Computer vision-based technologies significantly enhance surgical automation by advancing tool tracking, detection, and localization. However, Current data-driven approaches are data-voracious, requiring large, high-quality labeled image datasets. Our Work introduces a novel dynamic Gaussian Splatting technique to address the data scarcity in surgical image datasets. We propose a dynamic Gaussian model to represent dynamic surgical scenes, enabling the rendering of surgical instruments from unseen viewpoints and deformations with real tissue backgrounds. We utilize a dynamic training adjustment strategy to address challenges posed by poorly calibrated camera poses from real-world scenarios. Additionally, automatically generate annotations for our synthetic data. For evaluation, we constructed a new dataset featuring seven scenes with 14,000 frames of tool and camera motion and tool jaw articulation, with a background of an ex-vivo porcine model. Using this dataset, we synthetically replicate the scene deformation from the ground truth data, allowing direct comparisons of synthetic image quality. Experimental results illustrate that our method generates photo-realistic labeled image datasets with the highest PSNR (29.87). We further evaluate the performance of medical-specific neural networks trained on real and synthetic images using an unseen real-world image dataset. Our results show that the performance of models trained on synthetic images generated by the proposed method outperforms those trained with state-of-the-art standard data augmentation by 10%, leading to an overall improvement in model performances by nearly 15%.

Magnetic manipulation has been adopted as a method of actuation in both wireless capsule endoscopy and soft-tethered endoscopy, with the goal of improving gastrointestinal procedures. However, by nature of magnetic manipulation, these endoscopes are typically limited to a maximum of five degrees of freedom (DoF). With the need to introduce additional contact-based sensing modalities for subsurface investigation into these systems as well as to improve overall dexterity, it is both practically and clinically beneficial to recover the lost DoF i.e. the roll around the main axis. This paper presents a method of achieving the magnetic manipulation of an underactuated device by leveraging developable surfaces, specifically, the oloid shape. The design of a clinically relevant magnetic endoscope with all its ancillary elements, as well as contact sensors, is proposed and demonstrated in vivo. The contact sensor data from the in vivo experiments show that for sweeping motions over 100° of roll, contact between the endoscope’s sensor region and the colon wall can be maintained for 74% of the motion.

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of markerless methods based on deep learning. However, acquiring realistic surgical data, with ground truth (GT) instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation.

Robotic manipulation of 3-D soft objects remains challenging in the industrial and medical fields. Various methods based on mechanical modeling, data-driven approaches or explicit feature tracking have been proposed. A unifying disadvantage of these methods is the high computational cost of simultaneous imaging processing, identification of mechanical properties, and motion planning, leading to a need for less computationally intensive methods. We propose a method for autonomous robotic manipulation with 3-D surface feedback to solve these issues. First, we produce a deformation model of the manipulated object, which estimates the robots' movements by monitoring the displacement of surface points surrounding the manipulators. Then, we develop a 6-degree-of-freedom velocity controller to manipulate the grasped object to achieve a desired shape. We validate our approach through comparative simulations with existing methods and experiments using phantom and cadaveric soft tissues with theda Vinciresearch kit. The results demonstrate the robustness of the technique to occlusions and various materials. Compared to state-of-the-art linear and data-driven methods, our approach is more precise by 46.5% and 15.9% and saves 55.2% and 25.7% manipulation time, respectively.

The calibration of a vision-guided dual-arm robotic system, including the robot-robot and hand-eye calibration, requires the tracked positions of markers in different postures. However, in many cases, using markers to calibrate is impractical. Only some markerless features can be obtained rather than the rigid transform matrix; for example, the shaft of a markerless robotic tool can be tracked. Therefore, we proposed a Kronecker-Product-based method to calibrate the dual-arm system with a tracked robotic tool by decoupling the translation and rotation. The simulation and experiment results on a da Vinci Research Kit show that the proposed method is robust and accurate under different noise levels and various sample robot movements, compared with two state-of-the-art methods for dual-arm calibration with complete homogeneous transformations.

We propose an endoscopic image mosaicking algorithm that is robust to light conditioning changes, specular reflections, and feature-less scenes. These conditions are especially common in minimally invasive surgery where the light source moves with the camera to dynamically illuminate close range scenes. This makes it difficult for a single image registration method to robustly track camera motion and then generate consistent mosaics of the expanded surgical scene across different and heterogeneous environments. Instead of relying on one specialised feature extractor or image registration method, we propose to fuse different image registration algorithms according to their uncertainties, formulating the problem as affine pose graph optimisation. This allows to combine landmarks, dense intensity registration, and learning-based approaches in a single framework. To demonstrate our application we consider deep learning-based optical flow, hand-crafted features, and intensity-based registration, however, the framework is general and could take as input other sources of motion estimation, including other sensor modalities. We validate the performance of our approach on three datasets with very different characteristics to highlighting its generalisability, demonstrating the advantages of our proposed fusion framework. While each individual registration algorithm eventually fails drastically on certain surgical scenes, the fusion approach flexibly determines which algorithms to use and in which proportion to more robustly obtain consistent mosaics.

Magnetically actuated robots have proven effective in several applications, specifically in medicine. However, generating high actuating fields with a high degree of manipulability is still a challenge, especially when the application needs a large workspace to suitably cover a patient. The presented work discusses a novel approach for the control of magnetic field and field gradients using two robotically actuated permanent magnets. In this case, permanent magnets—relative to coil-based systems—have the advantage of larger field density without energy consumption. We demonstrate that collaborative manipulation of the two permanent magnets can introduce up to three additional Degrees of Freedom (DOFs) when compared to single permanent magnet approaches (five DOFs). We characterized the dual-arm system through the measurement of the fields and gradients and show accurate open-loop control with a 13.5% mean error. We then demonstrate how the magnetic DOFs can be employed in magnetomechanical manipulation, by controlling and measuring the wrench on two orthogonal magnets within the workspace, observing a maximum crosstalk of 6.1% and a mean error of 11.1%.

Magnetically actuated soft robots have recently been identified for application in medicine, due to their potential to perform minimally invasive exploration of human cavities. Magnetic solutions permit further miniaturization when compared to other actuation techniques, without loss in functionalities. Our long-term goal is to propose a novel actuation method for magnetically actuated soft robots, based on dual-arm collaborative magnetic manipulation. A fundamental step in this direction is to show that this actuation method is capable of controlling up to 8 coincident, independent Degrees of Freedom (DOFs). In present paper, we prove this concept by measuring the independent wrench components on a second pair of static permanent magnets, by means of a high resolution 6-axis load cell. The experiments show dominant activation of the desired DOFs, with mean cross-activation error of the undesired DOFs ranging from 2% to 10%.

The navigation of magnetic medical robots typically relies on localizing an actuated, intracorporeal, ferromagnetic body and back-computing a necessary field and gradient that would result in a desired wrench on the device. Uncertainty in this localization degrades the precision of force transmission. Reducing applied force uncertainty may enhance tasks such as in vivo navigation of miniature robots, actuation of magnetically guided catheters, tissue palpation, as well as simply ensuring a bound on forces applied on sensitive tissue. In this paper, we analyze the effects of localization noise on force uncertainty by using sensitivity ellipsoids of the magnetic force Jacobian and introduce an algorithm for uncertainty reduction. We validate the algorithm in both a simulation study and in a physical experiment. In simulation, we observe reductions in estimated force uncertainty by factors of up to 2.8 and 3.1 when using one and two actuating magnets, respectively. On a physical platform, we demonstrate a force uncertainty reduction by a factor of up to 2.5 as measured using an external sensor. Being the first consideration of force uncertainty resulting from noisy localization, this paper provides a strategy for investigators to minimize uncertainty in magnetic force transmission.

Manual palpation is commonly used to localize tumors and other features buried deep inside organs during open surgery. This approach is not feasible in minimally invasive or robotic surgery, as the contact with the tissue is mediated by instruments. To address this problem, we propose a soft robotic skin (SRS) that can be deployed from a small incision and create a stiffness map in a single step. Such a skin is composed of a matrix of soft robotic tactile elements (SRTEs), each one able to expand and record the tissue response during expansion. In this paper, we firstly prove the feasibility of palpation using a single SRTE. Then, we present and test a soft-suction based anchoring mechanism able to keep the SRS in the desired position in contact with the tissue, allowing surgeons to palpate different sides of the organ. Finally, we detail a calibration method for the SRTE, and assess the feasibility of identifying lumps buried inside a soft tissue phantom, and then inside a chicken liver during an ex-vivo trial. Experimental results show that the SRTE was able to differentiate simulated lumps (up to 3.25 mm deep) from healthy tissue in both the phantom and the ex-vivo trials. These results, added to the ability of the suction gripper to compensate for the expansion forces of each SRTE, are paving the way for soft robotic autonomous tools that can be used for intraoperative mapping of tissue cancers.

This paper aims to characterise the magnetic interaction in neighbouring sets of local electromagnetic actuation (LEMA) actuators in a robotic platform for abdominal surgery. The analysis looks into the affect of the magnetic fields contributed by a stator-rotor set (the actuation unit) located adjacent to the rotor of interest. Each rotor drives one of the degree-of-freedoms (DOFs) on a surgical robotic device. In this study, a two-DOF setup is used for the magnetic interaction analysis, which can be expanded to general case n-DOF setup with the Principle of Superposition of magnetic fields from multiple sources. The magnetic model is then used to compute the dynamics of the system, which involves the equation of motion of the rotors and associated robotic mechanism it drives, and the actuator (electrical) model that takes into account the back EMF generated by the permanent magnet rotors. The magnetic field effect of the neighbouring set onto the rotor is observed by obtaining the speed response of the rotor through simulation so that the dynamic model can be validated against the experimental results. The outcomes are useful for the design specification of the LEMA system configuration, involving the feasible / pragmatic distance between the stator sets such that the interference is minimised, and for the design of the necessary control strategy.

In this paper, we demonstrate velocity-level closed-loop control of a tethered magnetic capsule endoscope that is actuated via serial manipulator with a permanent magnet at its end-effector. Closed-loop control (2 degrees-of-freedom in position, and 2 in orientation) is made possible with the use of a real-time magnetic localization algorithm that utilizes the actuating magnetic field and thus does not require additional hardware. Velocity control is implemented to create smooth motion that is clinically necessary for colorectal cancer diagnostics. Our control algorithm generates a spline that passes through a set of input points that roughly defines the shape of the desired trajectory. The velocity controller acts in the tangential direction to the path, while a secondary position controller enforces a nonholonomic constraint on capsule motion. A soft nonholonomic constraint is naturally imposed by the lumen while we enforce a strict constraint for both more accurate estimation of tether disturbance and hypothesized intuitiveness for a clinician's teleoperation. An integrating disturbance force estimation control term is introduced to predict the disturbance of the tether. This paper presents the theoretical formulations and experimental validation of our methodology. Results show the system's ability to achieve a repeatable velocity step response with low steady-state error as well as ability of the tethered capsule to maneuver around a bend.

The purpose of this study is to validate a Jacobian-based iterative method for real-time localization of magnetically controlled endoscopic capsules. The proposed approach applies finite-element solutions to the magnetic field problem and least-squares interpolations to obtain closed-form and fast estimates of the magnetic field. By defining a closed-form expression for the Jacobian of the magnetic field relative to changes in the capsule pose, we are able to obtain an iterative localization at a faster computational time when compared with prior works, without suffering from the inaccuracies stemming from dipole assumptions. This new algorithm can be used in conjunction with an absolute localization technique that provides initialization values at a slower refresh rate. The proposed approach was assessed via simulation and experimental trials, adopting a wireless capsule equipped with a permanent magnet, six magnetic field sensors, and an inertial measurement unit. The overall refresh rate, including sensor data acquisition and wireless communication was 7 ms, thus enabling closed-loop control strategies for magnetic manipulation running faster than 100 Hz. The average localization error, expressed in cylindrical coordinates was below 7 mm in both the radial and axial components and 5$^\circ$ in the azimuthal component. The average error for the capsule orientation angles, obtained by fusing gyroscope and inclinometer measurements, was below 5$^\circ$.

Minimally invasive robotic surgery techniques are becoming popular thanks to their enhanced patient benefits, including shorter recovery time, better cosmetic results and reduced discomforts. Less invasive procedures would be achieved with the use of Medical Capsule Robots (MCRs). These devices are characterized by low power requirements and small dimensions as well as uncompromising safety. MCRs operate wirelessly in abdominal Minimally Invasive Surgery (MIS) and Natural Orifice Transluminal Endoscopic Surgery (NOTES) or in the Gastrointestinal (GI) tract. The design process of MCRs, however, is expensive and time consuming. A platform for rapid prototyping MCRs is needed so that MCR researchers can reduce development costs and spend more time in studying innovative MCR applications. In this work, we introduce an open source modular platform geared toward rapid prototyping MCRs. To speed up the prototyping process, the MCR is programmed using TinyOS instead of bare-bone C. We present the hardware architecture of the platform, and the motivation for using TinyOS. To show the viability of TinyOS, we present results from an experiment involving sensing, actuation and wireless communication. This work lays the foundation for our future goal of building an integrated design environment for the design, analysis and simulation of MCRs.

Independent mobility is an important aspect of an individual's life and must sometimes be augmented by use of an assistive device such as a wheeled walker or cane following a fall, injury, or functional decline. Physical therapists perform functional gait assessments to gauge the probability of an individual experiencing a fall and often recommend use of a walker, cane, or walking stick to decrease fall risk. Our team has developed a clinical assessment tool centered on a standard walking cane embedded system that can enhance a therapist's observation-based gait assessment with use of additional objective and quantitative data. This system can be utilized to detect timing and speed of cane placement, angular acceleration of the cane, and amounts of weight borne on the cane. This system is designed to assist physical therapists at the basic level in collection of objective data during gait analysis, to facilitate appropriate assistive gait device prescription, to provide patients and therapists feedback during gait training, and to reduce wrist and shoulder injuries with cane usage. However, more importantly, using the plethora of objective data that can be obtained from this cane, automated gait analysis and gait pattern classification can be performed to understand a patient's walking performance.

We propose local magnetic actuation (LMA) as an approach to robotic actuation for surgical instruments. An LMA actuation unit consists of a pair of diametrically magnetized single-dipole cylindrical magnets, working as magnetic gears across the abdominal wall. In this study, we developed a dynamic model for an LMA actuation unit by extending the theory proposed for coaxial magnetic gears. The dynamic model was used for closed-loop control, and two alternative strategies-using either the angular velocity at the motor or at the load as feedback parameter-were compared. The amount of mechanical power that can be transferred across the abdominal wall at different intermagnetic distances was also investigated. The proposed dynamic model presented a relative error below 7.5% in estimating the load torque from the system parameters. Both the strategies proposed for closed-loop control were effective in regulating the load speed with a relative error below 2% of the desired steady-state value. However, the load-side closed-loop control approach was more precise and allowed the system to transmit larger values of torque, showing, at the same time, less dependence from the angular velocity. In particular, an average value of 1.5 mN·m can be transferred at 7 cm, increasing up to 13.5 mN·m as the separation distance is reduced down to 2 cm. Given the constraints in diameter and volume for a surgical instrument, the proposed approach allows for transferring a larger amount of mechanical power than what would be possible to achieve by embedding commercial dc motors.

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to promote participation in these preventive screenings. Real-time pose estimation is therefore essential to enable feedback to the robotic endoscope's control system. Vision-based endoscope localization approaches are a promising avenue, since they do not require extra sensors on board the endoscopes. In this work, we compare several state-of-the-art algorithms for computing the image motion (optical flow), which is then used with a supervised learning strategy to provide an accurate estimate of the 6 degree of freedom endoscope motion. The method is validated using a robotically actuated endoscope in a human colon simulator, and represents a preliminary effort towards testing with clinical video data.

Minimally invasive surgical techniques are becoming popular due to their enhanced patient benefits. Less invasive procedures can be achieved with the use of wireless Medical Capsule Robots (MCRs). MCRs are low powered and small in size and can be used for physiological parameter monitoring, therapy delivery, and biopsy sampling. Designing MCRs from the ground up is a costly and time consuming process. In this work, we present a flexible modular architecture to facilitate the design of MCRs and propose using TinyOS as the operating system. To assess the architecture and validate the feasibility of TinyOS, we implement a closed-loop control of a sensor-actuator system and compare the results with a traditional MCR built based on an 8051 microcontroller (MCU) programmed in plain C. Similar performances from the two approaches lead us to conclude that TinyOS is a valid option to implement a modular architecture for designing MCRs.

Palpating tissues and organs to identify hidden tumors or to detect buried vessels is not a viable option in laparoscopic surgery due to lack of force feedback. So far, research toward restoring tactile and kinesthetic sensations in minimally invasive surgery has focused on the distal sensing element or on the proximal rendering of haptic cues. In this work we present a pilot study to assess the feasibility of wireless tissue palpation, where a magnetic device is deployed through a standard surgical trocar and operated to perform tissue palpation without requiring a dedicated entry port. The setup consists of a wireless intra-body device and an external robotic manipulator holding a load cell and a permanent magnet. Embedded in the wireless cylindrical device (12.7 mm in diameter and 27.5 mm in height) is a sensing module, a wireless microcontroller, a battery and a permanent magnet. This preliminary study assessed the precision in reconstructing the indentation depth based on magnetic field measurements at the wireless device (i.e., 0.1 mm accuracy). Experimental trials demonstrated the effectiveness of wireless vertical indentation in detecting the elastic modulus of three different silicone tissue simulators (elastic modulus ranging from 50 kPa to 93 kPa), showing a maximum relative error below 3%. Finally, wireless palpation was used to identify differences in tissue stiffness from a lump embedded into a porcine liver. The reported results have the potential to open a new paradigm in the field of palpation devices, where direct physical connection across the abdominal wall is no longer required.

The novel concept of Trans-abdominal Active Magnetic Linkage for laparoendoscopic single site surgery has the potential to enable the deployment of a bimanual robotic platform trough a single laparoscopic incision. The main advantage of this approach consists in shifting the actuators outside the body of the patient, while transmitting a controlled robotic motion by magnetic field across the abdomen without the need for dedicated incisions. An actuation mechanism based on this approach can be comprised of multiple anchoring and actuation units, mixed depending upon the specific needs. A static model providing anchoring and actuation forces and torques available at the internal side of the magnetic link was developed to provide a tool to navigate among the many possibilities of such an open ended design approach. The model was assessed through bench top experiments, showing a maximum relative error of 4% on force predictions. An example of a single degree of freedom manipulator actuated with the proposed concept and compatible with a 12-mm access port is able to provide an anchoring force of 3.82 N and an actuation force of 2.95 N.

Wireless capsule endoscopy offers significant advantages compared with traditional endoscopic procedures, since it limits the invasiveness of gastrointestinal tract screening and diagnosis. Moreover, active locomotion devices would allow endoscopy to be performed in a totally controlled manner, avoiding failures in the correct visualization of pathologies. Previous works demonstrated that magnetic locomotion through a robotic-aided platform would allow us to reach this goal reliably. In this paper, the authors present a comparative evaluation of control methodologies and user interfaces for a robotic-aided magnetic platform for capsule endoscopy, controlled through human-robot cooperative and teleoperated control algorithms. A detailed statistical analysis of significant control parameters was performed: teleoperated control is the more reliable control approach, and a serial kinematic haptic device results as the most suitable control interface to perform effective robotic-aided endoscopic procedures.

A wired miniature surgical camera robot with a novel Magnetic Levitation System (MLS) was modeled, designed and fabricated. A simple analysis and a theoretical model were developed in order to describe and predict basic behavior for different structural parameters of the system. The robot is composed of two main parts (head and tail) linked by a thin elastic flexible joint. The tail module embeds two magnets for anchoring and manual rough translation. The head module incorporates two motorized donut-shaped magnets and a miniaturized vision system at the tip. The MLS can exploit the external magnetic field to induce a smooth bending of the robotic head, guaranteeing a high span tilt motion of the point of view (0°-80°). The device is 100 mm long and 12.7 mm in diameter. Use of such a robot in single port or standard multiport laparoscopy could enable reduction of number/size of ancillary trocars, and/or increase the number of working devices that can be deployed, thus paving the way for multiple point of view laparoscopy.

A novel mechanism for actuating a miniature swimming robot is described, modeled, and experimentally validated. Underwater propulsion is obtained through the interaction of mobile internal permanent magnets that move a number of polymeric flaps arranged around the body of the robot. Due to the flexibility of the proposed swimming mechanism, a different range of performances can be obtained by varying the design features. A simple multiphysics dynamic model was developed in order to predict basic behavior in fluids for different structural parameters of the robot. In order to experimentally verify the proposed mechanism and to validate the model, a prototype of the swimming robot was fabricated. The device is 35 mm in length and 18 mm in width and thickness, and the forward motion is provided by four flaps with an active length of 20 mm. The model was able to correctly predict flap dynamics, thrust, and energy expenditure for magnetic dragging within a spindle-frequency range going from 2 to 5 Hz. Additionally, the model was used to infer robot-thrust variation related to different spindle frequencies and a 25% increase in flap active length. Concerning swimming performance, the proposed technical implementation of the concept was able to achieve 37 mm/s with 4.9% magnetic mechanism efficiency.

In this work, we propose a new concept for locomotion of a miniature jellyfish-like robot based on the interaction of mobile permanent magnets. The robot is 35 mm in length and 15 mm in width, and it incorporates a rotary actuator, a magnetic rotor, several elastic magnetic tails and a polymeric body embedding a wireless microcontroller and power supply. The novel magnetic mechanism is very versatile for numerous applications and can be tailored and adapted on the basis of different specifications. An analytical model of the magnetic mechanism allows to shape the robot design based on the specific application. The working principle of the robot together with the design, prototyping and testing phases are illustrated in this paper.

We present design and experimental performance results for a novel mechanism for robotic legged locomotion at the mesoscale (from hundreds of microns to tens of centimeters). The new mechanism is compact and strikes a balance between conflicting design objectives, exhibiting high foot forces and low power consumption. It enables a small robot to traverse a compliant, slippery, tubular environment, even while climbing against gravity. This mechanism is useful for many mesoscale locomotion tasks, including endoscopic capsule robot locomotion in the gastrointestinal tract. It has enabled fabrication of the first legged endoscopic capsule robot whose mechanical components match the dimensions of commercial pill cameras (11 mm diameter by 25 mm long). A novel slot-follower mechanism driven via lead screw enables the mechanical components of the capsule robot to be as small while simultaneously generating 0.63 N average propulsive force at each leg tip. In this paper, we describe kinematic and static analyses of the lead screw and slot-follower mechanisms, optimization of design parameters, and experimental design and tuning of a gait suitable for locomotion. A series ofex vivoexperiments demonstrate capsule performance and ability to traverse the intestine in a manner suitable for inspection of the colon in a time period equivalent to standard colonoscopy.

Remote manipulation tasks in the small scale can often not be performed autonomously, due to the unstructured nature of the environments and the limited capabilities of sensor and localization technologies. For these tasks, teleoperated systems are used, in which the human operator is integral part of the control. In time-delayed teleoperation, the operator gradually adopts discrete control strategies, such as 'move-and-wait'. In this paper, we present and compare three different control strategies for driving a nanomanipulation system with direct vision feedback. Two strategies are based on a fixed step size to move the manipulator, while the third uses a variable step size. The strategies are compared on a 2D fine positioning task. Experimental results are in agreement with Fitts' law and show that the third strategy, besides allowing movements of size ranging across several orders of magnitude, also allows to complete the fine positioning task in less time. The control strategies can be used in general to control vision-guided teleoperation systems affected by time delay.

This paper describes a microcontroller-based multichannel telemetry system, suitable for in vivo monitoring of physiological parameters. The device can digitalize and transmit up to three analog signals coming from different sensors. The telemetry transmission is obtained by using a carrier frequency of 433.92 MHz and an amplitude-shift keying modulation. The signal data rate is 13 kb/s per channel. The digital microcontroller provides good flexibility and interesting performance, such as the threshold monitoring, the transmission error detection, and a low power consumption, thanks to the implementation of a sleep mode. The small overall size (less than 1 cm3), the power density compatible with current regulations for the design of implantable devices, and the dedicated packaging make the system suitable for in vivo monitoring in humans. The design, fabrication, operation, packaging, and performance of the system are described in this paper. An in vivo pressure monitoring case study is described as well.

This paper is focused on a portable sensorized micro end-effector purposely designed for operating in biomedical test-benches. The gripper, previously designed and fabricated in various materials exploiting different technologies, has been devised to perform tasks of micromanipulation and characterization of biological tissues once integrated into the workstation developed in the authors' lab. The last model of the gripper prototype has been integrated in a "smart" mounting, with on board electronics for signal processing, and further improvements are planned in order to enhance the portability of the tool. Actually, for the development of novel tools and methodologies in the fields of biomedical engineering and computer assisted surgery (CAS), ever-growing importance have both in vitro and in vivo tests, aimed at validating methods and at optimizing innovative tools. The selected gripper, fabricated in stainless steel by laser machining, is equipped with commercial semiconductor strain gauges as force sensors.