Jun Ueda

The 1975 Asilomar Conference established safety principles for synthetic DNA technologies. Today, lipid-nanoparticle-encapsulated nucleic acids globally circumvent those principles. This stems not from emergency measures, but from misapplying outdated vaccine classifications to gene-transfer technologies.Classifying infectious-disease-targeted gene-transfer technologies as vaccines exempts them from mandatory pharmacokinetic and genotoxicity studies. This unjustified exemption now standardizes routine use in healthy populations, relying on assumptions lacking mechanism-informed data on distribution, persistence, and genomic interactions.We propose “Asilomar 2027”—a global summit establishing: (i) regulatory classification based on biological mechanisms, not intended-use labels; (ii) preclinical verification aligned with biological reality; (iii) shifting the evidentiary burden to manufacturers to prospectively prove safety via auditable data before deployment; and (iv) independent international oversight.This framework couples innovation with biology, restoring the rigorous safety standards essential for sustainable medical progress.

System identification of human sensorimotor systems requires multiple experimental trials to achieve reliable parameter estimates, yet practical constraints limit the total number of trials possible. While pseudorandom sequence (PRS) perturbations are widely used due to their white noise-like properties, and optimal multisines can theoretically provide better performance when prior system knowledge is available, their implementation on mechanical devices presents significant challenges. Device dynamics can degrade the designed spectral properties of both perturbation types, increasing the number of required trials to achieve desired estimation precision. This paper presents a foundational framework for device-dynamicsaware perturbation design that reduces the necessary number of experimental trials. The framework introduces two key components: a prefilter for PRS to minimize digital-to-analog conversion effects, and a modified cost function for multisine optimization that explicitly compensates for mechanical device dynamics. We propose a two-stage approach where the prefiltered PRS first provides initial estimates that inform subsequent optimal multisine design. Through human arm impedance experiments and devicerendered validation, we demonstrate that our framework achieves much smaller covariance resulting in fewer trials to achieve satisfactory identification performance compared to conventional methods. The optimal multisine stage, enhanced by device dynamics compensation, shows particular effectiveness in reducing parameter covariance. The covariance improvement translates to multiple practical benefits: a potential 62.5% reduction in required trial numbers when full-length signals are used, a 75% reduction in single-trial duration while maintaining estimation quality, or various combinations of these improvements depending on experimental constraints. These results establish a practical path toward more efficient human system identification protocols that minimize experimental burden while maintaining estimation accuracy.

Assistive robotics has become one of the key innovations to ensure that everyone, including vulnerable groups like the elderly and people with disabilities, can live independently and enrich their ...

Networkedcontrol systems are vulnerable to manipulation via data injection to observed states and control commands, resulting in undesired state trajectories and system instabilities. Adversarial attacks against such systems can be implemented in the form of undetectable attacks such that an observer never notices deviations from expected behavior. Even when protected by homomorphic encryption, these systems remain vulnerable to stealthy and perfectly undetectable attacks due to the malleability of encrypted data. This research develops a defense architecture against such undetectable attacks through the fusion of two complementary detection protocols working in conjunction with encryption. The mechanism’s strengths and weaknesses are analyzed for affine transformation-based perfectly undetectable attacks and covert attacks. The attacks are implemented against a mobile robot, and defense performance is analyzed, resulting in a robust defense mechanism that outperforms previous undetectable attack detection methods in terms of detection accuracy and reliability across the two representative attack types.

This paper investigates the vulnerability of bilat-eral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup en-ables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly un-detectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan.

Tactile sensing is pivotal for enhancing robot manipulation abilities by providing crucial feedback for localized information. However, existing sensors often lack the necessary resolution and bandwidth required for intricate tasks. To address this gap, we introduce VibTac, a novel multi-modal tactile sensing finger designed to offer high-resolution and high-bandwidth tactile sensing simultaneously. VibTac seamlessly integrates vision-based and vibration-based tactile sensing modes to achieve high-resolution and high-bandwidth tactile sensing respectively, leveraging a streamlined human-inspired design for versatility in tasks. This paper outlines the key design elements of VibTac and its fabrication methods, highlighting the significance of the Elastomer Gel Pad (EGP) in its sensing mechanism. The sensor's multi-modal performance is validated through 3D reconstruction and spectral analysis to discern tactile stimuli effectively. In experimental trials, VibTac demonstrates its efficacy by achieving over 90% accuracy in insertion tasks involving objects emitting distinct sounds, such as ethernet connectors. Leveraging vision-based tactile sensing for object localization and employing a deep learning model for "click" sound classification, VibTac showcases its robustness in real-world scenarios.

Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE QC and liver stiffness measurement (LSM) using a deep learning (DL) method. In this retrospective, single center, IRB-approved human study, a curated dataset involved 897 MRE magnitude slices from 146 2D MRE scans [1.5 T and 3 T MRI, 2D Gradient Echo (GRE), and 2D Spin Echo-Echo Planar Imaging (SE-EPI)] of 69 patients (37 males, mean age 51.6 years). A SqueezeNet-based binary QC model was trained using combined and individual inputs of MRE magnitude slices and their 2D Fast-Fourier transforms to detect artifacts from patient motion, aliasing, and blurring. Three independent observers labeled MRE magnitude images as 0 (non-diagnostic quality) or 1 (diagnostic quality) to create a reference standard. A 2D U-Net segmentation model was trained on diagnostic slices with liver masks to support LSM. Intersection over union between the predicted segmentation and confidence masks identified measurable areas for LSM on elastograms. Cohen’s unweighted Kappa coefficient, mean LSM error (%), and intra-class correlation coefficient were calculated to compare the DL-assisted approach with the observers’ annotations. An efficiency analysis compared the DL-assisted vs manual LSM durations. The top QC ensemble model (using MRE magnitude alone) achieved accuracy, precision, and recall of 0.958, 0.982, and 0.886, respectively. The mean LSM error between the DL-assisted approach and the reference standard was 1.9% ± 4.6%. DL-assisted approach completed LSM for 29 diagnostic slices in under 1 s, compared to 20 min manually. An automated DL-based classification of liver MRE diagnostic quality, liver segmentation, and LSM approach demonstrates a promising high performance, with potential for clinical adoption.

Cloud-based control is prevalent in many modern control applications. Such applications require security for the sake of data secrecy and system safety. The presented research proposes an encrypted adaptive control framework that can be secured for cloud computing with encryption and without issues caused by encryption overflow and large execution delays. This objective is accomplished by implementing a somewhat homomorphic encryption (SHE) scheme on a modified model reference adaptive controller with accompanying encryption parameter tuning rules. Additionally, this paper proposes a virtual false data injection attack (FDIA) trap based on the SHE scheme. The trap guarantees a probability of attack detection by the adjustment of encryption parameters, thus protecting the system from malicious third parties. The formulated algorithm is then simulated, verifying that after tuning encryption parameters, the encrypted controller produces desired plant outputs while guaranteeing detection or compensation of FDIAs. With the utilization of this novel control framework, adaptively controlled systems will maintain data confidentiality and integrity against malicious adversaries.

This paper demonstrates the fundamental vulnerability of networked linear control systems to perfectly undetectable false data injection attacks (FDIAs) based on affine transformations.The work formulates a generalized FDIA framework that coordinates multiplicative and additive data injections targeting both control commands and observables in networked systems.The paper derives mathematical conditions for executing affine transformation based perfectly undetectable attacks (AT-PAs) on state-feedback and output-feedback control systems, with attack capabilities varying based on the attacker's knowledge of plant dynamics and control gains.The paper examines several attack scenarios, including scaling and general affine transformations, and characterizes the range of system knowledge-from minimum to full-required for different attack types.The paper classifies ATPA into four types based on the feedback structure (state or output) and knowledge requirements: those that match plant dynamics without controller knowledge and those that match closed-loop dynamics by exploiting controller information.The paper examines several attack scenarios, including scaling and general affine transformations, and shows how carefully ATPAs can create the illusion of normal system operation while the actual system behavior deviates significantly from intended trajectories.Through theoretical analysis and examples, the paper demonstrates that these vulnerabilities are inherent to the structure of linear networked control systems and cannot be addressed by traditional model-based or residual-based detection methods alone.

This paper investigates the vulnerability of bilateral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup enables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly undetectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan.

This letter demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users. The attacker can implement these communication line attacks by satisfying three Conditions presented in this work. These claims are experimentally validated on a FANUC 6<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> of freedom manipulator by comparing a nominal (non-attacked) trial and a detectable attack case against three perfectly undetectable trajectory attack Scenarios: scaling, reflection, and shearing. The results show similar observed end effector error for the attack Scenarios and the nominal case, indicating that the perfectly undetectable affine transformation attack method keeps the attacker perfectly hidden while enabling them to attack manipulator trajectories.

Cloud-based control is prevalent in many modern control applications. Such applications require security for the sake of data secrecy and system safety. The presented research proposes an encrypted adaptive control framework that can be secured for cloud computing with encryption and without issues caused by encryption overflow and large execution delays. This objective is accomplished by implementing a somewhat homomorphic encryption (SHE) scheme on a modified model reference adaptive controller with accompanying encryption parameter tuning rules. Additionally, this paper proposes a virtual false data injection attack (FDIA) trap based on the SHE scheme. The trap guarantees a probability of attack detection by the adjustment of encryption parameters, thus protecting the system from malicious third parties. The formulated algorithm is then simulated, verifying that after tuning encryption parameters, the encrypted controller produces desired plant outputs while guaranteeing detection or compensation of FDIAs.

With the increasing integration of cyber-physical systems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade system performance while remaining undetected. This paper investigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a non-holonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the partially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are validated through experiments using a Turtlebot 3 platform, highlighting the urgent need for sophisticated detection mechanisms and resilient control strategies to safeguard CPS against such threats. Furthermore, a novel approach for detection of these attacks called the state monitoring signature function (SMSF) is introduced. An example SMSF, a carefully designed function resilient to FDIA, is shown to be able to detect the presence of a FDIA through signatures based on systems states.

Abstract Magnetic Resonance Elastography (MRE) is an imaging technique capable of quantifying the stiffness of in vivo tissue by applying and imaging shear waves produced by an MRE actuator. Poor image acquisition may result from the MRE procedure if there is insufficient contact between the MRE actuator and the patient. An experimental test setup outside of the clinic will aid in reducing the number of failed acquisitions by enabling the development of advanced actuators and actuator systems. This work presents the development and testing of a sensor-embedded tissue phantom setup paired with a support vector machine (SVM) classifier to automate the MRE actuator testing process. MRE actuation of soft tissue is simulated by utilizing a voice coil positioning stage that interfaces with a phantom. To capture the resulting vibrations, accelerometers are embedded inside the phantom. Subsequent characterization experiments verify the functionality of the developed phantoms to capture wave propagation. A secondary investigation was performed by utilizing the developed setup to collect acceleration measurements at varying contact distances. We provide an overview of feature analysis and selection to develop SVM models for contact detection. Multiple SVM models are reported, and the best-performing model displayed almost perfect validation (94.53%) and test (90.91%) accuracy. The pairing of sensor-embedded phantom with an SVM for detection demonstrates potential improvements to the MRE actuator developmental process by automatically assessing contact-related issues prior to clinical testing.

Magnetic Resonance Elastography (MRE) is a phase-contrast imaging technique that allows for determination of mechanical properties of tissue in-vivo. Due to physiological and morphological changes leading to changes in tissue mechanical properties, MRE may be a promising imaging tool for detection of intervertebral disc degeneration. We therefore performed a preliminary study to determine the frequency dependent mechanical properties of the lumbar intervertebral discs. Six healthy volunteers underwent multifrequency MRE (50, 80, and 100 Hz) to measure the mechanical properties of the intervertebral discs between the L3 and L4, and L4 and L5 vertebrae. Frequency-independent disc mechanical properties and best-fit mechanical model were determined from the frequency-dependent disc data by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). A seventh individual with a history of a discectomy on the disc between the L4 and L5 vertebrae was also scanned to provide a preliminary analysis about how degeneration impacts disc mechanical properties. Our findings show that the Zener model may best represent the disc's frequency-dependent mechanical response. Additionally, we observed a significantly lower complex shear modulus in the degenerated disc than the healthy discs at each frequency, demonstrating the potential for MRE to detect early signs of degeneration and pinpoint the cause of chronic back pain.

BACKGROUND: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors. PURPOSE: To report initial results on automatic classification of liver MRE image quality using various deep learning (DL) architectures. STUDY TYPE: Retrospective, single center, IRB-approved human study. POPULATION: Ninety patients (male = 51, mean age 52.8 ± 14.1 years). FIELD STRENGTHS/SEQUENCES: 1.5 T and 3 T MRI, 2D GRE, and 2D SE-EPI. ASSESSMENT: The curated dataset was comprised of 914 slices obtained from 149 MRE exams in 90 patients. Two independent observers examined the confidence map overlaid elastograms (CMOEs) for liver stiffness measurement and assigned a quality score (non-diagnostic vs. diagnostic) for each slice. Several DL architectures (ResNet18, ResNet34, ResNet50, SqueezeNet, and MobileNetV2) for binary quality classification of individual CMOE slice inputs were evaluated, using an 8-fold stratified cross-validation (800 slices) and a test dataset (114 slices). A majority vote ensemble combining the models' predictions of the highest-performing architecture was evaluated. STATISTICAL TEST: The inter-observer agreement and the agreement between DL models and one observer were assessed using Cohen's unweighted Kappa coefficient. Accuracy, precision, and recall of the cross-validation and the ensemble were calculated for the test dataset. RESULTS: The average accuracy across the eight models trained using each architecture ranged from 0.692 to 0.851 for the test dataset. The ensemble of the best performing architecture (SqueezeNet) yielded an accuracy of 0.921. The inter-observer agreement was excellent (Kappa 0.896 [95% CI 0.845-0.947]). The agreement between observer 1 and the predictions of each SqueezeNet model was slight to almost perfect (Kappa range: 0.197-0.831) and almost perfect for the ensemble (Kappa: 0.833). CONCLUSION: Our initial study demonstrates an automated DL-based approach for classifying liver 2D MRE diagnostic quality with an average accuracy of 0.851 (range 0.675-0.921) across the SqueezeNet models. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

This paper demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users. The attacker can implement these communication line attacks by satisfying three Conditions presented in this work. These claims are experimentally validated on a FANUC 6 degree of freedom manipulator by comparing a nominal (non-attacked) trial and a detectable attack case against three perfectly undetectable trajectory attack Scenarios: scaling, reflection, and shearing. The results show similar observed end effector error for the attack Scenarios and the nominal case, indicating that the perfectly undetectable affine transformation attack method keeps the attacker perfectly hidden while enabling them to attack manipulator trajectories.

This paper presents an investigation into the improvement of security and operation time in homomorphically encrypted systems using Field Programmable Gate Array (FPGA) technology. The primary objective is to generate keys efficiently, minimizing key sizes while maintaining security. By leveraging FPGA capabilities for key generation and key switching, smaller ciphertext sizes can be achieved, ultimately improving operation time. The paper focuses on the development of a sensor data encryption system implemented on an FPGA board. The proposed approach enables simultaneous key generation and encryption of incoming sensor data using generated keys. The developed system implemented fixed-size random number generation and prime number checking in hardware, subsequently expanding these capabilities to produce arbitrarily sized prime numbers.

Magnetic Resonance Imaging (MRI) is considered a safe imaging modality since there is no use of ionizing radiation. However, safety concerns still arise due to Radiofrequency (RF)-induced heating of electrically conducting structures such as medical implants. While recent advancements in robotics and sensors have enabled the measurement of temperature and electric fields outside the MRI setting, the heat generated by electromagnetic components within an MRI scanner still poses a challenge. This paper proposes the use of an MRI-compatible robot to accurately move and position a novel MRI-compatible sensor at different points in a gel phantom to generate heat and electric field maps around implantable medical devices. The effectiveness of the system is demonstrated by measuring a heat map around an abandoned pacemaker lead. The system provides a novel method of medical device safety evaluation in a clinical MRI setting.

This paper seeks to understand the viability of encrypted robot control. Controllers are susceptible to malicious attacks unless controller parameters are encrypted; however, homomorphic encryption is necessary in order to allow controller mathematical operations on encrypted text, but is limited due to heavy computational overhead. Encrypted control is accomplished via the implementation of Dyer’s somewhat homomorphic encryption scheme on multi and single threaded matrix transformations in order to telecommunicate movement commands between a virtual-reality joystick and a robot arm. Results find that encrypted teleoperation via the user interface is a viable encrypted controller technique, and is optimally produced on multi-threaded systems.

This paper investigates the design of optimal inputs for dynamic system identification. Specifically, this paper concerns the perturbation design for system identification experiments where target human systems are perturbed by mechanical inputs produced by an active device. Although conventional perturbation design criteria are generally applicable, including the scenario described above, problems arise due to the dynamics of the active device. A low-bandwidth active device may distort the input signal and thereby void the optimality of the input. To address this issue, the paper formulates an optimization problem for optimal input design that explicitly incorporates the active device dynamics. The cost function is the determinant of a modified covariance lower bound that takes the active device dynamics into consideration. The proposed method is demonstrated with an identification of a linear dynamics model simulating human arm impedance. Simulation results show that, compared with a standard optimal input and an input with a flat spectrum, the proposed optimal input with active device compensation achieved a smaller parameter covariance. Furthermore, the proposed optimization problem suggests that the optimal covariance lower bound can be achieved by active devices with different dynamics properties. This allows the control design of the active device to satisfy a wide variety of requirements without sacrificing its ability to perform system identification.

Developments in sensing and analysis methods have significantly increased the scope of physiological monitoring for healthcare purposes. While the continuous monitoring of physiological measurements enables improved detection and management of many illnesses, accompanying cybersecurity concerns continue to evolve. The large amounts of individualized data necessary to enable learned models for analysis must be sufficiently protected. In addition, the analysis and classification methods themselves should not be vulnerable to attack. This work addresses adversarial individual identification with multiple forms of physiological data, as well as potential performance interruption attacks. The paper proposes a homomorphic encryption scheme to mitigate both of these threats.

Multi-actuator magnetic resonance elastography (MRE) has previously been studied for overcoming wave attenuation and generating uniform displacements throughout a targeted imaging region. While the actuators’ locations, relative phase offsets, and angles are known to influence the generated displacements, their effects are dependent on the geometry and properties of the specific target. Experimental optimization of these MRE parameters can be performed but is time-consuming. Alternatively, finite element analysis (FEA) is used for three-dimensional model-specific characterization of displacement fields induced by MRE mechanical excitation loads across varying actuator locations. Cubic, tissue-like homogeneous and heterogeneous models were created and loads were applied to simulate single actuator and multi-actuator cases. Multi-actuator cases were phase-matched to promote constructive interference of the induced waves. An additional investigation was performed by repeating a single multi-actuator configuration with various loading angles in the heterogeneous model. The mean displacement amplitudes and the corresponding standard deviations throughout the imaging target volumes are compared across the multiple configurations. Wave images of selected configurations are presented for comparison. Multi-actuator configurations induced the greatest mean z displacement amplitudes within the imaging target of both models. To further increase the z displacement, the excitation loads can be angled towards the imaging target. The differences in simulated displacement fields demonstrate the potential for future automated parameter optimization for closed-loop MRE driver positioning using more complex FEA models.

Mechanical stimulation-conditioned transcranial magnetic stimulation (Mstim-cTMS) is a neuromodulation technique that transiently enhances neural excitability through paired peripheral and cortical stimulation. Importantly, the temporal inter-stimulus interval (ISI) must fall within an individual’s specific ISI window (ISI-W) to induce neuromodulatory effects. Currently, ISI-W identification is inefficient, incrementally applying Mstim-cTMS within a wide search range. This paper aims to test a real-time, closed-loop framework that decreases the number of trials required to obtain individualized ISI-Ws by implementing a previously developed statistical regression algorithm. Each Mstim-cTMS trial consisted of peripheral mechanical stimulation using a precise, tapping robot paired with transcranial magnetic stimulation to the motor cortex. Neuromodulation was characterized by measuring resulting motor evoked potentials (MEPs). The closed-loop process estimated the effective ISI-W by iterating Mstim-cTMS at statistically determined ISIs based on the detected MEPs. The estimated ISI-Ws of two subjects had an average correlation coefficient of 0.84 with their reference windows obtained using the conventional search method. The estimation reduced the number of required Mstim-cTMS trials by an average of 93.8%. The results indicate the proposed method’s potential for decreasing Mstim-cTMS startup durations. Future rehabilitation and neurophysiological studies can benefit from efficiently updating ISI-Ws to account for long-term neural variability.

The research presented in this paper aims to establish functional mockup units (FMU) co-simulation methods to simulate and evaluate encrypted dynamic systems using some-what homomorphic encryption (SHE). The proposed approach encrypts the entire dynamic system expressions, including: model parameters, state variables, feedback gains, and sensor signals, and perform computation in the ciphertext space to simulate dynamic behaviors or generate motion commands to servo systems. The developed FMU co-simulation helps analyze the relationship between security parameters and performance. Two illustrative examples are presented and analyzed: 1) encrypted Duffing oscillator and 2) encrypted teleoperation. How the time delay due to FMU co-simulation affects the refresh rate is also reported.

A large number of robots have been developed for image-guided interventions, with increased precision and improved control methods enabling procedures involving complicated and sensitive anatomical targets such as the spinal cord. While image-guided controllers are capable of accurate positioning at, or even beyond, the resolution of the chosen imaging device, targets for interventions are often manually selected from the available imaging data. This work involves the development of an intuitive and immersive virtual reality tool for detecting and visualizing scanner image data during the operation of a precision MRI-guided robot. This tool is integrated into the workflow of the image guidance protocol. Automated surgical freedom analysis is performed with the intention of further augmenting the trajectory selection process. A user study gauges both expert and novice reactions to the system.