Mean Angle Error Research Articles

Improving standard volar plate fixation in 3D-guided corrective osteotomy of the distal radius: evaluation of a shim instrument.

Standard volar plates often do not fit the surface of the malunited distal radius after osteotomy, necessitating an offset angle for accurate volar tilt correction. The correction can be achieved if the plate is held at the correct angle when the distal screws are locked. With the advantage of 3D surgical planning and patient-specific instruments, we developed a shim instrument to assist the surgeon in securing the plate at the intended angle when locking the distal screws, and evaluated radiological results. Five female patients aged 63-74 with dorsally angulated extra-articular malunions underwent surgery using 3D-printed guides and the shim instrument. The plate position, drilling guide alignment, screw placements, and distal radius correction on postoperative CTs were compared with the surgical plans. Errors were measured using an anatomical coordinate system, and standard 2D radiographic measures were extracted. Preoperative dorsal tilt ranged from 16° to 35°, and postoperative volar tilt from 1° to 11°. 3D analysis revealed mean absolute correction errors of 6.1° in volar tilt, 1.6° in radial inclination, and 0.6 mm in ulnar variance. The volar tilt error due to the shim instrument, indicated by the mean angle error of the distal screws to the plate, was 2.1° but varied across the five patients. Settling of the distal radius, due to tension during and after reduction, further contributed to a mean loss of 3.5° in volar tilt. The shim instrument helped with securing plates at the intended angle; however, further correction improvements should consider the tension between the fragments of osteoporotic bone.

Angular Position Sensor Based on Anisotropic Magnetoresistive and Anomalous Nernst Effect

Magnetic position sensors have extensive applications in various industrial sectors and consumer products. However, measuring angles in the full range of 0–360° in a wide field range using a single magnetic sensor remains a challenge. Here, we propose a magnetic position sensor based on a single Wheatstone bridge structure made from a single ferromagnetic layer. By measuring the anisotropic magnetoresistance (AMR) signals from the bridge and two sets of anomalous Nernst effect (ANE) signals from the transverse ports on two perpendicular Wheatstone bridge arms concurrently, we show that it is possible to achieve 0–360° angle detection using a single bridge sensor. The combined use of AMR and ANE signals allows a mean angle error in the range of 0.51–1.05° within a field range of 100 Oe–10,000 Oe to be achieved.

Intelligent electromagnetic navigation system for robot-assisted intraoral osteotomy in mandibular tumor resection: a model experiment.

Mandibular tumor surgery necessitates precise osteotomies based on tumor boundaries; however, conventional osteotomies often lack accuracy in predicting osteotomy positions and planes, potentially leading to excessive resection of normal bone tissues or residual tumors, thus compromising postoperative quality of life and clinical outcomes. Robotic-assisted surgery (RAS) augmented with artificial intelligence (AI) offers precise localization capabilities, aiding surgeons in achieving accurate osteotomy positioning. This study aimed to evaluate the feasibility and accuracy of a robotic magnetic navigation system for positioning and osteotomy in an intraoral surgical trial of a mandibular tumor model. Patient computed tomography (CT) imaging data of mandibular chin and body tumors were utilized to create 3D printed models, serving as study subjects for mandibular tumor resection. Ten pairs of models were printed for the experimental and control groups. The experimental group (EG) underwent osteotomy using a robot-assisted surgical navigation system, performing osteotomy under robotic navigation following alignment based on preoperative design. The control group (CG) underwent traditional surgery, estimating osteotomy position empirically according to preoperative design. Postoperative CT scans were conducted on both models, and actual postoperative results were compared to preoperative design. Osteotomy accuracy was evaluated by positional and angular errors between preoperatively designed and actual osteotomy planes. For ten randomly selected spots on the left and right sides, respectively, the EG group had mean distance errors of 0.338mm and 0.941mm. These values were obtained from the EG group. In the EG group, on the left side, the mean angular errors were 14.741 degrees, while on the right side, they were 13.021 degrees. For the 10 randomly selected spots on the left and right sides, respectively, the CG had mean distance errors of 1.776mm and 2.320mm. This is in contrast to the results obtained by the EG. It was determined that the left side had a mean angle error of 16.841 degrees, while the right side had an error of 18.416 degrees in the CG group. The above results indicated significantly lower point errors of bilateral osteotomy planes in the experimental group compared to the control group. This study demonstrates the feasibility of electromagnetic navigation robot-assisted intraoral osteotomy for mandibular tumors and suggests that this approach can enhance the precision of clinical surgery.

Deep learning-based estimation of whole-body kinematics from multi-view images

It is necessary to analyze the whole-body kinematics (including joint locations and joint angles) to assess risks of fatal and musculoskeletal injuries in occupational tasks. Human pose estimation has gotten more attention in recent years as a method to minimize the errors in determining joint locations. However, the joint angles are not often estimated, nor is the quality of joint angle estimation assessed. In this paper, we presented an end-to-end approach on direct joint angle estimation from multi-view images. Our method leveraged the volumetric pose representation and mapped the rotation representation to a continuous space where each rotation was uniquely represented. We also presented a new kinematic dataset in the domain of residential roofing with a data processing pipeline to generate necessary annotations for the supervised training procedure on direct joint angle estimation. We achieved a mean angle error of 7.19° on the new Roofing dataset and 8.41° on the Human3.6M dataset, paving the way for employment of on-site kinematic analysis using multi-view images.

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.

In-Lab Demonstration of an Underwater Acoustic Spiral Source.

Underwater acoustic spiral sources can generate spiral acoustic fields where the phase depends on the bearing angle. This allows estimating the bearing angle of a single hydrophone relative to a single source and implementing localization equipment, e.g., for target detection or unmanned underwater vehicle navigation, without requiring an array of hydrophones and/or projectors. A spiral acoustic source prototype made out of a single standard piezoceramic cylinder, which is able to generate both spiral and circular fields, is presented. This paper reports the prototyping process and the multi-frequency acoustic tests performed in a water tank where the spiral source was characterized in terms of the transmitting voltage response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and showed a maximum angle error of 3° when the calibration and the operation were carried out in the same conditions and a mean angle error of up to 6° for frequencies above 25 kHz when the same conditions were not fulfilled.

Geometric algebra-based multiview interaction networks for 3D human motion prediction

3D skeleton-based human motion prediction is an essential and challenging task for human-machine interactions, which aims to forecasts future poses given a history of their previous motions. Recent works based on Graph Neural Networks (GCNs) show promising performance for motion prediction due to the powerful ability of feature aggregation of GCNs. However, with the deep and multi-stage GCN model deployment, its feature extraction mechanism tends to result in feature similarity over all joints, and degrade the prediction performance. In addition, such a graph structure in recent works was still insufficient to process the high dimensional structural data in Euclidean space when inference through multi-layer networks. To solve the problem, we propose a novel Geometric Algebra-based Multi-view Interaction network (GA-MIN), which captures and aggregates motion features from two interactions: 1) global-interaction, which refactors various spectrum dependencies using geometric algebra-based structure, and 2) self-interaction, which leverage self-attention mechanism to capture compact representations. Extensive experiments are conducted on three public datasets: Human3.6M, CMU Mocap, and 3DPW, which prove that the proposed GA-MIN outperforms state-of-the-art methods on 3D Mean Per Joint Position Error (MPJPE) and Mean Angle Error (MAE) on average.

High Accuracy, 6-DoF Simultaneous Localization and Calibration Using Visible Light Positioning

Benefiting from the development of image sensors and the popularity of light-emitting diode (LED) lighting technology, visible light positioning (VLP) technology based on image sensors has ushered in vigorous development and broad prospects, which can provide low-cost and high-accuracy position service. However, the existing approaches require dense LEDs or sensors such as gyroscopes to assist positioning, which limits the area and lower the accuracy of positioning because of the errors from imperfect sensors. In this article, we propose a simultaneous localization and calibration VLP method based on double coplanar circular LED lights aiming to get rid of the dependence on additional sensors and dense LED transmitters. By the pinhole camera model and the perspective projection of circle, our proposed method extends the available position area and relaxes the required quantity of LED to two. The experiment result showed that our system has a mean 3D positioning accuracy of 7.91cm, a mean angle error of less than 1.6°, and an average latency of 182ms on mobile devices.

Accuracy and precision of the measurement of liner orientation of dual mobility cup total hip arthroplasty using ultrasound imaging.

The Dual Mobility Cup (DMC) was created in 1974 to prevent dislocation and decrease wear. However, the movement of the polyethylene liner in vivo remains unclear. The aims of this study were to visualise liner positions and quantify the accuracy of the liner plane orientation for static positions, using ultrasound imaging. DMC reconstruction and angle between cup and liner were evaluated on isolated submerged DMCs by comparing 3D laser scans and ultrasound imaging. Moreover, the abduction and anteversion angles of the liner plane relative to the pelvis orientation were calculated via combined motion analysis and 3D ultrasound imaging on four fresh post-mortem human subjects with implanted DMC. On submerged DMC, the mean angle error between ultrasound imaging and 3D scan was 1.2°. In cadaveric experiments, intra-operator repeatability proved satisfactory, with low range value (lower than 2°) and standard deviation (lower than 1°). The study demonstrates the feasibility of measuring liner orientation on submerged and ex vivo experiments using ultrasound imaging, and is a first step towards in vivo analysis of DMC movement.

Spatio-temporal aggregation of skeletal motion features for human motion prediction

This study proposes a human body motion prediction model that can adapt to the disorders in various human motion patterns and represent the kinematic constraints. In human motion prediction, the acquisition of features that capture inter-motion and inter-joint linkages is considered effective. To generate links that are adaptive to the reference time of dominant features and their crossing-over joints, we construct an attention-based network that aggregates motion sequences temporally and spatially. We evaluated the motion prediction results using the Human3.6M dataset with the indices of mean angle error and mean per joint position error and showed that our method outperforms other state-of-the-art methods.

A dual attention model based on probabilistically mask for 3D human motion prediction

Previous work based on attention and Graph Convolutional Network (GCN) have been shown impressive performance in 3D human motion prediction. We believe that 3D human motion prediction problem can be explained as a modeling task of understanding and synthesizing human poses based on learning sub-sequences of historical moments. However, existing methods fail to model the observation that human motion can be learned from historical state. In this paper, we propose a dual attention architecture, optimizing the utilization of historical sequences in two aspects. First, we introduce the local pre-attention based on temporal autocorrelation inside each sub-sequence for the current and historical sub-sequences. Specifically, our research uses the dependence of each moment and its several historical moments to access and capture past information selectively, generating reasonable historical time sub-sequence representation, which is a kind of generative sampling model matching with scores. Then, the current and historical sub-sequences are embedded into global attention to obtain the historical information that contributes the most to the observed sub-sequence. On the other hand, instead of using simple GCN with over-smoothing problem, We extend GCN to a multi-layer residual graph structure. The 3D mean per joint position error (MPJPE) and the mean angle error (MAE) experiments on the datasets of Human3.6 M and 3DPW prove that our model can obtain more accurate prediction results than previous methods. Our code is available at https://github.com/lishuangshuang2022/DANet.

Smartphone-derived 3D head and pinnae modeling for personalized 3D virtual audio

3D audio is commonplace augmented or virtual reality environments, and is used simulate the direction of arrival for sounds presented over headphones. The Head-Related Transfer Function (HRTF) enables 3D audio by characterizing how sound is received from a sound source in every direction. Optimal sound localization performance with HRTF-based 3D audio is thought to require personalized HRTFs because the localization cues are dependent on the size and shape of the head and pinnae. In this work, we propose a prototype pipeline to generate HRTFs from high resolution smartphone videos of the head and ears, and we validate the localization performance of the resulting HRTFs through human-subject behavioral experiments. To create a personalized 3D HRTF, a video was taken of a subject’s torso, left ear and right ear using a smartphone camera. Acoustic simulation with Boundary Element Modeling was performed in COMSOL, taking an approximately 1 h to complete. Preliminary virtual sound localization results were obtained on a pilot subject for each of 3 different HRTF types: A generic HRTF, a gold standard acoustically measured HRTF, and the smartphone video HRTF. We found a mean angle error of 36.6, 21.1, and 14.0 deg, respectively, indicating that the smartphone-based HRTF developed in this study provides higher localization accuracy than other methods on this pilot subject. Further testing will add additional subjects, and compare to free-field localization over loudspeakers.

I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance- based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject- independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems.

Monocular Visual Position and Attitude Estimation Method of a Drogue Based on Coaxial Constraints.

In aerial refueling, there exists deformation of the circular feature on the drogue’s stabilizing umbrella to a certain extent, which causes the problem of duality of position estimation by a single circular feature. In this paper, a monocular visual position and attitude estimation method of a drogue is proposed based on the coaxial constraints. Firstly, a procedure for scene recovery from one single circle is introduced. The coaxial constraints of the drogue are proposed and proved to be useful for the duality’s elimination by analyzing the matrix of the spatial structure. Furthermore, we came up with our method, which is composed of fitting the parameters of the spatial circles by restoring the 3D points on it, using the two-level coaxial constraints to eliminate the duality, and optimizing the normal vector of the plane where the inner circle is located. Finally, the effectiveness and robustness of the method proposed in this paper are verified, and the influence of the coaxial circle’s spatial structure on the method is explored through simulations of and experiments on a drogue model. Under the interference of a large amount of noise, the duality elimination success rate of our method can also be maintained at a level that is more than 10% higher than others. In addition, the accuracy of the normal vector obtained by the fusion algorithm is improved, and the mean angle error is reduced by more than 26.7%.

Automatic analysis system of calcaneus radiograph: Rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation

Background and objectiveCalcaneus is the largest tarsal bone to withstand the daily stresses of weight-bearing. The calcaneal fracture is the most common type in the tarsal bone fractures. After a fracture is suspected, plain radiographs should be taken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measured by four anatomic landmarks in lateral foot radiograph, can guide fracture diagnosis and facilitate operative recovery of the fractured calcaneus. This study aims to develop an analysis system that can automatically locate four anatomic landmarks, measure BA and CAG for fracture assessment, identify fractured calcaneus, and segment fractured regions. MethodsFor landmark detection, we proposed a coarse-to-fine Rotation-Invariant Regression-Voting (RIRV) landmark detection method based on regressive Multi-Layer Perceptron (MLP) and Scale Invariant Feature Transform (SIFT) patch descriptor, which solves the problem of fickle rotation of calcaneus. By implementing a novel normalization approach, the RIRV method is explicitly rotation-invariance comparing with traditional regressive methods. For fracture identification and segmentation, a convolution neural network (CNN) based on U-Net with auxiliary classification head (U-Net-CH) is designed. The input ROIs of the CNN are normalized by detected landmarks to uniform view, orientation, and scale. The advantage of this approach is the multi-task learning that combines classification and segmentation. ResultsOur system can accurately measure BA and CAG with a mean angle error of 3.8○ and 6.2○ respectively. For fracture identification and fracture region segmentation, our system presents good performance with an F1-score of 96.55%, recall of 94.99%, and segmentation IoU-score of 0.586. ConclusionA powerful calcaneal radiograph analysis system including anatomical angles measurement, fracture identification, and fracture segmentation can be built. The proposed analysis system can aid orthopedists to improve the efficiency and accuracy of calcaneus fracture diagnosis.

The Path Direction Control System for Lanange Jagad Dance Robot Using the MPU6050 Gyroscope Sensor

The ability to walk straight on a dance robot is very important considering that in competitions, dance robots are required to be able to walk through several zones starting from the starting zone and ending with the closed zone. Therefore, a control system is needed in the Lanange Jagad dance robot so that the robot can control the direction of its walking motion and reduce errors in dance motion while walking on the dance robot. This control system uses a reading value based on the orientation of the rotating motion on the yaw angle axis on the MPU6050 gyroscope sensor which will later be used as a corrector for dance robots when performing various dance movements while walking in the competition arena. From the results of the overall test of the Lanange Jagad dance robot after adding the road direction control system, the percentage of the success rate in the battery power supply condition is 12 volts to 12.6 volts by 100% with the greater the battery power supply, the error in the robot's final angle average to The starting angle of the robot is getting smaller and the percentage of the success rate at the slope of the 0o to 4o race arena is 93.3%. With the tilted race arena, the error in the mean error of the robot's final angle to the starting angle of the robot is also greater, so it can be concluded that the robot can be controlled direction of walking and can walk straight to the finish in the closed zone.

How accurately can surgeons perform angle manipulation? Quantitative assessment of the accuracy of manual angle manipulation of orthopedic surgery: a cadaver study

To date, only few studies have been performed on the accuracy of manual angle manipulation during orthopedic surgery. This cadaver study was aimed at quantitatively assessing the accuracy of manual angle manipulation performed by orthopedic surgeons according to their surgical experience and comparing it with manipulation performed with the assistance of a digital goniometer. Six lower-leg specimens of fresh-frozen human cadavers were subjected to angle manipulation performed via Kirschner wire (K-wire) insertion. K-wires were inserted manually and with the assistance of a digital goniometer at target angles of 0°, 30°, and 60° by three operators who had different levels of experience in orthopedic surgery. The accuracy of the insertion angles at the target angles was evaluated using computed tomography. The mean angle error in the manual angle manipulation was 8.8° (standard deviation [SD] 6.0). When the target angles were set to 0°, 30°, and 60°, the identified angle errors were 6.1° (SD 4.3), 8.8° (SD 6.6), and 11.7° (SD 5.6), respectively, and each value did not show any significant difference among the operators. With the assistance of a digital goniometer, the mean (SD) angle error was significantly improved to 2.1° (1.1°) (p < 0.001). The amount of improvement in accuracy significantly increased as the target angle increased (p = 0.01). This cadaver study quantified the inaccuracy of manual angle manipulation in orthopedic surgery and showed that these inaccuracies ​​can be improved using an assistive device. These results support the need to develop a device that can compensate manual angle manipulation in orthopedic surgery.

Markerless robotic pedicle screw placement based on structured light tracking.

Most existing robot systems for pedicle screw placement rely on optical markers to establish the spatial relationship between the surgical tool and the surgical path. Marker installation and registration are time-consuming, and error may also accumulate along the complicated coordinate transformation chain. Therefore, we proposed a markerless structured light-based method to simplify the surgery workflow and the coordinate transformation chain. Firstly, a structured light camera is used to directly track both the surgical tool and the bone anatomy without using markers. Secondly, a markerless "two-direction" approach for robot-camera registration together with a feedback robot control method is developed. Lastly, a prototype system is built and examined with precision validation experiments and pedicle screw drilling experiments. Precision validation experiments show satisfactory positioning accuracy of the system. In drilling experiments, 42 paths were drilled on three synthesized cervical vertebrae phantoms and all the paths successfully went through the pedicles. The mean position error of the entry point was 0.28 ± 0.16mm, and the mean angle error was 0.49 ± 0.24°, which can meet the clinical requirement. The results show the feasibility of the proposed structured light-based method for pedicle screw placement, which has a simple workflow and can achieve good accuracy without using optical markers.

A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

Complex intra-fractional motion and deformation of the liver significantly impacts the accuracy of delivered dose in radiotherapy. It limits margin reduction, dose escalation and normal tissue sparing. A critical component of motion management is to accurately reconstruct tumor motion. In this study, we developed a six degrees of freedom projection marker matching method (6-DoF PM3) to reconstruct translational and rotational liver tumor motion in a rotational treatment delivery, such as volumetric modulated arc therapy (VMAT). Specifically, we modeled the use of two gold markers implanted in a linear form. The four endpoints of the two gold linear markers were used as tracking surrogates. During delivery, kV x-ray projection images were acquired. A method was developed to automatically identify the 2D marker-endpoints on the projection images. 3D marker positions were determined by solving an optimization problem with the objective function penalizing the distance from the reconstructed 3D position of each fiducial marker endpoint to the corresponding straight line defined by the kV x-ray projection of the endpoints. We performed a series of tests to evaluate different components of the method. For 2D marker endpoints identification, 99.9% of the marker endpoints were identified with an error (1 pixel) along both u and v directions. For 3D reconstruction of motion in simulation studies, error of rotational angle was ° without considering the 2D marker identification error. The rotational angle error was relatively sensitive to the accuracy of 2D marker identification. When the 2D error raised from 0.22 mm to 0.776 mm, the error of 3D rotational angle increased from 0.5° to 2.5°. In the experimental end-to-end tests, the mean root-mean square error of the 3D reconstructed marker positions was 0.75 mm and the mean error of rotational angle was within 1.7°. Our method can accurately determine intra-fractional liver tumor motion in rotational radiotherapy using kV projections of only two linear fiducial markers.

Augmented reality surgical navigation with accurate CBCT-patient registration for dental implant placement.

It is challenging to achieve high implant accuracy in dental implant placement, because high risk tissues need to be avoided. In this study, we present an augmented reality (AR) surgical navigation with an accurate cone beam computed tomography (CBCT)-patient registration method to provide clinically desired dental implant accuracy. A registration device is used for registration between preoperative data and patient outside the patient's mouth. After registration, the registration device is worn on the patient's teeth for tracking the patient. Naked-eye 3D images of the planning path and the mandibular nerve are superimposed onto the patient in situ to form an AR scene. Simultaneously, a 3D image of the drill is overlaid accurately on the real one to guide the implant procedure. Finally, implant accuracy is evaluated postoperatively. A model experiment was performed by an experienced dentist. Totally, ten parallel pins were inserted into five 3D-printed mandible models guided by our AR navigation method and through the dentist's experience, respectively. AR-guided dental implant placement showed better results than the dentist's experience (mean target error = 1.25mm vs. 1.63mm; mean angle error = 4.03° vs. 6.10°). Experimental results indicate that the proposed method is expected to be applied in the clinic. Graphical abstract ᅟ.