Markerless Respiratory Motion Tracking Using Single Depth Camera

The aim of this study is to propose a novel system that has an ability to detect intra-fractional motion during radiotherapy treatment in real-time using three-dimensional surface taken by a depth camera, Microsoft Kinect v1. Our approach introduces three new aspects for three-dimensional surface tracking in radiotherapy treatment. The first aspect is a new algorithm for noise reduction of depth values. Ueda’s algorithm was implemented and enabling a fast least square regression of depth values. The second aspect is an application for detection of patient’s motion at multiple points in thracoabdominal regions. The third aspect is an estimation of three-dimensional surface from multiple depth values. For evaluation of noise reduction by Ueda’s algorithm, two respiratory patterns are measured by the Kinect as well as a laser range meter. The resulting cross correlation coefficients between the laser range meter and the Kinect were 0.982 for abdominal respiration and 0.995 for breath holding. Moreover, the mean cross correlation coefficients between the signals of our system and the signals of Anzai with respect to participant’s respiratory motion were 0.90 for thoracic respiration and 0.93 for abdominal respiration, respectively. These results proved that the performance of the developed system was comparable to existing motion monitoring devices. Reconstruction of three-dimensional surface also enabled us to detect the irregular motion and breathing arrest by comparing the averaged depth with predefined threshold values.

Reliability of Passive Physical Activity Assessment for Inpatients Using Marker⁃Free Motion Tracking System

Evidence⁃based practices of public health will benefit from quantification of passive physical activity assessment.This study aims to investigate the reliability of marker⁃free system(MFS)such as Microsoft Kinect in measuring upper extremity motion from different angles.Ten healthy participants performed elbow and shoulder extension/flexion along frontal and median anatomical planes for ten pace⁃controlled repetitions,during which the spatiotemporal positions of upper extremity joints were concurrently recorded by two sensors from 0°and 45°viewing angles.Reliability between the two sensors were evaluated using Pearson correlation coefficient,intra⁃class correlation coefficients,and 95%limits of agreement and coefficient of variation.Worse reliability was observed when possibility of occlusion was higher.However,better reliability was found when longer observation interval(10 s)was used as elementary measuring unit than shorter observation interval(2 s).The overall angular reliability of activity as displacement or changes in angle was not satisfactory.The results are expected to inform the industry for the extension of MFS to clinic applications.

Motion Tracking with Fast Adaptive Background Subtraction

To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.

Aye-aye middle finger kinematic modeling and motion tracking during tap-scanning

The aye-aye(Daubentonia madagascariensis)is a nocturnal lemur native to the island of Madagascar with a unique thin middle finger.Its slender third digit has a remarkably specific adaptation,allowing them to perform tap-scanning to locate small cavities beneath tree bark and extract wood-boring larvae from it.As an exceptional active acoustic actuator,this finger makes an aye-aye’s biological system an attractive model for pioneering Nondestructive Evaluation(NDE)methods and robotic systems.Despite the important aspects of the topic in the aye-aye’s unique foraging and its potential contribution to the engineering sensory,little is known about the mechanism and dynamics of this unique finger.This paper used a motion-tracking approach for the aye-aye’s middle finger using simultaneous video graphic capture.To mimic the motion,a two-link robot arm model is designed to reproduce the trajectory.Kinematics formulations were proposed to derive the motion of the middle finger using the Lagrangian method.In addition,a hardware model was developed to simulate the aye-aye’s finger motion.To validate the model,different motion states such as trajectory paths and joint angles,were compared.The simulation results indicate the kinematics of the model were consistent with the actual finger movement.This model is used to understand the aye-aye’s unique tap-scanning process for pioneering new tap-testing NDE strategies for various inspection applications.

Ultrasensitive graphene-Si position-sensitive detector for motion tracking

Motion tracking has attracted great attention in the fields of real-time tracking,nanorobotics,and targeted therapy.For achieving more accurate motion tracking,the highly sensitive position-sensitive detector(PSD)is desirable.Here,we demonstrate a meliorated PSD based on graphene-Si heterojunction for motion tracking.The position sensitivity of PSD was improved by employing surface engineering of graphene.Through modulating the transport property of graphene,nearly 20-fold increase of sensitivity was achieved under weak light,and at the same time,the detection limit power was reduced to^2 nW.A motion tracking system was developed based on the improved PSD,and human arm swing was tracked,which demonstrated high sensitivity and real-time tracking capabilities of the PSD.In addition,the PSD can support up to^10 kHz high-frequency tracking.This work provides a new strategy for improving the performance of PSD,and promotes the development of two-dimensional materials in novel optoelectronic devices.

Inertial Motion Tracking on Mobile and Wearable Devices:Recent Advancements and Challenges

Motion tracking via Inertial Measurement Units(IMUs)on mobile and wearable devices has attracted significant interest in recent years.High-accuracy IMU-tracking can be applied in various applications,such as indoor navigation,gesture recognition,text input,etc.Many efforts have been devoted to improving IMU-based motion tracking in the last two decades,from early calibration techniques on ships or airplanes,to recent arm motion models used on wearable smart devices.In this paper,we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices.We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

Tracking controller for robot manipulators viac omposite nonlinear feedback law

A composite nonlinear feedback tracking controller for motion control of robot manipulators is described. The structure of the controller is composed of a composite nonlinear feedback law plus full robot nonlinear dynamics compensation. The stability is carried out in the presence of friction. The controller takes advantage of varying damping ratios induced by the composite nonlinear feedback control, so the transient performance of the closed-loop is remarkably improved. Simulation results demonstrate the feasibility of the proposed method.

Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.

Review of Emerging Technologies in Pelvic Floor Ultrasonography

Pelvic floor dysfunction is a common morbidity with a negative impact on quality of life. These disorders include multiple clinical conditions which range from urinary and defecatory disorders to sexual disorders, affecting 24% of women. Since the pelvic floor is one of the most complex regions in the human body, in order to perform an accurate diagnosis, it is important to combine history taking, physical examination and imaging. While in the past, diagnosis of pelvic floor dysfunction was done using history taking and physical examination alone, it had been recognized the need for imaging as well. In the last decades different imaging modalities have been in use, including magnetic resonance imaging and computerized tomography scanning and, nowadays, the use of ultrasonography is gaining popularity. Ultrasound technology is evolving, with technology for 3D, 4D, Doppler and more, making it optimal for pelvic floor imaging. In this paper we review the different ultrasound modalities for pelvic floor imaging. The purpose of this review is to introduce the emerging ultrasound technologies for pelvic floor imaging including volume render mode, fusion imaging, framing, motion tracking and color vector mapping and elastography. The different ultrasonography modalities have resulted to be very useful for the diagnosis and assessment of pelvic floor dysfunctions, they are characterized by availability, short time, low cost, and radiation free. However, the effectiveness of the analysis is operator-dependent.

The optimization method of industrial robot arm structure based on green manufacturing technology

In order to realize the optimal design of the industrial robot arm structure,an optimization method of the industrial robot arm structure based on green manufacturing technology is proposed.The stability of arm structure parameter acquisition can be controlled.The quantitative adjustment model of structural optimization parameters is constructed.The differential fusion control of the arm structure is realized.This paper analyzes the structure parameter law of the robot arm.We use dynamic parameter prediction and output torque parameter compensation method to control the arm structure.According to the adaptive iterative processing results,the arm structure parameter identification is realized.According to the identification results,the cutting parameter optimization method is adopted for the analytical control of the arm structure,and finally the optimized design of the industrial robot arm structure is realized through the green manufacturing technology.The simulation test results show that for the accuracy of the industrial robot arm structure design,this method is better,the output stability is higher,and the arm motion trajectory has a low deviation from the actual motion trajectory,which improves the optimization control and design capabilities of the industrial robot arm structure.

Adaptive mixture observation models for multiple object tracking

Multiple object tracking （MOT） poses many difficulties to conventional well-studied single object tracking （SOT） algorithms, such as severe expansion of configuration space, high complexity of motion conditions, and visual ambiguities among nearby targets, among which the visual ambiguity problem is the central challenge. In this paper, we address this problem by embedding adaptive mixture observation models （AMOM） into a mixture tracker which is implemented in Particle Filter framework. In AMOM, the extracted multiple features for appearance description are combined according to their discriminative power between ambiguity prone objects, where the discriminability of features are evaluated by online entropy-based feature selection techniques. The induction of AMOM can help to surmount the incapability of conventional mixture tracker in handling object occlusions, and meanwhile retain its merits of flexibility and high efficiency. The final experiments show significant improvement in MOT scenarios compared with other methods.

A human hybrid tracking and localization method for mixed reality simulation of complex system

In order to obtain stable and high-precision motion tracking in mixed reality(MR)simulation of the complex system,an optical-inertial hybrid tracking method is proposed,which combines the accuracy of optical motion capture equipment and the robustness of inertial motion capture equipment,and can reduce the occlusion problem of entity scene on the premise of ensuring the localization accuracy.The method has been applied to the ergonomic evaluation of large civil aircraft development processes,and the effectiveness and usability of the method have been verified.