Upper-limb paralysis requires extensive rehabilitation to recover functionality for everyday living, and such assistance can be supported with robot technology. Against such a background, we have proposed an electromyography (EMG)-driven hybrid rehabilitation system based on motion estimation using a probabilistic neural network. The system controls a robot and functional electrical stimulation (FES) from movement estimation using EMG signals based on the user\'s intention, enabling intuitive learning of joint motion and muscle contraction capacity even for multiple motions. In this study, hybrid and visual-feedback training were conducted with pointing movements involving the non-dominant wrist, and the motor learning effect was examined via quantitative evaluation of accuracy, stability, and smoothness. The results show that hybrid instruction was as effective as visual feedback training in all aspects. Accordingly, passive hybrid instruction using the proposed system can be considered effective in promoting motor learning and rehabilitation for paralysis with inability to perform voluntary movements.

The goal of this paper is twofold: firstly, to provide a novel mathematical model that describes the kinematic chain of motion of the human fingers based on Lagrangian mechanics with four degrees of freedom and secondly, to estimate the model parameters using data from able-bodied individuals. In the literature there are a variety of mathematical models that have been developed to describe the motion of the human finger. These models offer little to no information on the underlying mechanisms or corresponding equations of motion. Furthermore, these models do not provide information as to how they scale with different anthropometries. The data used here is generated using an experimental procedure that considers the free response motion of each finger segment with data captured via a motion capture system. The angular data collected are then filtered and fitted to a linear second-order differential approximation of the equations of motion. The results of the study show that the free response motion of the segments is underdamped across flexion/extension and ad/abduction.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.

OBJECTIVE: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-encoded FID navigator) in each radial spoke.
METHODS: A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements.
RESULTS: Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results.
CONCLUSIONS: Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.

UNASSIGNED: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning.
UNASSIGNED: Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity.
UNASSIGNED: The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%.
UNASSIGNED: Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such \'anti-directional turning\' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

Motion capture systems have enormously benefited the research into human-computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge-Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs.

Ultrasonic echocardiography is commonly used for monitoring myocardial dysfunction. However, it has limitations such as poor quality of echocardiography images and subjective judgment of doctors.
In this paper, a calculation model based on optical flow tracking of echocardiogram is proposed for the quantitative estimation motion of the segmental wall. To improve the accuracy of optical flow estimation, a method based on confidence-optimized multiresolution(COM) optical flow model is proposed to reduce the estimation errors caused by the large amplitude of myocardial motion and the presence of \"shadows\" and other image quality problems. In addition, motion vector decomposition and dynamic tracking of the ventricular region of interest are used to extract information regarding the myocardial segmental motion. The proposed method was validated using simulation images and 50 clinical cases (25 patients and 25 healthy volunteers) for myocardial motion analysis.
The results demonstrated that the proposed method could track the motion information of myocardial segments well and reduce the estimation errors of optical flow caused due to the use of low-quality echocardiogram images.
The proposed method improves the accuracy of motion estimation for the cardiac ventricular wall.

Material models are required to solve continuum mechanical problems. These models contain parameters that are usually determined by application-specific test setups. In general, the theoretically developed models and, thus, the parameters to be determined become increasingly complex, e.g., incorporating higher-order motion derivatives, such as the strain or strain rate. Therefore, the strain rate behaviour needs to be extracted from experimental data. Using image data, the most-common way in solid experimental mechanics to do so is digital image correlation. Alternatively, optical flow methods, which allow an adaption to the underlying motion estimation problem, can be applied. In order to robustly estimate the strain rate fields, an optical flow approach implementing higher-order spatial and trajectorial regularisation is proposed. Compared to using a purely spatial variational approach of higher order, the proposed approach is capable of calculating more accurate displacement and strain rate fields. The procedure is finally demonstrated on experimental data of a shear cutting experiment, which exhibited complex deformation patterns under difficult optical conditions.