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Editorial

J. Dyn. Sys., Meas., Control. 2016;138(11):110201-110201-2. doi:10.1115/1.4033774.
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Medical devices and sensors are routinely used to record various physiological signals for simple monitoring of the human health, and sensing is an integral part of health monitoring. However, more advanced devices, signal measurement, modeling, and closed-loop control may be critical for surgical procedures, earlier diagnosis of diseases and disorders, drug administration, and clinical rehabilitation. The goal of this special issue is to provide a forum for latest research in biomedical signal measurement and processing, dynamic modeling and analysis, and control for clinical diagnosis, patient health monitoring, drug administration, and biosignal-assisted rehabilitation. There has been a significant increase in research activities in these areas within diverse specialties including mechanical, electrical, and biomedical engineering. Developing sensors to produce appropriate biosignals, developing dynamic models of biosystems and biosignals for diagnostics, and using biosignals as feedback in controlled processes such as drug delivery and rehabilitation are some of the biggest challenges encountered in these engineering fields.

Commentary by Dr. Valentin Fuster

Research Papers

J. Dyn. Sys., Meas., Control. 2016;138(11):111001-111001-5. doi:10.1115/1.4033829.

The purpose of this study was to evaluate the relationship between forceplate inaccuracies and joint torques during running. Instrumented gait analysis data were collected on a single subject running above ground. A Monte Carlo analysis was performed using 60 simulations. In each simulation, joint torques were computed as the ground reaction force (GRF) data were perturbed. Errors in joint torques were larger for proximal joints compared to the distal joints. These errors in joint torques were due more to inaccuracies in the GRF magnitude than the center of pressure (COP) measurements. Clinically, these results may be used to determine a priori the forceplate accuracy needed to measure a desired difference in joint torque between patient populations.

Topics: Torque , Errors , Knee
Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111002-111002-11. doi:10.1115/1.4033830.

Wireless capsule endoscopes (WCE) are a new technology for inspection of the intestines, which offer many advantages over conventional endoscopes, while devices currently in use are passive and can only follow the natural transit of the intestines. There is a considerable interest in methods of controlled actuation for these devices. In this paper, an actuation system based on magnetic levitation is proposed, utilizing a small permanent magnet within the capsule and an arrangement of digitally controlled electromagnet placed on a movable frame. The objective of this paper is to design a multi-input multi-output (MIMO), three degrees-of-freedom (3DOF) tracking system for capsule endoscope. Two techniques, entire eigenstructure assignment (EEA) and linear quadratic regulator (LQR), are presented to design the controller of the system. The performance of the EEA and LQR controllers was compared based on the stability parameters to validate the proposed actuation system. Finally, simulation results suggest that the LQR approach can be used to synthesize a suitable and simple controller for this application.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111003-111003-12. doi:10.1115/1.4033831.

Stroke patients are often affected by hand impairment. Literature shows different experiences of robotic rehabilitation that is able to prove an intensive and effective therapy. A preliminary analysis of the state of the art evidenced lacks in hand robotic rehabilitation devices. Thus, this work proposes a new rehabilitation device for hand rehabilitation based on a compliant transmission. The mechanical power generator is not on the hand to reduce the weight of the device. The mechanical model of the system is descripted. Experimental results on 126 stroke patients evidenced the efficacy of this device

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111004-111004-9. doi:10.1115/1.4033949.
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In this paper, a wireless human motion monitoring system is presented for gait analysis and visual feedback in rehabilitation training. The system consists of several inertial sensors and a pair of smart shoes with pressure sensors. The inertial sensors can capture lower-extremity joint rotations in three dimensions and the smart shoes can measure the force distributions on the two feet during walking. Based on the raw measurement data, gait phases, step lengths, and center of pressure (CoP) are calculated to evaluate the abnormal walking behaviors. User interfaces are developed on both laptops and mobile devices to provide visual feedback to patients and physical therapists. The system has been tested on healthy subjects and then applied in a clinical study with 24 patients. It has been verified that the patients are able to understand the intuitive visual feedback from the system, and similar training performance has been achieved compared to the traditional gait training with physical therapists. The experimental results with one healthy subject, one stroke patient, and one Parkinson's disease patient are compared to demonstrate the performance of the system.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111005-111005-8. doi:10.1115/1.4033833.

This paper presents a closed-loop control of fluid resuscitation to overcome hypovolemia based on model-based estimation of relative changes in blood volume (BV). In this approach, the control system consists of a model-based relative BV (RBV) estimator and a feedback controller. The former predicts relative changes in the BV response to augmented fluid by analyzing an arterial blood pressure (BP) waveform and the electrocardiogram (ECG). Then, the latter determines the amount of fluid to be augmented by comparing target versus predicted relative changes in BV. In this way, unlike many previous methods for fluid resuscitation based on controlled variable(s) nonlinearly correlated with the changes in BV, fluid resuscitation can be guided by a controlled variable linearly correlated with the changes in BV. This paper reports initial design of the closed-loop fluid resuscitation system and its in silico evaluation in a wide range of hypovolemic scenarios. The results suggest that closed-loop fluid resuscitation guided by a controlled variable linearly correlated with the changes in BV can be effective in overcoming hypovolemia: across 100 randomly produced hypovolemia cases, it resulted in the BV regulation error of 7.98 ± 171.6 ml, amounting to 0.18 ± 3.04% of the underlying BV. When guided by pulse pressure (PP), a classical controlled variable nonlinearly correlated with the changes in BV; the same closed-loop fluid resuscitation system resulted in persistent under-resuscitation with the BV regulation error of −779.1 ± 147.4 ml, amounting to −13.9 ± 2.65% of the underlying BV.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111006-111006-7. doi:10.1115/1.4033834.
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We present a computationally efficient approach for the intra-operative update of the feedback control policy for the steerable needle in the presence of the motion uncertainty. The solution to dynamic programming (DP) equations, to obtain the optimal control policy, is difficult or intractable for nonlinear problems such as steering flexible needle in soft tissue. We use the method of approximating Markov chain to approximate the continuous (and controlled) process with its discrete and locally consistent counterpart. This provides the ground to examine the linear programming (LP) approach to solve the imposed DP problem that significantly reduces the computational demand. A concrete example of the two-dimensional (2D) needle steering is considered to investigate the effectiveness of the LP method for both deterministic and stochastic systems. We compare the performance of the LP-based policy with the results obtained through more computationally demanding algorithm, iterative policy space approximation. Finally, the reliability of the LP-based policy dealing with motion and parametric uncertainties as well as the effect of insertion point/angle on the probability of success is investigated.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111007-111007-8. doi:10.1115/1.4033836.

In this paper, we propose a quantitative approach based on identifying hand trajectory dissimilarities through the use of a multidimensional scaling (MDS) analysis. A high-rate motion capture system is used to gather three-dimensional (3D) trajectory data of healthy and stroke-impacted hemiparetic subjects. The mutual dissimilarity between any two trajectories is measured by the area between them. This area is used as a dissimilarity variable to create an MDS map. The map reveals a structure for measuring the difference and variability of individual trajectories and their groups. The results suggest that the recovery of hemiparetic subjects can be quantified by comparing the difference and variability of their individual MDS map points to the points from the cluster of healthy subject trajectories. Within the MDS map, we can identify fully recovered patients, those who are only functionally recovered, and those who are either in an early phase of, or are nonresponsive to the therapy.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111008-111008-10. doi:10.1115/1.4033832.

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111009-111009-11. doi:10.1115/1.4033835.

Myoelectric classification has been widely studied for controlling prosthetic devices and human computer interface (HCI). However, it is still not robust due to external conditions: limb position changes, electrode shifts, and skin condition changes. These issues compromise the reliability of pattern recognition techniques in myoelectric systems. In order to increase the reliability in the limb position effect when a limb position is changed from the position in which the system is trained, this paper proposes a myoelectric system using dynamic motions. Dynamic time warping (DTW) technique was used for the alignment of two different time-length motions, and correlation coefficients were then calculated as a similarity metric to classify dynamic motions. On the other hand, Fisher's linear discriminant analysis was applied on static motions for the purpose of dimensionality reduction and Naïve Bayesian classifier for classifying the motions. To estimate the robustness to the limb position effect, static and dynamic motions were collected at four different limb positions from eight human subjects. The statistical analysis, t-test (p < 0.05), showed that, for all subjects, dynamic motions were more robust to the limb position effect than static motions when training and validation sets were extracted from different limb positions with the best classification accuracy of 97.59% and 3.54% standard deviation (SD) for dynamic motions compared with 71.85% with 12.62% SD for static motions.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111010-111010-16. doi:10.1115/1.4033775.

A bilateral nonlinear adaptive impedance controller is proposed for the control of multi-degrees-of-freedom (DOF) teleoperation systems. In this controller, instead of conventional position and/or force tracking, the impedance of the nonlinear teleoperation system is controlled. The controller provides asymptotic tracking of two impedance models in Cartesian coordinates for the master and slave robots. The proposed bilateral controller can be used in different medical applications, such as telesurgery and telerehabilitation, where the impedance of the robot in interaction with human subject is of great importance. The parameters of the two impedance models can be adjusted according to the application and corresponding objectives and requirements. The dynamic uncertainties are considered in the model of the master and slave robots. The stability and the tracking performance of the system are proved via a Lyapunov analysis. Moreover, the adaptation laws are proposed in the joint space for reducing the computational complexity, however, the controller and the stability proof are all presented in Cartesian coordinates. Using simulations on a 2DOF robot, the effectiveness of the proposed controller is investigated in telesurgery and telerehabilitation operations.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111011-111011-10. doi:10.1115/1.4033837.
OPEN ACCESS

Integrating an exoskeleton as the external apparatus for a brain–machine interface (BMI) has the advantage of providing multiple contact points to determine body segment postures and allowing control to and feedback from each joint. When using macaques as subjects to study the neural control of movement, an upper limb exoskeleton design with unlikely singularity is required to guarantee safe and accurate tracking of joint angles over all possible range of motion (ROM). Additionally, the compactness of the design is of more importance considering macaques have significantly smaller body dimensions than humans. This paper proposes a six degree-of-freedom (DOF) passive upper limb exoskeleton with 4DOFs at the shoulder complex. System kinematic analysis is investigated in terms of its singularity and manipulability. A real-time data acquisition system is set up, and system kinematic calibration is conducted. The effectiveness of the proposed exoskeleton system is finally demonstrated by a pilot animal test in the scenario of a reach and grasp task.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111012-111012-8. doi:10.1115/1.4033838.

Automated early detection of myocardial infarction (MI) has been long studied for the purpose of saving human lives. In this paper, we propose a rule-based expert system to analyze a 12-lead electrocardiogram (ECG) for various types of MI. This system is developed by mapping clinical definitions of different types of MI and their differential diagnosis into corresponding algorithmic rule sets. Essential preprocessing steps such as baseline correction, removal of ectopic beats, and median filtering are carried out on recorded ECG. Techniques such as multistage polynomial correction and QRS subtraction are exploited to achieve reliable preprocessing. The processed ECG is then delineated using a time-domain differential-based search algorithm recently proposed by the team to obtain the relevant features and measures. These features and measures are further utilized by an if-then rule set to classify the ECG into various groups. The performance of the system when validated on sample MI database exhibited a sensitivity of 95.7% and specificity of 94.6%. Unlike many previous works, this reliable performance is achieved without the use of abstract classifiers or the need of prior training. Being based on medical definitions, the system is also easily comprehensible, modifiable, and compatible with manual diagnosis.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111013-111013-8. doi:10.1115/1.4033864.

This paper is concerned with the mathematical modeling and detection of endotracheal (ET) intubation in children under general anesthesia during surgery. In major pediatric surgeries, the airway is often secured with an endotracheal tube (ETT) followed by initiation of mechanical ventilation. Clinicians utilize auscultation of breath sounds and capnography to verify correct ETT placement. However, anesthesia providers often delay timely charting of ET intubation. This latency in event documentation results in decreased efficacy of clinical decision support systems. In order to target this problem, we collected real inpatient data and designed an algorithm to accurately detect the intubation time within the clinically valid range; the results show that we are able to achieve high accuracy in more than 96% of the cases. Automatic detection of ET intubation time would thus enhance better real-time data capture to support future improvement in clinical decision support systems.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(11):111014-111014-12. doi:10.1115/1.4033863.

A simulation study to control the motion of a human arm using muscle excitations as inputs is presented to validate a recently developed adaptive output feedback controller for a class of unknown multi-input multi-output (MIMO) systems. The main contribution of this paper is to extend the results of Nguyen and Leonessa (2014, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems,” ASME Paper No. DSCC2014-6214; 2014, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results,” ASME Paper No. DSCC2014-6217; and 2015, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Systems: Experimental Results,” American Control Conference, pp. 3515–3521) by combining a recently developed fast adaptation technique and a new controller structure to derive a simple approach for a class of high relative degree uncertain systems. Specifically, the presented control approach relies on three components: a predictor, a reference model, and a controller. The predictor is designed to predict the systems output for any admissible control input. A full state feedback control law is then derived to control the predictor output to approach the reference system. The control law avoids the recursive step-by-step design of backstepping and remains simple regardless of the system relative degree. Ultimately, the control objective of driving the actual system output to track the desired trajectory is achieved by showing that the system output, the predictor output, and the reference system trajectories all converge to each other. Thelen and Millard musculotendon models (Thelen, D. G., 2003, “Adjustment of Muscle Mechanics Model Parameters to Simulate Dynamic Contractions in Older Adults,” ASME J. Biomech. Eng., 125(1), pp. 70–77; Millard, M, Uchida, T, Seth, A, and Delp, Scott L., 2013, “Flexing Computational Muscle: Modeling and Simulation of Musculotendon Dynamics,” ASME J. Biomech. Eng., 135(2), p. 021005) are used to validate the proposed controller fast tracking performance and robustness.

Commentary by Dr. Valentin Fuster

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