Refine
Document Type
- Article (reviewed) (13)
- Patent (3)
- Article (unreviewed) (2)
- Conference Proceeding (1)
- Contribution to a Periodical (1)
- Letter to Editor (1)
- Moving Images (1)
Conference Type
- Konferenzartikel (1)
Is part of the Bibliography
- yes (22)
Keywords
- Götz von Berlichingen (4)
- neuroprosthetics (4)
- 3D-CAD (3)
- Augmented Reality (3)
- Prothetik (3)
- amputee (3)
- 3D computer-aided design (2)
- Handprothese (2)
- Iron Hand (2)
- Kamera-Tracking (2)
Institute
Open Access
- Open Access (15)
- Closed Access (4)
- Gold (2)
- Bronze (1)
- Closed (1)
- Diamond (1)
- Hybrid (1)
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.
Optische Navigationssysteme weisen bisher eine eindeutige Trennung zwischen nachverfolgendem Gerät (Tool Tracker) und nachverfolgten Geräten (Tracked Tools) auf. In dieser Arbeit wird ein neues Konzept vorgestellt, dass diese Trennung aufhebt: Jedes Tracked Tool ist gleichzeitig auch Tool Tracker und besteht aus Marker-LEDs sowie mindestens einer Kamera, mit deren Hilfe andere Tracker in Lage und Orientierung nachverfolgt werden können. Bei Verwendung von nur einer Kamera geschieht dies mittels Pose Estimation, ab zwei Kameras werden die Marker-LEDs trianguliert. Diese Arbeit beinhaltet die Vorstellung des neuen Peer-To-Peer-Tracking-Konzepts, einen sehr schnellen Pose-Estimation-Algorithmus für beliebig viele Marker sowie die Klärung der Frage, ob die mit Pose Estimation erreichbare Genauigkeit vergleichbar mit der eines Stereo-Kamera-Systems ist und den Anforderungen an die chirurgische Navigation gerecht wird.
A novel, unsupervised, artificial intelligence system is presented, whose input signals and trainable weights consist of complex or hypercomplex values. The system uses the effect given by the complex multiplication that the multiplicand is not only scaled but also rotated. The more similar an input signal and the reference signal are, the more likely the input signal belongs to the corresponding class. The data assigned to a class during training is stored on a generic layer as well as on a layer extracting special features of the signal. As a result, the same cluster can hold a general description and the details of the signal. This property is vital for assigning a signal to an existing or a new class. To ensure that only valid new classes are opened, the system determines the variances by comparing each input signal component with the weights and adaptively adjusts its activation and threshold functions for an optimal classification decision. The presented system knows at any time all boundaries of its clusters. Experimentally, it is demonstrated that the system is able to cluster the data of multiple classes autonomously, fast, and with high accuracy.
Background/Objectives: The subject of this work is the reconstruction of the inner mechanics of Götz von Berlichingen's second "Iron Hand". The complex inner mechanics were unknown until Christian von Mechel published a detailed description in 1815. In this artificial hand, each finger can be engaged individually in its three joints and the thumb in one joint.
Methods: Based on this description, the individual components were reconstructed at an enlarged scale of 2:1 using CAD software and a 3D printer for the mechanisms. In addition, an FEM analysis was carried out for the components exposed to the greatest stress in order to identify critical areas.
Results: By making some adjustments to the mechanics, it was possible to reproduce the mechanisms on a scale of 2:1 on the basis of the index finger. However, if the model was scaled back to 1:1, the internal plastic components were too fragile. This problem was caused by the properties of the 3D printing materials and could be solved by manufacturing the springs from steel.
Conclusions: This work aims to make a valuable contribution to the preservation and understanding of the historical artificial second "Iron Hand" of Götz von Berlichingen. It once again demonstrates the very precise and detailed craftsmanship of goldsmiths of that time.
Background: This paper presents a conceptual design for an anthropomorphic replacement hand made of silicone that integrates a sensory feedback system. In combination with a motorized orthosis, it allows performing movements and registering information on the flexion and the pressure of the fingers.
Methods: To create the replacement hand, a three-dimensional (3D) scanner was used to scan the hand of the test person. With computer-aided design (CAD), a mold was created from the hand, then 3D-printed. Bending and force sensors were attached to the mold before silicone casting to implement the sensory feedback system. To achieve a functional and anthropomorphic appearance of the replacement hand, a material analysis was carried out. In two different test series, the properties of the used silicones were analyzed regarding their mechanical properties and the manufacturing process.
Results: Individual fingers and an entire hand with integrated sensors were realized, which demonstrated in several tests that sensory feedback in such an anthropomorphic replacement hand can be realized. Nevertheless, the choice of silicone material remains an open challenge, as there is a trade-off between the hardness of the material and the maximum mechanical force of the orthosis.
Conclusion: Apart from manufacturing-related issues, it is possible to cost-effectively create a personalized, anthropomorphic replacement hand, including sensory feedback, by using 3D scanning and 3D printing techniques.
In this paper, a concept for an anthropomorphic replacement hand cast with silicone with an integrated sensory feedback system is presented. In order to construct the personalized replacement hand, a 3D scan of a healthy hand was used to create a 3D-printed mold using computer-aided design (CAD). To allow for movement of the index and middle fingers, a motorized orthosis was used. Information about the applied force for grasping and the degree of flexion of the fingers is registered using two pressure sensors and one bending sensor in each movable finger. To integrate the sensors and additional cavities for increased flexibility, the fingers were cast in three parts, separately from the rest of the hand. A silicone adhesive (Silpuran 4200) was examined to combine the individual parts afterwards. For this, tests with different geometries were carried out. Furthermore, different test series for the secure integration of the sensors were performed, including measurements of the registered information of the sensors. Based on these findings, skin-toned individual fingers and a replacement hand with integrated sensors were created. Using Silpuran 4200, it was possible to integrate the needed cavities and to place the sensors securely into the hand while retaining full flexion using a motorized orthosis. The measurements during different loadings and while grasping various objects proved that it is possible to realize such a sensory feedback system in a replacement hand. As a result, it can be stated that the cost-effective realization of a personalized, anthropomorphic replacement hand with an integrated sensory feedback system is possible using 3D scanning and 3D printing. By integrating smaller sensors, the risk of damaging the sensors through movement could be decreased.
Neuroprosthetics 2.0
(2019)