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Method for controlling a device, in particular, a prosthetic hand or a robotic arm (US20200327705A1)
(2020)
A method for controlling a device, in particular a prosthetic hand or a robotic arm, includes using an operator-mounted camera to detect at least one marker positioned on or in relation to the device. Starting from the detection of the at least one marker, a predefined movement of the operator together with the camera is detected and is used to trigger a corresponding action of the device. The predefined movement of the operator is detected in the form of a line of sight by means of camera tracking. A system for controlling a device, in particular a prosthetic hand or a robotic arm, includes a pair of AR glasses adapted to detect the at least one marker and to detect the predefined movement of the operator.
Purpose
This work presents a new monocular peer-to-peer tracking concept overcoming the distinction between tracking tools and tracked tools for optical navigation systems. A marker model concept based on marker triplets combined with a fast and robust algorithm for assigning image feature points to the corresponding markers of the tracker is introduced. Also included is a new and fast algorithm for pose estimation.
Methods
A peer-to-peer tracker consists of seven markers, which can be tracked by other peers, and one camera which is used to track the position and orientation of other peers. The special marker layout enables a fast and robust algorithm for assigning image feature points to the correct markers. The iterative pose estimation algorithm is based on point-to-line matching with Lagrange–Newton optimization and does not rely on initial guesses. Uniformly distributed quaternions in 4D (the vertices of a hexacosichora) are used as starting points and always provide the global minimum.
Results
Experiments have shown that the marker assignment algorithm robustly assigns image feature points to the correct markers even under challenging conditions. The pose estimation algorithm works fast, robustly and always finds the correct pose of the trackers. Image processing, marker assignment, and pose estimation for two trackers are handled in less than 18 ms on an Intel i7-6700 desktop computer at 3.4 GHz.
Conclusion
The new peer-to-peer tracking concept is a valuable approach to a decentralized navigation system that offers more freedom in the operating room while providing accurate, fast, and robust results.
Restoring hand motion to people experiencing amputation, paralysis, and stroke is a critical area of research and development. While electrode-based systems that use input from the brain or muscle have proven successful, these systems tend to be expensive and di¨cult to learn. One group of researchers is exploring the use of augmented reality (AR) as a new way of controlling hand prostheses. A camera mounted on eyeglasses tracks LEDs on a prosthetic to execute opening and closing commands using one of two different AR systems. One system uses a rectangular command window to control motion: crossing horizontally signals “open” along one direction and “close” in the opposite direction. The second system uses a circular command window: once control is enabled, gripping strength can be controlled by the direction of head motion. While the visual system remains to be tested with patients, its low cost, ease of use, and lack of electrodes make the device a promising solution for restoring hand motion.
In the field of neuroprosthetics, the current state-of-the-art method involves controlling the prosthesis with electromyography (EMG) or electrooculography/electroencephalography (EOG/EEG). However, these systems are both expensive and time consuming to calibrate, susceptible to interference, and require a lengthy learning phase by the patient. Therefore, it is an open challenge to design more robust systems that are suitable for everyday use and meet the needs of patients. In this paper, we present a new concept of complete visual control for a prosthesis, an exoskeleton or another end effector using augmented reality (AR) glasses presented for the first time in a proof-of-concept study. By using AR glasses equipped with a monocular camera, a marker attached to the prosthesis is tracked. Minimal relative movements of the head with respect to the prosthesis are registered by tracking and used for control. Two possible control mechanisms including visual feedback are presented and implemented for both a motorized hand orthosis and a motorized hand prosthesis. Since the grasping process is mainly controlled by vision, the proposed approach appears to be natural and intuitive.
A new concept for robust non-invasive optical activation of motorized hand prostheses by simple and non-contactcommands is presented. In addition, a novel approach for aiding hand amputees is shown, outlining significantprogress in thinking worth testing. In this, personalized 3D-printed artificial flexible hands are combined withcommercially available motorized exoskeletons, as they are used e.g. in tetraplegics.
Neuroprosthetics 2.0
(2019)
Nowadays, robotic systems are an integral part of many orthopedic interventions. Stationary robots improve the accuracy but also require adapted surgical workflows. Handheld robotic devices (HHRDs), however, are easily integrated into existing workflows and represent a more economical solution. Their limited range of motion is compensated by the dexterity of the surgeon. This work presents control algorithms for HHRDs with multiple degrees of freedom (DOF). These algorithms protect pre- or intraoperatively defined regions from being penetrated by the end effector (e.g., a burr) by controlling the joints as well as the device’s power. Accuracy tests on a stationary prototype with three DOF show that the presented control algorithms produce results similar to those of stationary robots and much better results than conventional techniques. This work presents novel and innovative algorithms, which work robustly, accurately, and open up new opportunities for orthopedic interventions.
Flexible Three-dimensional Camera-based Reconstruction and Calibration of Tracked Instruments
(2016)
Navigated instruments commonly include applied parts, e.g. burrs or saw blades, that need to be calibrated with respect to the attached or integrated tracker. Since this calibration has to be very precise, it is often performed by the manufacturer. However, due to the great variety of instruments and the option to exchange the applied parts (e.g. burrs) there is a definite demand for flexible and generic calibration techniques. Furthermore, if we look into the medical field, there is also a need for calibrating sterile instruments. We propose a new and flexible camera-based calibration technique that addresses these demands by working contactlessly, precisely, and generically for a large variety of tracked instruments. This is realized using one or more tracked cameras which are calibrated with respect to an attached or integrated tracker. The tracked instrument is rotated in front of the camera(s) and its 3D geometry and surface are reconstructed from the 2D images in the coordinate system of the attached or integrated tracker. The 3D geometry of the navigated instrument was reconstructed with an accuracy of under 0.2 mm. The radius of a sphere-shaped instrument was reconstructed with an RMS deviation of 0.015mm.