Movement Tracking and False Positive Reduction Method for Microwave Colonoscopy Systems

Motion capture and tracking have rapidly become crucial tools in the fields of animation and robotics since they make it possible to record and evaluate the motion of human subjects. In this paper, we provide an overview of motion capture and tracking, investigate the many methodologies and datasets that are now in use, describe the system components that are essential to motion capture and tracking for animation and robotics, and discuss the problems and opportunities that lie ahead for this particular field of research. In this article, we investigate the numerous motion capture and tracking systems that are currently on the market and highlight the advantages and disadvantages of each. In addition, we highlight the technological challenges of motion capture and tracking, as well as the ethical and privacy concerns that are related with these issues. In conclusion, we offer several proposals for further research, including the development of innovative motion capture and tracking methods, the collection of motion capture datasets that are both vaster and more diverse, and the assessment of ethical and privacy problems. This study, taken as a whole, sheds light on the present state of the art in motion capture and tracking technology, as well as its prospective applications in the next generation of animation and robotics.

In the field of multimodal human–computer interaction (HCI), hand (and finger) motion tracking remains a critical challenge because it is the input means of visual reproduction and provides the necessary parameters for real-time modeling of the haptic model. Among motion tracking methods including optical tracking methods, inertial tracking methods, and magnetic motion tracking (MMT) methods, the MMT method has a great development prospect as a marker-based method because of its advantages of no line-of-sight problem, being natural-oriented, and high accuracy. In this article, we first discuss the performance and application scenarios under different configurations of electromagnet-based MMT (EMMT) and permanent-magnet-based MMT (PMMT) systems. And then, the MMT algorithms are reviewed, which can be divided into two broad categories: The model-based algorithms include linear algorithm, nonlinear optimization algorithm, and recursive Bayesian algorithm, and the offline-data-based algorithms include neural network algorithm and lookup table (LUT) algorithm. In addition, a state-of-the-art comparison of MMT algorithms is given. In the end, we have an insightful discussion and give the expected future outlook of MMT.

We propose a method for detecting and tracking the motion of a large number of moving objects in crowded environments, such as concourses in railway stations or airports, shopping malls, or convention centers. Unlike many methods for motion detection and tracking, our approach is not based on vision but uses 2D range images from a laser rangefinder. This facilitates the real-time capability of our approach, which was a primary goal. The time-variance of an environment is captured by a sequence of temporal maps, which we denoted as time stamp maps. A time stamp map is a projection of a range image onto a two-dimensional grid, where each cell which coincides with a specific range value is assigned a time stamp. Based on this representation we devised two very simple algorithms for motion detection and motion tracking. Our approach is very efficient, with a complete cycle involving both motion detection and tracking taking 6 ms on a Pentium 166Mhz.Keywordsmotion detectionreal-time motion trackingmultiple moving objectsrange imagestemporal maps

PurposeTo evaluate the performance of the first clinical real‐time motion tracking and compensation system using multileaf collimator (MLC) and jaws during helical tomotherapy delivery.MethodsAppropriate mechanical and dosimetry tests were performed on the first clinical real‐time motion tracking system (Synchrony on Radixact, Accuray Inc) recently installed in our institution. kV radiography dose was measured by CTDIw using a pencil chamber. Changes of beam characteristics with jaw offset and MLC leaf shift were evaluated. Various dosimeters and phantoms including A1SL ion chamber (Standard Imaging), Gafchromic EBT3 films (Ashland), TomoPhantom (Med Cal), ArcCheck (Sun Nuclear), Delta4 (ScandiDos), with fiducial or high contrast inserts, placed on two dynamical motion platforms (CIRS dynamic motion‐CIRS, Hexamotion‐ScandiDos), were used to assess the dosimetric accuracy of the available Synchrony modalities: fiducial tracking with nonrespiratory motion (FNR), fiducial tracking with respiratory modeling (FR), and fiducial free (e.g., lung tumor tracking) with respiratory modeling (FFR). Motion detection accuracy of a tracking target, defined as the difference between the predicted and instructed target positions, was evaluated with the root mean square (RMS). The dose accuracy of motion compensation was evaluated by verifying the dose output constancy and by comparing measured and planned (predicted) three‐dimensional (3D) dose distributions based on gamma analysis.ResultsThe measured CTDIw for a single radiograph with a 120 kVp and 1.6 mAs protocol was 0.084 mGy, implying a low imaging dose of 8.4 mGy for a typical Synchrony motion tracking fraction with 100 radiographs. The dosimetric effect of the jaw swing or MLC leaf shift was minimal on depth dose (<0.5%) and was <2% on both beam profile width and output for typical motions. The motion detection accuracies, that is, RMS, were 0.84, 1.13, and 0.48 mm for FNR, FR, and FFR, respectively, well within the 1.5 mm recommended tolerance. Dose constancy with Synchrony was found to be within 2%. The gamma passing rates of 3D dose measurements for a variety of Synchrony plans were well within the acceptable level.ConclusionsThe motion tracking and compensation using kV radiography, MLC shifting, and jaw swing during helical tomotherapy delivery was tested to be mechanically and dosimetrically accurate for clinical use.

PurposeThis work reports the clinical implementation of a real-time motion tracking and correction system using dynamic multileaf collimator and jaws during helical tomotherapy delivery (Synchrony on Radixact; Accuray, Inc). Methods and MaterialsThe first clinical Synchrony on Radixact system was recently installed and tested at our institution. Various clinical workflows, including fiducial implantation, computed tomography simulation, treatment planning, delivery quality assurance, treatment simulation, and delivery, for both fiducial-free and fiducial-based motion tracking methods were developed. Treatment planning and delivery data from initial patients, including dosimetric benefits, real-time target detection, model building, motion tracking accuracy, delivery smoothness, and extra dose from real-time radiographic imaging, were analyzed. ResultsThe Synchrony on Radixact system was tested to be within its performance specifications and has been used to treat 10 lung (fiducial-free) and 5 prostate (fiducial-based) patients with cancer so far in our clinic. The success of these treatments, especially for fiducial-free tracking, depends on multiple factors, including careful selection of the patient, appropriate setting of system parameters, appropriate positioning of the patient and skin markers, and use of treatment simulation. For the lung tumor cases, difficulties in model building, due primarily to the changes of target detectability or respiration patterns, were observed, which led to important system upgrades, including the addition of a treatment delivery simulation capability. Motion tracking metrics for all treated patients were within specifications, for example, (1) delivery quality assurance passing rates >95%; (2) extra dose from radiograph <0.5% of the prescription dose; and (3) average Potential Diff, measured Δ, and Rigid Body were within 6.5, 2.9, and 3.9 mm, respectively. ConclusionsPractical workflows for the use of the first clinical motion tracking and correction system in helical tomotherapy delivery have been developed, and the system has now been successfully implemented in our clinic for treating patients with lung and prostate cancer.

Development of Frameless SRS using Real-Time 6D Facial Surface Monitoring and Continuous Adaptive Head Motion Correction

Motion tracking has long been one of the primary challenges in mixed reality (MR), augmented reality (AR), and virtual reality (VR). Military and defense training can provide particularly difficult challenges for motion tracking, such as in the case of Military Operations in Urban Terrain (MOUT) and other dismounted, close quarters simulations. These simulations can take place across multiple rooms, with many fast-moving objects that need to be tracked with a high degree of accuracy and low latency. Many tracking technologies exist, such as optical, inertial, ultrasonic, and magnetic. Some tracking systems even combine these technologies to complement each other. However, there are no systems that provide a high-resolution, flexible, wide-area solution that is resistant to occlusion. While frameworks exist that simplify the use of tracking systems and other input devices, none allow data from multiple tracking systems to be combined, as if from a single system. In this paper, we introduce a method for compensating for the weaknesses of individual tracking systems by combining data from multiple sources and presenting it as a single tracking system. Individual tracked objects are identified by name, and their data is provided to simulation applications through a server program. This allows tracked objects to transition seamlessly from the area of one tracking system to another. Furthermore, it abstracts away the individual drivers, APIs, and data formats for each system, providing a simplified API that can be used to receive data from any of the available tracking systems. Finally, when single-piece tracking systems are used, those systems can themselves be tracked, allowing for real-time adjustment of the trackable area. This allows simulation operators to leverage limited resources in more effective ways, improving the quality of training.

Accurate device motion tracking enables many applications like Virtual Reality (VR) and Augmented Reality (AR). To make these applications available in people's daily life, low-cost acoustic-based motion tracking methods are proposed. However, existing acoustic-based methods are all based on distance estimation. These methods measure the distance between a speaker and a microphone. With a speaker or microphone array, it can get multiple estimated distances and further achieve multidimensional motion tracking. The weakness of distance-based motion tracking methods is that they need large array size to get accurate results. Some systems even require an array larger than 1 m. This weakness limits the adoption of existing solutions in a single device like a smart speaker. To solve this problem, we propose Acoustic Strength-based Angle Tracking (ASAT) System and further implement a motion tracking system based on ASAT. ASAT achieves angle tracking by creating a periodically changing sound field. A device with a microphone will sense the periodically changing sound strength in the sound field. When the device moves, the period of received sound strength will change. Thus we can derive the angle change and achieve angle tracking. The ASAT-based system can obtain the localization accuracy as 5 cm when the distance between the speaker and the microphone is in the range of 3 m.

The aim of this study was to compare prospective head motion correction and motion tracking abilities of two tracking systems: Active NMR field probes and a Moiré phase tracking camera system using an optical marker. Both tracking systems were used simultaneously on human subjects. The prospective head motion correction was compared in an MP2RAGE and a gradient echo sequence. In addition, the motion tracking trajectories for three subjects were compared against each other and their correlation and deviations were analyzed. With both tracking systems motion artifacts were visibly reduced. The precision of the field probe system was on the order of 50 µm for translations and 0.03° for rotations while the camera's was approximately 5 µm and 0.007°. The comparison of the measured trajectories showed close correlation and an average absolute deviation below 500 µm and 0.5°. This study presents the first in vivo comparison between NMR field probes and Moiré phase tracking. For the gradient echo images, the field probes had a similar motion correction performance as the optical tracking system. For the MP2RAGE measurement, however, the camera yielded better results. Still, both tracking systems substantially decreased image artifacts in the presence of subject motion. Thus, the motion tracking modality should be chosen according to the specific requirements of the experiment while considering the desired image resolution, refresh rate, and head coil constraints.

A device-free human detection and tracking system using a received signal strength indicator (RSSI) for an indoor environment is presented in this paper. The proposed system has two major functions: a wireless communication system and a human detection and tracking system. The first function is developed for measuring and collecting RSSI signals affected by human presence and movement, while the second function is developed for detecting and tracking the human using a predefined threshold and a zone selection method. The novelty of our proposed system is that the communication protocol can avoid signal interference and packet loss in the network, and the detection and tracking method can specify an actual zone that the human is present by taking an optimal predefined threshold and a level of RSSI variation in each zone into consideration. The proposed system is verified by experiments, and various human movement patterns with different directions and speeds are tested. The experimental results show that the proposed communication protocol can significantly provide communication reliability, and the proposed method can properly detect and track human movements. The packet delivery ratio indicating communication reliability is almost 100%. Detection and tracking accuracy measured by the number of times the method can detect and track the human with the correct zone is almost 100% in all cases of one man movements.

Electromagnetic tracking has widely been used in many fields, such as virtual reality, motion tracking, and biomedicine. Especially in surgical navigation systems, electromagnetic tracking offers a cost-effective alternative to traditional X-ray fluoroscopy. In this paper, we present a new magnetic tracking method based on phase difference detection. Two 3-axis magnetic sources and one 3-axis sensor are needed in the tracking system. The position and orientation of the sensor can be obtained simply by measuring the phase difference between the square of the synthetic magnetic induction intensity at the position of the sensor and the square of the excitation current of the magnetic source. The experimental results indicate that this method has a good tracking accuracy and speed. The mean position error is less than 0.1 cm, and the theoretical tracking time can be less than 3.5 ms when the frequency of the excitation current is no less than 1 kHz. Furthermore, antinoise simulations show that the tracking performance of this method is more robust against environmental noise than that of existing methods. Therefore, the electromagnetic tracking method proposed in this paper has good application prospects in many respects. It enables real-time tracking with high accuracy and a strong antinoise ability.

Simulation-based training program for peripherally inserted central venous catheter insertion: Randomized comparative study of synchronous direct feedback versus asynchronous distance feedback. Best Abstract, SESAM Lisbon Congress 2023.

This extended abstract details previous methods for motion tracking and capture in 3D animation and in particular that of hand motion tracking and capture. The research aims to enable gesture capture with interpretation of the captured gestures and control of the target 3D animation software. This stage of the project involves the development and testing of a motion analysis system. A motion analysis system is being built from algorithms recently developed. The authors review current software and research methods available in this area and describe current work-in-progress.Motion capture is a technique of digitally recording the movements of real entities, usually humans. It was originally developed as an analysis tool in biomechanics research, but has grown increasingly important as a source of motion data for computer animation. In this context it has been widely used for both cinema and video games. Hand motion capture and tracking in particular has received a lot of attention because of its critical role in the design of new Human Computer Interaction methods and gesture analysis. One of the main difficulties is the capture of human hand motion.

Tracking the motion and pathing of insects is critical for understanding the underlying factors determining their behaviors. Methods of tetherless motion tracking using servosphere systems as omnidirectional treadmills have been demonstrated to be as suitable for insect tracking as conventional methods, such as tethers or markers in arenas or trackballs, while presenting several practical and experimental advantages. This project expands on previously established applications of the servosphere system as a method of motion tracking with three primary contributions: (i) building, coding, and operating a three-wheeled servosphere system in order to evaluate its viability as a method of live motion tracking, (ii) tracking the motion of subjects stimulated by external olfactory cues to show the system's robustness with both innate and stimulated motion, and (iii) developing a convolutional neural network (CNN) model using trajectories recorded by the servosphere system to demonstrate its potential applications in classification via artificial intelligence. Across seven different subjects, the average error value was 3.23 mm, which fell within the allotted uncertainty of 6 mm. The unstimulated subjects' average speed was calculated to be 5.448 mm/s and the stimulated subjects' average speed was 11.594 mm/s, and their trajectories yielded a model accuracy of 64%. The experimental results show promising signs of applying the servosphere system in further motion tracking data analytics.

Enhancements have been made in the development of a real-time optical pose measurement and tracking system that provides 3D position and orientation data for a single photon emission computed tomography (SPECT) imaging system for awake, unanesthetized, unrestrained small animals. Three optical cameras with infrared (IR) illumination view the head movements of an animal enclosed in a transparent burrow. Markers placed on the head provide landmark points for image segmentation. Strobed IR LED’s are synchronized to the cameras and illuminate the markers to prevent motion blur for each set of images. The system using the three cameras automatically segments the markers, detects missing data, rejects false reflections, performs trinocular marker correspondence, and calculates the 3D pose of the animal’s head. Improvements have been made in methods for segmentation, tracking, and 3D calculation to give higher speed and more accurate measurements during a scan. The optical hardware has been installed within a Siemens MicroCAT II small animal scanner at Johns Hopkins without requiring functional changes to the scanner operation. The system has undergone testing using both phantoms and live mice and has been characterized in terms of speed, accuracy, robustness, and reliability. Experimental data showing these motion tracking results are given.