investigated the effects of the nasal passage and mask on FOT measurements from eight healthy subjects. A method for flow correction has been developed, which contains a pressure-flow characteristics compensation of the undetermined flow leakage at the face-mask interface. Impedance calculation and parameter estimation were performed in the frequency domain using fast Fourier transform (FFT). Average nasal parameters were Rnaw=4.07 cmH2O/l/s for resistance and Lnaw=0.0183 cmH2O/l/s2 for inertance. On average, the nasal resistance corresponds to 65.85% of the total resistance.

Physiological closed-loop control of mechanical ventilation and extracorporeal membrane oxygenation.

A new concept is presented for cooperative automation of mechanical ventilation and extracorporeal membrane oxygenation (ECMO) therapy for treatment of acute respiratory distress syndrome (ARDS). While mechanical ventilation is continuously optimized to promote lung protection, extracorporeal gas transfer rates are simultaneously adjusted to control oxygen supply and carbon dioxide removal using a robust patient-in-the-loop control system. In addition, the cooperative therapy management uses higher-level algorithms to adjust both therapeutic approaches. The controller synthesis is derived based on the introduced objectives, the experimental setup and the uncertain models. Finally, the autonomous ARDS therapy system capabilities are demonstrated and discussed based on in vivo data from animal experiments.

Observer-Based Human Knee Stiffness Estimation.

OBJECTIVE: We consider the problem of stiffness estimation for the human knee joint during motion in the sagittal plane. METHODS: The new stiffness estimator uses a nonlinear reduced-order biomechanical model and a body sensor network (BSN). The developed model is based on a two-dimensional knee kinematics approach to calculate the angle-dependent lever arms and the torques of the muscle-tendon-complex. To minimize errors in the knee stiffness estimation procedure that result from model uncertainties, a nonlinear observer is developed. The observer uses the electromyogram (EMG) of involved muscles as input signals and the segmental orientation as the output signal to correct the observer-internal states. Because of dominating model nonlinearities and nonsmoothness of the corresponding nonlinear functions, an unscented Kalman filter is designed to compute and update the observer feedback (Kalman) gain matrix. RESULTS: The observer-based stiffness estimation algorithm is subsequently evaluated in simulations and in a test bench, specifically designed to provide robotic movement support for the human knee joint. CONCLUSION: In silico and experimental validation underline the good performance of the knee stiffness estimation even in the cases of a knee stiffening due to antagonistic coactivation. SIGNIFICANCE: We have shown the principle function of an observer-based approach to knee stiffness estimation that employs EMG signals and segmental orientation provided by our own IPANEMA BSN. The presented approach makes realtime, model-based estimation of knee stiffness with minimal instrumentation possible.

Linearity of electrical impedance tomography during maximum effort breathing and forced expiration maneuvers.

Electrical impedance tomography (EIT) provides global and regional information about ventilation by means of relative changes in electrical impedance measured with electrodes placed around the thorax. In combination with lung function tests, e.g. spirometry and body plethysmography, regional information about lung ventilation can be achieved. Impedance changes strictly correlate with lung volume during tidal breathing and mechanical ventilation. Initial studies presumed a correlation also during forced expiration maneuvers. To quantify the validity of this correlation in extreme lung volume changes during forced breathing, a measurement system was set up and applied on seven lung-healthy volunteers. Simultaneous measurements of changes in lung volume using EIT imaging and pneumotachography were obtained with different breathing patterns. Data was divided into a synchronizing phase (spontaneous breathing) and a test phase (maximum effort breathing and forced maneuvers). The EIT impedance changes correlate strictly with spirometric data during slow breathing with increasing and maximum effort ([Formula: see text]) and during forced expiration maneuvers ([Formula: see text]). Strong correlations in spirometric volume parameters [Formula: see text] ([Formula: see text]), [Formula: see text]/FVC ([Formula: see text]), and flow parameters PEF, [Formula: see text], [Formula: see text], [Formula: see text] ([Formula: see text]) were observed. According to the linearity during forced expiration maneuvers, EIT can be used during pulmonary function testing in combination with spirometry for visualisation of regional lung ventilation.

Low-Cost Wearable for Fatigue and Back-Stress Measurement in Nursing.

In recent years the need for informal home care in European countries is growing quickly due to increased life expectancy and demographic change. Informal caregivers have to overcome many obstacles ranging from a lack of adequate training to misjudging their physical and psychological abilities. The aim of this project is to create a low cost wearable device, which unobtrusively measures the physical stress load on caregivers. Two parameters with the most impact on performance and well-being of the caregiver have been identified: (i) fatigue and (ii) back-stress. Based on the measurements, an alert is issued if caregivers are not performing a task correctly, or if they are overexerting themselves. This paper discusses the design of such device and description of an initial prototype, its advantages and possible further development and applications.

Body-Sensor-Network-Based Spasticity Detection.

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

An object-oriented model of the cardiopulmonary system with emphasis on the gravity effect.

We introduce a novel comprehensive model of the cardiopulmonary system with emphasis on perfusion and ventilation distribution along the vertical thorax axis under the gravity effect. By using an object-oriented environment, the complex physiological system can be represented by a network of electrical, lumped-element compartments. The lungs are divided into three zones: upper, middle, and lower zone. Blood flow increases with the distance from the apex to the base of the lungs. The upper zone is characterized by a complete collapse of the pulmonary capillary vasculature; thus, there is no flow in this zone. The second zone has a "waterfall effect" where the blood flow is determined by the difference between the pulmonary-arterial and alveolar pressures. At resting position, the upper lobes of the lungs are more expanded than the middle and lower lobes. However, during spontaneous breathing, ventilation is nonuniform with more air entering the lower lobes than the middle and upper lobes. A simulative model of the complete system is developed which shows results in good agreement with the literature.

Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems.

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed "Integrated Posture and Activity Network by Medit Aachen" (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is Sensors 2015, 15 25920 corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of 3.89°.

Electrical Bioimpedance-Controlled Surgical Instrumentation.

A bioimpedance-controlled concept for bone cement milling during revision total hip replacement is presented. Normally, the surgeon manually removes bone cement using a hammer and chisel. However, this procedure is relatively rough and unintended harm may occur to tissue at any time. The proposed bioimpedance-controlled surgical instrumentation improves this process because, for example, most risks associated with bone cement removal are avoided. The electrical bioimpedance measurements enable online process-control by using the milling head as both a cutting tool and measurement electrode at the same time. Furthermore, a novel integrated surgical milling tool is introduced, which allows acquisition of electrical bioimpedance data for online control; these data are used as a process variable. Process identification is based on finite element method simulation and on experimental studies with a rapid control prototyping system. The control loop design includes the identified process model, the characterization of noise as being normally distributed and the filtering, which is necessary for sufficient accuracy ( ±0.5 mm). Also, in a comparative study, noise suppression is investigated in silico with a moving average filter and a Kalman filter. Finally, performance analysis shows that the bioimpedance-controlled surgical instrumentation may also performs effectively at a higher feed rate (e.g., 5 mm/s).

Control of an electromechanical hydrocephalus shunt--a new approach.

Hydrocephalus is characterized by an excessive accumulation of cerebrospinal fluid (CSF). Therapeutically, an artificial pressure relief valve (so-called shunt) is implanted which opens in case of increased intracranial pressure (ICP) and drains CSF into another body compartment. Today, available shunts are of a mechanical nature and drainage depends on the pressure drop across the shunt. According to the latest data, craniospinal compliance is considered to be even more important than mean ICP alone. In addition, ICP is not constant but varies due to several influences. In fact, heartbeat-related ICP waveform patterns depend on volume changes in the cranial vessels during a heartbeat and changes its shape as a function of craniospinal compliance. In this paper, we present an electromechanical shunt approach, which changes the CSF drainage as a function of the current ICP waveform. A series of 12 infusion tests in patients were analyzed and revealed a trend between the compliance and specific features of the ICP waveform. For waveform analysis of patient data, an existing signal processing algorithm was improved (using a Moore machine) and was implemented on a low-power microcontroller within the electromechanical shunt. In a test rig, the ICP waveforms were replicated and the decisions of the ICP analysis algorithm were verified. The proposed control algorithm consists of a cascaded integral controller which determines the target ICP from the measured waveform, and a faster inner-loop integral controller that keeps ICP close to the target pressure. Feedforward control using measurement data of the patient's position was implemented to compensate for changes in hydrostatic pressure during change in position. A model-based design procedure was used to lay out controller parameters in a simple model of the cerebrospinal system. Successful simulation results have been obtained with this new approach by keeping ICP within the target range for a healthy waveform.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

BACKGROUND: Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. METHODS: We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. RESULTS: Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. CONCLUSIONS: It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

Body sensor network-based strapdown orientation estimation: application to human locomotion.

In this contribution, inertial and magnetic sensors are considered for real-time strapdown orientation tracking of human limb or robotic segment orientation. By using body sensor network integrated triaxial gyrometer, accelerometer, and magnetometer measurements, two orientation estimation filters are presented and subsequently designed for bias insensitive tracking of human gait. Both filters use quaternions for rotation representation, where preprocessing accelerometer and magnetometer data is conducted with the quaternion based estimation algorithm (QUEST) as a reference filter input. This results in a significant reduction of the complexity and calculation cost on the body sensor network. QUEST-based preprocessed attitude data is used for the designed extended Kalman filter (EKF) and a new complementary sliding mode observer. EKF-QUEST and complementary sliding mode observer are designed and tested in simulations and subsequently validated with a reference motion tracking system in treadmill tests.

Simultaneous automatic control of oxygen and carbon dioxide blood gases during cardiopulmonary bypass.

In this work an automatic control strategy is presented for the simultaneous control of oxygen and carbon dioxide blood gas partial pressures to be used during cardiopulmonary bypass surgery with heart-lung machine support. As the exchange of blood gases in the artificial extracorporeal lung is a highly nonlinear process comprising varying time delays, uncertainties, and time-varying parameters, it is currently being controlled manually by specially trained perfusionist staff. The new control strategy includes a feedback linearization routine with augmented time-delay compensation and two external linear gain-scheduled controllers, for partial oxygen and carbon dioxide pressures. The controllers were robustly tuned and tested in simulations with a detailed artificial lung (oxygenator) model in cardiopulmonary bypass conditions. Furthermore, the controllers were implemented in an ex vivo experiment using fresh porcine blood as a substitute fluid and a special deoxygenation technique to simulate a patient undergoing cardiopulmonary bypass. Both controllers showed robust stability during the experiments and a good disturbance rejection to extracorporeal blood flow changes. This automatic control strategy is proposed to improve patient's safety by fast control reference tracking and good disturbance rejection under varying conditions.