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Objective: A tendon-transfer is a reconstructive orthopedic surgery where tendons are re-routed from a non-functioning muscle and attached a functioning muscle. Prior work has shown that using a passive implanted device in the ECRL-to-FDP tendon-transfer surgery significantly improves hand grasping function. However, it is still unclear how hand-function improvement, measured by finger joint range of motion and torque, is dependent on the implant’s geometry and location within the tendon network. This paper presents a genetic algorithm that determines the device’s optimal geometry and location. Methods: Hand biomechanical simulation platform was developed to model hand function and also model the tendon-transfer surgery with and without the implant. Finger kinematics and joint torque were used to develop three unique objective functions to optimize the implant’s parameters. Results: The optimized device resulted in an 11X increase in finger kinematics with only a 0.9% decrease in joint torque when compared with the biomechanical function enabled by the current suture-based surgery. Conclusion: Designing implantable devices that modify musculoskeletal function is challenging. Factors like tendon routing and joint kinematics create a complex nonlinear system when considering biomechanical function. A genetic algorithm is an effective tool to tackle these nonlinear landscapes to produce optimized designs. Significance: The state-of-the-art surgical procedure to repair high median-ulnar nerve palsy leads to poor hand function and severely limits the patient’s ability to perform activities of daily life. This work provides a method for defining relevant objective functions for hand biomechanical function and then uses those objective functions with genetic algorithms to optimize the geometry of an orthopedic implant across multiple variables. The achieved biomechanical function is significantly better than hand function enabled by current surgical procedure. [ABSTRACT FROM AUTHOR]

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes [ABSTRACT FROM AUTHOR]

Human Body Communication (HBC) is an alternative to radio wave-based Wireless Body Area Network (WBAN) because of its wide bandwidth leading to enhanced energy efficiency. Designing Modern HBC devices need the accurate electrical equivalent of the HBC channel for energy efficient communication. The objective of this paper is to present an improved lumped element-based detailed model of Galvanic HBC channel which can be used to explain the dependency of the channel behaviour on the internal body dependent parameters such as electrical properties of skin and muscle tissue layers along with the external parameters such as electrode size, electrode separation, geometrical position of the electrodes and return-path or parasitic capacitances. The model considers the frequency-dependent impedance of skin and muscle tissue layers and the effect of various coupling capacitances between the body and Tx/Rx electrodes to the Earth-Ground. A 2D planar structure of skin and muscle tissue layers is simulated using a Finite Element Method (FEM) tool to prove the validity of the proposed model. The effect of symmetry and asymmetry at the transmitter and receiver ends is also explained using the model. The model become very useful for fast calculation of Galvanic channel response without using any FEM tool. Experimental results show that the galvanic response is not only a function of channel length but also depends on the mismatch at the transmitter and receiver end. In case of a very high mismatch scenario, the channel behavior is dominated by the capacitive HBC, even for a galvanic excitation and termination. [ABSTRACT FROM AUTHOR]

With the advent of wearables, Human Body Communication (HBC) has emerged as a physically secure and power-efficient alternative to the otherwise ubiquitous Wireless Body Area Network (WBAN). Whereas the most investigated HBC modalities have been Electric and Electro-quasistatic (EQS) Capacitive and Galvanic, recently Magnetic HBC (M-HBC) has been proposed as a viable alternative. Previous works have investigated M-HBC through application points-of-view, without exploring its fundamental working principle. In this paper, a ground up analysis is performed to study the possible effects and contributions of the human body channel in M-HBC over 1kHz to 10 GHz, by electromagnetic simulations and supporting experiments. The results show that while M-HBC can be successfully operated as a body area network, the human body itself plays a minimal or negligible role in its functionality. For Magneto-quasistatic (MQS) HBC (frequencies less than ∼30 MHz), the body is transparent to the quasistatic magnetic field. Conversely for higher frequencies, the conductivity of human tissues attenuates Magnetic HBC fields due to induced Eddy currents, preventing the body to support efficient waveguide modes. With this conceptual understanding developed, different modes of operations of MQS HBC are outlined for both high impedance capacitive and 50Ω termination cases, and their performances are compared with EQS HBC for similar sized devices, over varying distances between TX and RX. The resulting report presents a fundamental understanding towards M-HBC operation and its contrast with EQS HBC, aiding HBC device designers to make educated design decisions, depending on application scenarios. [ABSTRACT FROM AUTHOR]

Objective: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. Methods: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. Results: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. Conclusion: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. Significance: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed. [ABSTRACT FROM AUTHOR]

Objective: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. Methods: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). Results: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Conclusions: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. Significance: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to model and experimentally validate the path loss benefits of magnetic human body communication (mHBC) using small form-factor-accurate coils operating under realistic conditions. Methods: A radiating near-field coupling model and numerical simulations are presented to show that the magnetic-dominant near-field coupling between resonant coils offers low path loss across the body and exhibits extra robustness to antenna misalignment compared to far-field RF schemes. To overcome the pitfalls in conventional vector-network-analyzer-based measurement configurations, we propose a standardized setup applied to broadband channel loss measurement with portable instruments. Two types of PCB coils for mHBC communication, designed for large devices such as smartphones and small devices such as earbuds, respectively, are built and measured. Results: The mHBC link for the ear-to-ear non-line-of-sight (NLOS) path measures up to −23.1 dB and −31.2 dB with large and small coils, respectively, which is 50 dB more efficient than the conventional Bluetooth channels utilizing antennas of similar sizes. Ear-to-pocket and pocket-to-pocket channels also show at least 16 dB higher transmission than the Bluetooth channel. Conclusion: In terms of path loss, the mHBC approach offers compelling performance for short-range applications over the body region. For coils with dimensions of several centimeters, working between 100 MHz and 200 MHz minimizes the channel loss while keeping the bandwidth above 1 MHz. Significance: The extremely high efficiency of the proposed mHBC channel provides a solution to the energy problem for miniaturized wearables, potentially leading to new wearable device designs. [ABSTRACT FROM AUTHOR]

Objective: Electrical impedance myography (EIM) measures bioimpedance over muscles. This paper proposes a circuit-based modelling methodology originated from finite element analysis (FEA), to emulate tissues and effects from anthropometric variations, and electrode placements, on EIM measurements. The proposed methodology is demonstrated on the upper arms and lower legs. Methods: FEA evaluates impedance spectra (Z-parameters), sensitivity, and volume impedance density for variations of subcutaneous fat thickness (t $_{f}$), muscle thickness (t $_{m}$), and inter-electrode distance (IED), on limb models over 1Hz-1 MHz frequency range. The limbs’ models are based on simplified anatomical data and dielectric properties from published sources. Contributions of tissues to the total impedance are computed from impedance sensitivity and density. FEA Z-parameters are imported into a circuit design environment, and used to develop a three Cole dispersion circuit-based model. FEA and circuit model simulation results are compared with measurements on ten human subjects. Results: Muscle contributions are maximized at 31.25 kHz and 62.5 kHz for the upper arm and lower leg, respectively, at 4 cm IED. The circuit model emulates variations in t $_{f}$ and t $_{m}$ , and simulates up to 89 times faster than FEA. The circuit model matches subjects measurements with RMS errors $\mathbf {< \;}36.43\boldsymbol{\Omega }$ and $\mathbf {< \;}17.28\mathbf {^{\circ }}$ , while FEA does with $\mathbf {< \;}36.59\boldsymbol{\Omega }$ and $\mathbf {< \;}4.36\mathbf {^{\circ }}$. Conclusions: We demonstrate that FEA is able to estimate the optimal frequencies and electrode placements, and circuit-based modelling can accurately emulate the limbs’ bioimpedance. Significance: The proposed methodology facilitates studying the impact of biophysical principles on EIM, enabling the development of future EIM acquisition systems. [ABSTRACT FROM AUTHOR]

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers. Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches. Conclusion and Significance: This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies. [ABSTRACT FROM AUTHOR]

Electrooculography (EOG) can be used to measure eye movements while the eyelids are open or closed and to assist in the diagnosis of certain eye diseases. However, challenges in biosignal acquisition and processing lead to limited accuracy, limited resolution (both temporal and spatial), as well as difficulties in reducing noise and detecting artifacts. Methods such as finite impulse response, wavelet transforms, and averaging filters have been used to denoise and enhance EOG measurements. However, these filters are not specifically designed to detect saccades, and so key features (e.g., saccade amplitude) can be over-filtered and distorted as a consequence of the filtering process. Here we present a model-based fusion technique to enhance saccade features within noisy and raw EOG signals. Specifically, we focus on Westheimer (WH) and linear reciprocal (LR) eye models with a Kalman filter. EOG signals were measured using OpenBCI’s Cyton Board (at 250 Hz), and these measurements were compared with a state-of-the-art EyeLink 1000 (EL; 250 Hz) eye tracker. On average, the LR model-based KF produced a 47% improvement of measurement accuracy over the bandpass filters. Thus, we conclude that our LR model-based KF outperforms standard bandpass filtering techniques in reducing noise, eliminating artifacts, and restoring missing features of saccade signatures present within EOG signals. [ABSTRACT FROM AUTHOR]

In the above paper [1] there are two errors which we correct here. An important reference was omitted. [ABSTRACT FROM AUTHOR]

Objective: Statistical shape models have been successfully used in numerous biomedical image analysis applications where prior shape information is helpful such as organ segmentation or data augmentation when training deep learning models. However, training such models requires large data sets, which are often not available and, hence, shape models frequently fail to represent local details of unseen shapes. This work introduces a kernel-based method to alleviate this problem via so-called model localization. It is specifically designed to be used in large-scale shape modeling scenarios like deep learning data augmentation and fits seamlessly into the classical shape modeling framework. Method: Relying on recent advances in multi-level shape model localization via distance-based covariance matrix manipulations and Grassmannian-based level fusion, this work proposes a novel and computationally efficient kernel-based localization technique. Moreover, a novel way to improve the specificity of such models via normalizing flow-based density estimation is presented. Results: The method is evaluated on the publicly available JSRT/SCR chest X-ray and IXI brain data sets. The results confirm the effectiveness of the kernelized formulation and also highlight the models’ improved specificity when utilizing the proposed density estimation method. Conclusion: This work shows that flexible and specific shape models from few training samples can be generated in a computationally efficient way by combining ideas from kernel theory and normalizing flows. Significance: The proposed method together with its publicly available implementation allows to build shape models from few training samples directly usable for applications like data augmentation. [ABSTRACT FROM AUTHOR]

Objective: Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. Methods: The proposed anatomically parameterized SSM (ANAT $_{\text{SSM}}$) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT $_{\text{SSM}}$. We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT $_{\text{SSM}}$) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). Results: Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. Conclusion: This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. Significance: The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment. [ABSTRACT FROM AUTHOR]

Objective: During dynamic simulations, residuals are nonphysical generalized forces/moments that dynamically balance external and inertial forces/moments, accounting for data processing and modelling errors. Hicks et al. (2015) made the original residual threshold recommendations for an acceptable simulation, but these thresholds are not based on the dynamic, physics-based movement characteristics. In this study, we present three new, physics-based guidelines for accepting dynamic simulations of movement using zero moment point computations and thresholds for forces, center of pressure, and free moment. Methods: We formulate new guidelines and evaluate them alongside the original 2015 recommendations using two movements: single-leg jump-landing (SLJL) and walking gait. We also present a MATLAB function for users to test if their simulations meet these guidelines. Results: We found that on average, only 4.3% (SLJL) and 8.2% (walking gait) of the original 2015 residuals volume met all the new physics-based guidelines. The free-moment guideline was the most restrictive for reasonable simulations, especially for high-velocity movements at times with lower vertical ground reaction forces. Additionally, some of the new recommended residuals volume fell outside of the original 2015 recommendations. Moreover, accepting reasonable simulations using different thresholds leads to different joint torques as high as 24 Nm (SLJL) and 8.2 Nm (walking gait). Conclusion: The physics-based guidelines are overall more restrictive than the original 2015 recommendations and elicit different simulation kinetics. Significance: Using different guidelines may lead to different conclusions and clinical interpretations. We advocate for the physics-based guidelines as they are built upon the dynamic, physics-based characteristics of the movement. [ABSTRACT FROM AUTHOR]

Human Body Communication (HBC) has come up as a promising alternative to traditional radio frequency (RF) Wireless Body Area Network (WBAN) technologies. This is essentially due to HBC providing a broadband communication channel with enhanced signal security in the physical layer due to lower radiation from the human body as compared to its RF counterparts. An in-depth understanding of the mechanism for the channel loss variability and associated biophysical model needs to be developed before electro-quasistatic (EQS) HBC can be used more frequently in WBAN consumer and medical applications. Recent developments have shown biophysical models that capture the channel response for fixed transmitter and receiver positions on the human body which do not capture the variability in the HBC channel for varying positions of the devices with respect to the body. In this study, we provide a detailed analysis of the change in path loss in a capacitive-HBC channel in the EQS domain. Causes of channel loss variability namely: inter-device coupling and effects of fringe fields due to body's shadowing effects are investigated. FEM based simulation results are used to analyze the channel response of human body for different positions and sizes of the device which are further verified using measurement results to validate the developed biophysical model. Using the biophysical model, we develop a closed form equation for the path loss in a capacitive HBC channel which is then analyzed as a function of the geometric properties of the device and the position with respect to the human body which will help pave the path towards future EQS-HBC WBAN design. [ABSTRACT FROM AUTHOR]

Adding haptic feedback has been reported to improve the outcome of minimally invasive robotic surgery. In this study, we seek to determine whether an algorithm based on simulating responses of a cutaneous afferent population can be implemented to improve the performance of presenting haptic feedback for robot-assisted surgery. We propose a bio-inspired controlling model to present vibration and force feedback to help surgeons localize underlying structures in phantom tissue. A single pair of actuators was controlled by outputs of a model of a population of cutaneous afferents based on the pressure signal from a single sensor embedded in surgical forceps. We recruited 25 subjects including 10 expert surgeons to evaluate the performance of the bio-inspired controlling model in an artificial palpation task using the da Vinci surgical robot. Among the control methods tested, the bio-inspired system was unique in allowing both novices and experts to easily identify the locations of all classes of tumors and did so with reduced contact force and tumor contact time. This work demonstrates the utility of our bio-inspired multi-modal feedback system, which resulted in superior performance for both novice and professional users, in comparison to a traditional linear and the existing piecewise discrete algorithms of haptic feedback. [ABSTRACT FROM AUTHOR]

Objective: To improve insulin treatment in type 2 diabetes (T2D) using model-based control techniques, the underlying model needs to be individualized to each patient. Due to the impact of unknown meals, exercise and other factors on the blood glucose, it is difficult to utilize available data from continuous glucose monitors (CGMs) for model fitting and parameter estimation purposes. Methods: To overcome this problem, we propose a novel method for modeling the glycemic disturbances as a stochastic process. To differentiate meals from other glycemic disturbances, we model the meal intake as a separate stochastic process while encompassing all other disturbances in another stochastic process. Using particle filtering, we validate the model on simulations as well as on clinical data. Results: Based on simulated CGM data, the residuals generated by the particle filter are white, indicating a good model fit. For the clinical data, we use parameter values estimated based on fasting glucose data. The residuals obtained from clinical CGM data contain correlations up to lag 5. Conclusion: The proposed model is shown to adequately describe the meal-induced glucose fluctuations in simulated CGM data while validations on clinical CGM data show promising results as well. Significance: The proposed model may lay the grounds for new ways of utilizing available CGM data, including CGM-based parameter estimation and stochastic optimal control. [ABSTRACT FROM AUTHOR]