Intention recognition for gaze controlled robotic minimally invasive laser ablation

Eye tracking technology has shown promising results for allowing hands-free control of robotically-mounted cameras and tools. However existing systems present only limited capabilities in allowing the full range of camera motions in a safe, intuitive manner. This paper introduces a framework for the recognition of surgeon intention, allowing activation and control of the camera through natural gaze behaviour. The system is resistant to noise such as blinking, while allowing the surgeon to look away safely at any time. Furthermore, this paper presents a novel approach to control the translation of the camera along its optical axis using a combination of eye tracking and stereo reconstruction. Combining eye tracking and stereo reconstruction allows the system to determine which point in 3D space the user is fixating, enabling a translation of the camera to achieve the optimal viewing distance. In addition, the eye tracking information is used to perform automatic laser targeting for laser ablation. The desired target point of the laser, mounted on a separate robotic arm, is determined with the eye tracking thus removing the need to manually adjust the laser's target point before starting each new ablation. The calibration methodology used to obtain millimetre precision for the laser targeting without the aid of visual servoing is described. Finally, a user study validating the system is presented, showing clear improvement with median task times under half of those of a manually controlled robotic system

A low-power opportunistic communication protocol for wearable applications

© 2015 IEEE.Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment

Micromagnetometer calibration for accurate orientation estimation

Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3° root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications

Smart wireless headphone for cardiovascular and stress monitoring

Wearable technology has become ubiquitous in recent years due to the miniaturization of circuit electronics and advances in smart materials that can conform to the requirements posed by the human body, behaviour and experience. Sensors of this type are found attached almost to every body segment, capable of delivering signals even in harsh activity scenarios. The reliability and relevance of the physiological data retrieved by wearables have yet to surpass the conventional technologies in the healthcare system today. In this paper we present a small device incorporated inside an headphone set that continuously monitors the ECG, impedance and acceleration of the head. As opposed to most biometric sensors, ECG measurement relies on non-optical methods by capturing the electrical potential around the ear in both sides of the head, whereas impedance monitoring involves AC stimulation instead of DC, the latter commonly involved in skin galvanic response estimation. Signal processing of impedance parameters is performed in situ using a fast variant of the Discrete Fourier Transform in order to save computational resources and power expenditure from a microcontroller equipped with Bluetooth Low Energy. Applications that can benefit from this device include cardiovascular and stress level assessment of individuals for whom an hearable is a requirement for work or leisure

Fifty Years of Innovation in Plastic Surgery

© 2016 The Korean Society of Plastic and Reconstructive Surgeons.Background Innovation has molded the current landscape of plastic surgery. However, documentation of this process only exists scattered throughout the literature as individual articles. The few attempts made to profile innovation in plastic surgery have been narrative, and therefore qualitative and inherently biased. Through the implementation of a novel innovation metric, this work aims to identify and characterise the most prevalent innovations in plastic surgery over the last 50 years. Methods Patents and publications related to plastic surgery (1960 to 2010) were retrieved from patent and MEDLINE databases, respectively. The most active patent codes were identified and grouped into technology areas, which were subsequently plotted graphically against publication data. Expert-derived technologies outside of the top performing patents areas were additionally explored. Results Between 1960 and 2010, 4,651 patents and 43,118 publications related to plastic surgery were identified. The most active patent codes were grouped under reconstructive prostheses, implants, instruments, non-invasive techniques, and tissue engineering. Of these areas and other expert-derived technologies, those currently undergoing growth include surgical instruments, implants, non-invasive practices, transplantation and breast surgery. Innovations related to microvascular surgery, liposuction, tissue engineering, lasers and prostheses have all plateaued. Conclusions The application of a novel metric for evaluating innovation quantitatively outlines the natural history of technologies fundamental to the evolution of plastic surgery. Analysis of current innovation trends provides some insight into which technology domains are the most active

Implicit active constraints for safe and effective guidance of unstable concentric tube robots

Safe and effective telemanipulation of concentric tube robots is hindered by their complex, non-intuitive kinematics. In order for clinicians to operate these robots naturally, guidance schemes in the form of attractive and repulsive constraints can simplify task execution. The real-time seamless calculation and application of guidance, however, requires computationally efficient algorithms that solve the non-linear inverse kinematics of the robot and guarantee that the commanded robot configuration is stable and sufficiently away from the anatomy. This paper presents a multi-processor framework that allows on-the-fly calculation of optimal safe paths based on rapid workspace and roadmap precomputation. The realtime nature of the developed software enables complex guidance constraints to be implemented with minimal computational overhead. A clinically challenging user study demonstrates that the incorporated guiding constraints are highly beneficial for fast and accurate navigation with concentric tube robots

Shape sensing of miniature snake-like robots using optical fibers

Snake like continuum robots are increasingly used for minimally invasive surgery. Most robotic devices of this sort that have been reported to date are controlled in an open loop manner. Using shape sensing to provide closed loop feedback would allow for more accurate control of the robot's position and, hence, more precise surgery. Fiber Bragg Gratings, magnetic sensors and optical reflectance sensors have all been reported for this purpose but are often limited by their cost, size, stiffness or complexity of fabrication. To address this issue, we designed, manufactured and tested a prototype two-link robot with a built-in fiber-optic shape sensor that can deliver and control the position of a CO 2 -laser fiber for soft tissue ablation. The shape sensing is based on optical reflectance, and the device (which has a 4 mm outer diameter) is fabricated using 3D printing. Here we present proof-of-concept results demonstrating successful shape sensing - i.e. measurement of the angular displacement of the upper link of the robot relative to the lower link - in real time with a mean measurement error of only 0.7°

Adaptive Bayesian networks for video processing

ABSTRACT Due to its static nature, the inference capability of Bayesian Networks (BNs) oflen deteriorates when the basis of input data varies, especially in video processing applications where the environment often changes constantly. This paper presents an adaptive BN where the network parameters are adjusted in accordance to input variations. An efficient re-training method is introduced for updating the parameters and the proposed network is applied to shadow removal in video sequence processing with quantitative results demonstrating the significance of adapting the network with environmental changes