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Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera's focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.

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METHOD: 2D/3D kV imaging and CBCT data using 6 degrees of freedom (6DoF) were compared to evaluate inter and intrafraction motion. RESULTS: Results showed that intrafraction errors were low and interfraction levels were within institutional protocols. CONCLUSION: Confidence was given to use low dose 2D/3D kV imaging to confirm daily patient set up errors, and to use pre-treatment CBCT only once weekly for additional imaging information. IMPLICATIONS FOR PRACTICE: Further research is necessary to assess other uncertainties, to enable the calculation of a margin and determining the feasibility of further reduction of this.

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This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

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Head impact sensors worn in the mouth are popular because they couple directly to the teeth and provide six-degree-of-freedom head measurements. Mouthpiece signal filters have conventionally used cutoff frequencies lower than recommended practices (Society of Automotive Engineers, SAE J211-1) to eliminate extraneous noise when measuring with live subjects. However, there is little information about the effects of filter choice on the accuracy of signals measured by instrumented mouthpieces. Lack of standardization in head impact measurement device post-processing techniques can result in data that are not comparable across studies or device brands. This study sought optimal filter cutoff frequencies for six-degree-of-freedom measurements made at the teeth using instrumented mouthguards. We collected linear acceleration and angular velocity signals at the head center of gravity (CG) using laboratory-grade instrumentation. We also collected and filtered similar six-degree-of-freedom measurements from an instrumented mouthguard using 24 cutoff frequencies, from 25 to 600 Hz. We transformed the measurements to linear acceleration at the center of gravity of the head (CG) using all kinematic variables at the teeth, optimizing linear and angular mouthguard cutoff frequencies with one equation. We calculated the percent error in transformed peak resultant linear acceleration and minimized the mean and standard deviation in error. The optimal cutoff frequencies were 175 Hz for linear acceleration and 250 Hz for angular velocity. Rigid impacts (3-5 ms duration) had higher optimal cutoff frequencies (175 Hz linear acceleration, 275 Hz angular velocity) than padded impacts (10-12 ms duration; 100 Hz linear acceleration, 175 Hz angular velocity), and all impacts together (3-12 ms duration; 175 Hz linear acceleration, 250 Hz angular velocity). Instrumented mouthpiece manufacturers and researchers using these devices should consider these optimal filter cutoff frequencies to minimize measurement error. Sport-specific filter criteria for teeth-based sensors may be warranted to account for the difference in optimal cutoff frequency combination by impact duration.

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Recently, 6DoF object pose estimation has become increasingly important for a broad range of applications in the fields of virtual reality, augmented reality, autonomous driving, and robotic operations. This task involves extracting the target area from the input data and subsequently determining the position and orientation of the objects. In recent years, many new advances have been made in pose estimation. However, existing reviews have the problem of only summarizing category-level or instance-level methods, and not comprehensively summarizing deep learning methods. This paper will provide a comprehensive review of the latest progress in 6D pose estimation to help researchers better understanding this area. In this study, the current methods about 6DoF object pose estimation are mainly categorized into two groups: instance-level and category-level groups, based on whether it is necessary to acquire the CAD model of the object. Recent advancements about learning-based 6DoF pose estimation methods are comprehensively reviewed. The study systematically explores the innovations and applicable scenarios of various methods. It provides an overview of widely used datasets, task metrics, and diverse application scenarios. Furthermore, state-of-the-art methods are compared across publicly accessible datasets, taking into account differences in input data types. Finally, we summarize the challenges of current tasks, methods for different applications, and future development directions.

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PURPOSE: Multiple survey results have identified a demand for improved motion management for liver cancer IGRT. Until now, real-time IGRT for liver has been the domain of dedicated and expensive cancer radiotherapy systems. The purpose of this study was to clinically implement and characterise the performance of a novel real-time 6 degree-of-freedom (DoF) IGRT system, Kilovoltage Intrafraction Monitoring (KIM) for liver SABR patients. METHODS/MATERIALS: The KIM technology segmented gold fiducial markers in intra-fraction x-ray images as a surrogate for the liver tumour and converted the 2D segmented marker positions into a real-time 6DoF tumour position. Fifteen liver SABR patients were recruited and treated with KIM combined with external surrogate guidance at three radiotherapy centres in the TROG 17.03 LARK multi-institutional prospective clinical trial. Patients were either treated in breath-hold or in free breathing using the gating method. The KIM localisation accuracy and dosimetric accuracy achieved with KIM + external surrogate were measured and the results were compared to those with the estimated external surrogate alone. RESULTS: The KIM localisation accuracy was 0.2±0.9 mm (left-right), 0.3±0.6 mm (superior-inferior) and 1.2±0.8 mm (anterior-posterior) for translations and -0.1◦±0.8◦ (left-right), 0.6◦±1.2◦ (superior-inferior) and 0.1◦±0.9◦ (anterior-posterior) for rotations. The cumulative dose to the GTV with KIM + external surrogate was always within 5% of the plan. In 2 out of 15 patients, >5% dose error would have occurred to the GTV and an organ-at-risk with external surrogate alone. CONCLUSIONS: This work demonstrates that real-time 6DoF IGRT for liver can be implemented on standard radiotherapy systems to improve treatment accuracy and safety. The observations made during the treatments highlight the potential false assurance of using traditional external surrogates to assess tumour motion in patients and the need for ongoing improvement of IGRT technologies.

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The 6-Degree-of-Freedom (6-DoF) robotic grasping is a fundamental task in robot manipulation, aimed at detecting graspable points and corresponding parameters in a 3D space, i.e affordance learning, and then a robot executes grasp actions with the detected affordances. Existing research works on affordance learning predominantly focus on learning local features directly for each grid in a voxel scene or each point in a point cloud scene, subsequently filtering the most promising candidate for execution. Contrarily, cognitive models of grasping highlight the significance of global descriptors, such as size, shape, and orientation, in grasping. These global descriptors indicate a grasp path closely tied to actions. Inspired by this, we propose a novel bio-inspired neural network that explicitly incorporates global feature encoding. In particular, our method utilizes a Truncated Signed Distance Function (TSDF) as input, and employs the recently proposed Transformer model to encode the global features of a scene directly. With the effective global representation, we then use deconvolution modules to decode multiple local features to generate graspable candidates. In addition, to integrate global and local features, we propose using a skip-connection module to merge lower-layer global features with higher-layer local features. Our approach, when tested on a recently proposed pile and packed grasping dataset for a decluttering task, surpassed state-of-the-art local feature learning methods by approximately 5% in terms of success and declutter rates. We also evaluated its running time and generalization ability, further demonstrating its superiority. We deployed our model on a Franka Panda robot arm, with real-world results aligning well with simulation data. This underscores our approach's effectiveness for generalization and real-world applications.

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Due to the difficulty in generating a 6-Degree-of-Freedom (6-DoF) object pose estimation dataset, and the existence of domain gaps between synthetic and real data, existing pose estimation methods face challenges in improving accuracy and generalization. This paper proposes a methodology that employs higher quality datasets and deep learning-based methods to reduce the problem of domain gaps between synthetic and real data and enhance the accuracy of pose estimation. The high-quality dataset is obtained from Blenderproc and it is innovatively processed using bilateral filtering to reduce the gap. A novel attention-based mask region-based convolutional neural network (R-CNN) is proposed to reduce the computation cost and improve the model detection accuracy. Meanwhile, an improved feature pyramidal network (iFPN) is achieved by adding a layer of bottom-up paths to extract the internalization of features of the underlying layer. Consequently, a novel convolutional block attention module-convolutional denoising autoencoder (CBAM-CDAE) network is proposed by presenting channel attention and spatial attention mechanisms to improve the ability of AE to extract images' features. Finally, an accurate 6-DoF object pose is obtained through pose refinement. The proposed approach is compared to other models using the T-LESS and LineMOD datasets. Comparison results demonstrate the proposed approach outperforms the other estimation models.

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This study aimed to validate a bespoke 3D-printed phantom for use in quality assurance (QA) of a 6 degrees-of-freedom (6DoF) treatment couch. A novel phantom design comprising a main body with internal cube structures, was fabricated at five centres using Polylactic Acid (PLA) material, with an additional phantom produced incorporating a PLA-stone hybrid material. Correctional setup shifts were determined using image registration by 3D-3D matching of high HU cube structures between obtained cone-beam computer tomography (CBCT) images to reference CTs, containing cubes with fabricated rotational offsets of 3.5°, 1.5° and -2.5° in rotation, pitch, and roll, respectively. Average rotational setup shifts were obtained for each phantom. The reproducibility of 3D-printing was probed by comparing the internal cube size as well as Hounsfield Units between each of the uniquely produced phantoms. For the five PLA phantoms, the average rot, pitch and roll correctional differences from the fabricated offsets were -0.3 ± 0.2°, -0.2 ± 0.5° and 0.2 ± 0.3° respectively, and for the PLA hybrid these differences were -0.09 ± 0.14°, 0.30 ± 0.00° and 0.03 ± 0.10°. There was found to be no statistically significant difference in average cube size between the five PLA printed phantoms, with the significant difference (P < 0.05) in HU of one phantom compared to the others attributed to setup choice and material density. This work demonstrated the capability producing a novel 3D-printed 6DoF couch QA phantom design, at multiple centres, with each unique model capable of sub-degree couch correction.

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Accurately estimating the 6DoF pose of objects during robot grasping is a common problem in robotics. However, the accuracy of the estimated pose can be compromised during or after grasping the object when the gripper collides with other parts or occludes the view. Many approaches to improving pose estimation involve using multi-view methods that capture RGB images from multiple cameras and fuse the data. While effective, these methods can be complex and costly to implement. In this paper, we present a Single-Camera Multi-View (SCMV) method that utilizes just one fixed monocular camera and the initiative motion of robotic manipulator to capture multi-view RGB image sequences. Our method achieves more accurate 6DoF pose estimation results. We further create a new T-LESS-GRASP-MV dataset specifically for validating the robustness of our approach. Experiments show that the proposed approach outperforms many other public algorithms by a large margin. Quantitative experiments on a real robot manipulator demonstrate the high pose estimation accuracy of our method. Finally, the robustness of the proposed approach is demonstrated by successfully completing an assembly task on a real robot platform, achieving an assembly success rate of 80%.

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PURPOSE: Quantifying intra-fractional six-degree-of-freedom (6DoF) residual errors or motion from approved patient setups is necessary for accurate beam delivery in spine stereotactic body radiotherapy. However, previously reported errors were not acquired during beam delivery. Therefore, we aimed to quantify the 6DoF residual errors and motions during arc beam delivery using a concurrent cone-beam computed tomography (CBCT) imaging technique, intra-irradiation CBCT. METHODS: Consecutive 15 patients, 19 plans for various treatment sites, and 199 CBCT images were analyzed. Pre-irradiation CBCT was performed to verify shifts from the initial patient setup using the ExacTrac system. During beam delivery by two or three co-planar full-arc rotations, CBCT imaging was performed concurrently. Subsequently, an intra-irradiation CBCT image was reconstructed. Pre- and intra-irradiation CBCT images were rigidly registered to a planning CT image based on the bone to quantify 6DoF residual errors. RESULTS: 6DoF residual errors quantified using pre- and intra-irradiation CBCTs were within 2.0 mm/2.0°, except for one measurement. The mean elapsed time (mean ± standard deviation [min:sec]) after pre-irradiation CBCT to the end of the last arc beam delivery was 6:08 ± 1:25 and 7:54 ± 2:14 for the 2- and 3-arc plans, respectively. Root mean squares of residual errors for several directions showed significant differences; however, they were within 1.0 mm/1.0°. Time-dependent analysis revealed that the residual errors tended to increase with elapsed time. CONCLUSION: The errors represent the optimal intra-fractional error compared with those acquired using the pre-, inter-beam, and post-6DoF image guidance and can be acquired within a standard treatment timeslot.

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This paper proposes a robust control allocation for the capture control of the space inertial sensor's test mass under overcritical conditions. Uncertainty factors of the test mass control system under the overcritical condition are analyzed first, and a 6-DOF test mass dynamics model with system uncertainty is established. Subsequently, a time-varying weight function is designed to coordinate the allocation of 6-DOF generalized forces. Moreover, a robust control allocation method is proposed to distribute the commanded forces and torques into individual electrodes in an optimal manner, which takes into account the system uncertainties. This method transforms the robust control allocation problem into a second-order cone optimization problem, and its dual problem is introduced to simplify the computational complexity and improve the solving efficiency. Numerical simulation results are presented to illustrate and highlight the fine performance benefits obtained using the proposed robust control allocation method, which improves capture efficiency, increases the security margin and reduces allocation errors.

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PURPOSE: The objectives of the present study were to investigate the length change in different bundles of the superficial medial collateral ligament (sMCL) and lateral collateral ligament (LCL) during lunge, and to evaluate their association with Knee Society Score (KSS) following medial-pivot total knee arthroplasty (MP-TKA). METHODS: Patients with unilateral MP-TKA knees performed a bilateral single-leg lunge under dual fluoroscopy surveillance to determine the in-vivo six degrees-of-freedom knee kinematics. The contralateral non-operated knees were used as the control group. The attachment sites of the sMCL and LCL were marked to calculate the 3D wrapping length. The sMCL and LCL were divided into anterior, intermediate, and posterior portions (aMCL, iMCL, pMCL, aLCL, iLCL, pLCL). Correlations between lengths/elongation rate of ligament bundles from full extension to 100° flexion and the KSS were examined. RESULTS: The sMCL and LCL demonstrated relative stability in length at low flexion, but sMCL length decreased whereas LCL increased with further flexion on operated knees. The sMCL length increased at low flexion and remained stable with further flexion, while the LCL length decreased with flexion on the contralateral non-operated knees. The lengths of aMCL, iMCL, and pMCL showed moderate (0.5 < r < 0.7, p < 0.05) negative correlations with the KSS, and the lengths of aLCL, iLCL, and pLCL were positively correlated with the KSS at mid flexion on operated knees (p < 0.05). The elongation rates of aLCL, iLCL, and pLCL were negatively correlated with the KSS at high flexion on operated knees (p < 0.05). However, no significant correlations between the length of different bundles of sMCL or LCL with KSS were found on contralateral non-operated knees. CONCLUSIONS: The elongation pattern of sMCL/LCL on MP-TKA knees showed differences with contralateral non-operated knees. The sMCL is tense at low to middle flexion and relaxed at high flexion, while LCL is relaxed at low to middle flexion and tense at high flexion following MP-TKA. Medial stability and proper lateral flexibility during mid flexion were associated with favorable postoperative outcomes in MP-TKA patients. In contrast, lateral relaxation at deep flexion should be avoided when applying soft-tissue balancing in MP-TKA. LEVEL OF EVIDENCE: Level III.

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Various types of displacement sensors, which measure position changes of object, have been developed depending on the type and shape of the object under measurement, measurement range of the amount of displacement, required accuracy, and application. We are developing a new type of displacement sensor that is image-based, capable of measuring changes in 6DOF (3D position and orientation) of an object simultaneously, and is compact and low-cost. This displacement sensor measures the 6DOF of an object using images obtained by a monocular vision system. To confirm the usefulness of the proposed method, experimental measurements were conducted using a simple and inexpensive optical system. In this experiment, we were able to accurately measure changes of about 0.25 mm in displacement and 0.1 deg in inclination of the object at a distance of a few centimeters, and thus confirming the usefulness of the proposed method.

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Recently, due to the development of semiconductor technology, high-performance memory and digital convergence technology that integrates and implements various functions into one semiconductor chip has been regarded as the next-generation core technology. In the semiconductor manufacturing process, various motors are being applied for automated processes and high product reliability. However, dust and shaft loss due to mechanical friction of a general motor system composed of motor-bearing are problematic for semiconductor wafer processing. In addition, in the edge bread remove (EBR) process after the photoresist application process, a nozzle position control system for removing unnecessary portions of the wafer edge is absolutely necessary. Therefore, in this paper, in order to solve the problems occurring in the semiconductor process, a six-degrees-of-freedom (6-DOF) magnetic levitation system without shaft and bearing was designed for application to the semiconductor process system; and an integrated driving control algorithm for 6-DOF control (levitation, rotation, tilt (Roll-Pitch), X-Y axis movement) using the force of each current component derived through current vector control was proposed. Finally, the 6-DOF magnetic levitation system with the non-contact position sensors was fabricated and the validity of the 6-DOF magnetic levitation control method proposed in this paper was verified through a performance test using a prototype.

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The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable robots to perform dexterous grasping. Compared with 2D images, 3D point clouds based on objects can generate more reasonable and stable grasping poses. In this paper, we propose a new algorithm structure based on the PointNet network to process object point cloud information. First, we use the T-Net network to align the point cloud to ensure its rotation invariance; then we use a multilayer perceptron to extract point cloud characteristics and use the symmetric function to get global features, while adding the point cloud characteristics attention mechanism to make the network more focused on the object local point cloud. Finally, a grasp quality evaluation network is proposed to evaluate the quality of the generated candidate grasp positions, and the grasp with the highest score is obtained. A grasping dataset is generated based on the YCB dataset to train the proposed network, which achieves excellent classification accuracy. The actual grasping experiments are carried out using the Baxter robot and compared with the existing methods; the proposed method achieves good grasping effect.

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Six degree-of-freedom (6-DOF) posture measurement is an important academic research topic which has been broadly applied in many fields. As a high-speed photoelectronic sensor with ultra-high resolution and precision, position sensitive detector (PSD) has shown to be one of the most competitive candidates in 6-DOF measurement. This review presents the research progress of PSD-based 6-DOF posture measurement systems in the field of large-scale equipment assembly, ultra-precision manufacturing and other emerging areas. A total of six methods for implementing 6-DOF measurement are summarized and their advantages and limitations are discussed. Meanwhile, the paper illustrates challenges, potential solutions and future development trends.

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Whether load carriage leads to six-degrees-of-freedom (6DOF) knee kinematic alterations remains unclear. Exploring this mechanism may reveal meaningful knee kinematic information that can be used to improve load carriage conditions, the design of protective devices, and the knowledge of the effects of load carriage on knees. We recruited 44 subjects to explore kinematic alterations from an unloaded state to 60% bodyweight (BW) load carriage. A three-dimensional gait analysis system was used to collect the knee kinematic data. One-way repeated analysis of variance (ANOVA) was used to explore the effects of load levels on knee kinematics. The effects of increasing load levels on knee kinematics were smooth with decreased or increased trends. We found that knees significantly exhibited increased lateral tibial translation (up to 1.2 mm), knee flexion angle (up to 1.4°), internal tibial rotation (up to 1.3°), and tibial proximal translation (up to 1.0 mm) when they went from an unloaded state to 60%BW load carriage during the stance phase (p < 0.05). Significant small knee adduction/abduction angle and posterior tibial translation alterations (<1°/mm) were also identified (p < 0.05). Load carriage can cause significant 6DOF knee kinematic alterations. The results showed that knee kinematic environments are challenging during increased load. Our results contain kinematic information that could be helpful for knee-protection-related activities, such as target muscle training to reduce abnormal knee kinematics and knee brace design.

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Background and purpose: This study evaluated translational and rotational intra-fractional patient movement during spinal stereotactic body radiotherapy (SBRT) using 6D positioning based on 3D cone beam computerized tomography (CBCT) and stereoscopic kilovoltage imaging (ExacTrac). The aim was to determine whether additional intra-fractional image verification reduced intra-fractional motion without significantly prolonging treatment time, whilst maintaining acceptable imaging related dose. Materials and methods: A retrospective analysis of 38 patients with 41 primary tumour volumes treated with SBRT between September 2018 and May 2021 was performed. Three different image-guided radiotherapy (IGRT) workflows were assessed. The translational and rotational positioning errors for the different imaging workflows, 3D translational vectors and estimates of imaging dose delivered for the different imaging workflows were evaluated. Results: As the frequency of intra-fractional imaging increased from workflow 1 to 3, the mean intra-fraction 3D translational vector improved from 0.91 mm (±0.52 mm), to 0.64 (±0.34 mm). 85 %, 83 % and 97 % of images were within a tolerance of 1 mm/1° for workflows 1, 2 and 3 respectively, based on post treatment CBCT images. The average treatment time for workflow 3 was 13 min, as compared to 12 min for workflows 1 and 2. The effective dose per treatment for IGRT workflows 1, 2 and 3 measured 0.6 mSv, 0.95 mSv and 1.8 mSv respectively. Conclusion: The study demonstrated that the use of additional intra-fractional stereoscopic kilovoltage image-guidance during spinal SBRT, reduced the number of measurements deemed "out of tolerance" and treatment delivery could be optimized within a standard treatment timeslot without applying substantial additional radiation dose.

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Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human-machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1-3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.