Sarthak Misra

Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity.

Mechanics of Flexible Needles Robotically Steered through Soft Tissue

The tip asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. This enables robotic needle steering, which can be used in medical procedures to reach subsurface targets inaccessible by straight-line trajectories. However, accurate path planning and control of needle steering require models of needle-tissue interaction. Previous kinematic models required empirical observations of each needle and tissue combination in order to fit model parameters. This study describes a mechanics-based model of robotic needle steering, which can be used to predict needle behavior and optimize system design based on fundamental mechanical and geometrical properties of the needle and tissue. We first present an analytical model for the loads developed at the tip, based on the geometry of the bevel edge and material properties of soft-tissue simulants (gels). We then present a mechanics-based model that calculates the deflection of a bevel-tipped needle inserted through a soft elastic medium. The model design is guided by microscopic observations of needle-gel interactions. The energy-based formulation incorporates tissue-specific parameters, and the geometry and material properties of the needle. Simulation results follow similar trends (deflection and radius of curvature) to those observed in experimental studies of robotic needle insertion.

Bilateral Telemanipulation With Time Delays: A Two-Layer Approach Combining Passivity and Transparency

In this paper, a two-layer approach is presented to guarantee the stable behavior of bilateral telemanipulation systems in the presence of time-varying destabilizing factors such as hard contacts, relaxed user grasps, stiff control settings, and/or communication delays. The approach splits the control architecture into two separate layers. The hierarchical top layer is used to implement a strategy that addresses the desired transparency, and the lower layer ensures that no “virtual” energy is generated. This means that any bilateral controller can be implemented in a passive manner. Separate communication channels connect the layers at the slave and master sides so that information related to exchanged energy is completely separated from information about the desired behavior. Furthermore, the proposed implementation does not depend on any type of assumption about the time delay in the communication channel. By complete separation of the properties of passivity and transparency, each layer can accommodate any number of different implementations that allow for almost independent optimization. Experimental results are presented, which highlight the benefit of the proposed framework.

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

Needle insertion is commonly performed in minimally invasive medical procedures such as biopsy and radiation cancer treatment. During such procedures, accurate needle tip placement is critical for correct diagnosis or successful treatment. Accurate placement of the needle tip inside tissue is challenging, especially when the target moves and anatomical obstacles must be avoided. We develop a needle steering system capable of autonomously and accurately guiding a steerable needle using two-dimensional (2D) ultrasound images. The needle is steered to a moving target while avoiding moving obstacles in a three-dimensional (3D) non-static environment. Using a 2D ultrasound imaging device, our system accurately tracks the needle tip motion in 3D space in order to estimate the tip pose. The needle tip pose is used by a rapidly exploring random tree-based motion planner to compute a feasible needle path to the target. The motion planner is sufficiently fast such that replanning can be performed repeatedly in a closed-loop manner. This enables the system to correct for perturbations in needle motion, and movement in obstacle and target locations. Our needle steering experiments in a soft-tissue phantom achieves maximum targeting errors of 0.86 ± 0.35 mm (without obstacles) and 2.16 ± 0.88 mm (with a moving obstacle).

Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors

We present a prototype of a flexible nitinol needle (φ 1.0 mm and length 172 mm) integrated with an array of 12 Fiber Bragg Grating (FBG) sensors. These sensors measure the axial strain, which enables the computation of the needle curvature. We reconstruct the three-dimensional (3-D) needle shape from the curvature. Experiments are performed where the needle is deflected in free space. The maximum errors between the experiments and beam theory-based model are 0.20 mm (in-plane deflection with single bend), 0.51 mm (in-plane deflection with double bend), and 1.66 mm (out-of-plane). We also describe kinematics-based and mechanics-based models for predicting the 3-D needle shape during insertion into soft tissue. We perform experiments where the needle is inserted into a soft-tissue simulant, and the 3-D needle shape is reconstructed using the FBG sensors. We compare the reconstructed needle shape to deflection obtained from camera images and our models. The maximum error between the experiments and the camera images is 0.74 mm. The maximum errors between the kinematics-based and mechanics-based models and the camera images are 3.77 mm and 2.20 mm, respectively. This study demonstrates that deflection models and needles integrated with FBG sensors have the potential to be used in combination with clinical imaging modalities in order to enable accurate needle steering.

MagnetoSperm: A microrobot that navigates using weak magnetic fields

In this work, a propulsion system similar in motion to a sperm-cell is investigated. This system consists of a structure resembling a sperm-cell with a magnetic head and a flexible tail of 42 μm and 280 μm in length, respectively. The thickness, length, and width of this structure are 5.2 μm, 322 μm, and 42 μm, respectively. The magnetic head includes a 200 nm-thick cobalt-nickel layer. The cobalt-nickel layer provides a dipole moment and allows the flexible structure to align along oscillating weak (less than 5mT) magnetic field lines, and hence generates a propulsion thrust force that overcomes the drag force. The frequency response of this system shows that the propulsion mechanism allows for swimming at an average speed of 158 ± 32 μm/s at alternating weak magnetic field of 45 Hz. In addition, we experimentally demonstrate controlled steering of the flexible structure towards reference positions.

Robotic Needle Steering: Design, Modeling, Planning, and Image Guidance

This chapter describes how advances in needle design, modeling, planning, and image guidance make it possible to steer flexible needles from outside the body to reach specified anatomical targets not accessible using traditional needle insertion methods. Steering can be achieved using a variety of mechanisms, including tip-based steering, lateral manipulation, and applying forces to the tissue as the needle is inserted. Models of these steering mechanisms can predict needle trajectory based on steering commands, motivating new preoperative path planning algorithms. These planning algorithms can be integrated with emerging needle imaging technology to achieve intraoperative closed-loop guidance and control of steerable needles.

Precise Localization and Control of Catalytic Janus Micromotors using Weak Magnetic Fields

We experimentally demonstrate the precise localization of spherical Pt-Silica Janus micromotors (diameter 5 μm) under the influence of controlled magnetic fields. First, we control the motion of the Janus micromotors in two-dimensional (2D) space. The control system achieves precise localization within an average region-of-convergence of 7 μm. Second, we show that these micromotors provide sufficient propulsion force, allowing them to overcome drag and gravitational forces and move both downwards and upwards. This propulsion is studied by moving the micromotors in three-dimensional (3D) space. The micromotors move downwards and upwards at average speeds of 19.1 μm/s and 9.8 μm/s, respectively. Moreover, our closed-loop control system achieves localization in 3D space within an average region-of-convergence of 6.3 μm in diameter. The precise motion control and localization of the Janus micromotors in 2D and 3D spaces provides broad possibilities for nanotechnology applications.

3D flexible needle steering in soft-tissue phantoms using Fiber Bragg Grating sensors

Needle insertion procedures are commonly used for surgical interventions. In this paper, we develop a three-dimensional (3D) closed-loop control algorithm to robotically steer flexible needles with an asymmetric tip towards a target in a soft-tissue phantom. Twelve Fiber Bragg Grating (FBG) sensors are embedded on the needle shaft. FBG sensors measure the strain applied on the needle during insertion. A method is developed to reconstruct the needle shape using the strain data obtained from the FBG sensors. Four experimental cases are conducted to validate the reconstruction method (single-bend, double-bend, 3D double-bend and drilling insertions). In the experiments, the needle is inserted 120 mm into a soft-tissue phantom. Camera images are used as a reference for the reconstruction experiments. The results show that the mean needle tip accuracy of the reconstruction method is 1.8 mm. The reconstructed needle shape is used as feedback for the steering algorithm. The steering algorithm estimates the region that the needle can reach during insertion, and controls the needle to keep the target in this region. Steering experiments are performed for 110 mm insertion, and the mean targeting accuracy is 1.3 mm. The results demonstrate the capability of using FBG sensors to robotically steer needles.

Mechanics of needle-tissue interaction

When a needle is inserted into soft tissue, interaction forces are developed at the needle tip and along the needle shaft. The needle tip force is due to cutting of the tissue, and the force along the needle shaft is due to friction between needle and tissue. In this study, the friction force is determined for needles inserted into a gelatine phantom at insertion velocities of 10 mm/s and 20 mm/s. The friction force is found to be dependent on the insertion velocity. The needle tip force is calculated using the friction and insertion force, and is used as input for a mechanics-based model which predicts the amount of needle deflection. In the model, the needle is considered to be a cantilever beam supported by springs which have needle-tissue interaction stiffness (Ke). The value of the interaction stiffness is evaluated by comparing results from experiments and simulation. A mechanical needle insertion device is used to insert needles. Needle deflection during insertion is determined using a needle tip tracking algorithm. Results of this study provide insight into the mechanics of needle-tissue interaction, and can be used in studies for robotically steering needles into soft tissue.