Motion Trajectory Information Research Articles

Design and Load Kinematics Analysis of Rollover Rehabilitation Mechanism Fitting Human Motion Curve.

Supine rollover plays an important role in the prevention of pressure sores in long-term bedridden patients. It is of great significance to study the mechanism of human supine rollover movement and to design the rehabilitation rollover mechanism in line with man-machine cooperation. In human supine rollover movement, shoulder and hip are the key parts of force application. Based on anatomical theory, the motion trajectory information of shoulder and hip skeletal rehabilitation parts is collected by combining optical motion capture and rigid body modeling. Following a kinematics simulation analysis, the simulation curve was compared with the experimental curve track; the numerical difference was small. It is proved that the simulation model is correct, and it is also shown that the designed rehabilitation rollover mechanism can better reproduce the natural rolling motion state of the human body. It can meet the requirements of human-machine synergistic assisted lateral roll rehabilitation aids and provides a solution for pressure sore prevention.

A Comprehensive overview of AR/VR by Writing in Air

The research is all about how often people could identify alphabets and numbers written in the open air. A leap motion captures motion trajectory information and plots it as a continuous stream of points. Lines would be combined and major slopes identified from the major points. Significant slopes are converted into directions by the use of geometry. Slope orientation is another name for this. Slope Alignment Sequence is a list of slope orientations that are used to individually depict Alphanumeric Letters. In this experiment, Dependency injection OpenCV is used to sketch on the camera with a virtual pen, i.e. any marker may be used to draw using the contour detection technique centered on the mask of the desired cultured reference marker. If you're a beginner who wants to enthuse your fellow members with a visual demonstration, the application user will be identified by the system until the produced slope direction sequence matches the system's comparison slope alignment sequence. In the algorithm's matching process, the sets and subsets process is used. The researchers suggested a new approach for classifying Alphabetic Chars written in the air in this article. The outcome of the experiment shows that the suggested approach is reliable.

A Crowd Behavior Identification Method Combining the Streakline With the High-Accurate Variational Optical Flow Model

A crowd behavior identification method is proposed by combining the streakline based on fluid mechanics with a high-accurate variational optical flow model in this paper. Firstly, calculated by the high-accurate variational optical flow model, the streaklines are used to acquire the crowd motion trajectory information. The angular histogram and the regions of interest in the scenes are obtained by calculating and clustering the dasymetric dot maps of the starting and ending points of the trajectory, and then, combining the dasymetric dot map and angular histogram information to analyze whether there are specific crowd behaviors in the regions of interest, and thus to identify different types of crowd behavior in such scene. Finally, experimental comparison and analysis are made to verify the effectiveness and accuracy of the method proposed in this paper.

Millimeter-Wave Radar and Machine Vision-Based Lane Recognition

Camera can sensor the environment on the lane by extracting the lane lines, but such detection is limited to a short distance with effect of illumination and other factors; radar can detect objects a long distance away but cannot detect the lane conditions. This paper combined machine vision with millimeter-wave radar and extracted the nearby distinct lane line through images; at the same time, the radar obtained the motion trajectory information of distant vehicles, then the least-square method was used to make curve fitting on those motion trajectory information in order to reconstruct the lane line information. Finally, in the stage of fusing two segments of lane lines, the goodness of fit was applied to complete the matching of corresponding lane lines. While, for areas between two segments of lane lines that neither camera or radar can detect, we established a lane model, utilized probabilistic neural network to select the corresponding lane model for matching, and then used approximate mathematics expression according to the selected lane model, thus obtaining the final front road information of current vehicle.

A system for accelerometer-based gesture classification using artificial neural networks.

A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.

Dense Trajectories and DHOG for Classification of Viewpoints from Echocardiogram Videos.

In echo-cardiac clinical computer-aided diagnosis, an important step is to automatically classify echocardiography videos from different angles and different regions. We propose a kind of echocardiography video classification algorithm based on the dense trajectory and difference histograms of oriented gradients (DHOG). First, we use the dense grid method to describe feature characteristics in each frame of echocardiography sequence and then track these feature points by applying the dense optical flow. In order to overcome the influence of the rapid and irregular movement of echocardiography videos and get more robust tracking results, we also design a trajectory description algorithm which uses the derivative of the optical flow to obtain the motion trajectory information and associates the different characteristics (e.g., the trajectory shape, DHOG, HOF, and MBH) with embedded structural information of the spatiotemporal pyramid. To avoid “dimension disaster,” we apply Fisher's vector to reduce the dimension of feature description followed by the SVM linear classifier to improve the final classification result. The average accuracy of echocardiography video classification is 77.12% for all eight viewpoints and 100% for three primary viewpoints.

Motion trajectory information and agency influence motor learning during observational practice

Fundamental to performing actions is the acquisition of motor behaviours. We examined if motor learning, through observational practice, occurs by viewing an agent displaying naturalistic or constant velocity, and whether motion trajectory, as opposed to end-state, information is required. We also investigated if observational practice is sensitive to belief regarding the origin of an agent. Participants had to learn a novel movement sequence timing task, which required upper-limb movements to a series of targets within a pre-specified absolute and relative time goal. Experiment 1 showed learning after viewing naturalistic and constant velocity, but not end-state information. For Experiment 2, in addition to learning the movement sequence, participants observed a series of movement stimuli that were either the trained or new sequences and asked to rate their confidence on whether the observed sequence was the same or different to observational practice. The results indicated that agency belief modulates how naturalistic and constant velocity is coded. This indicated that the processes associated with belief are part of an interpretative predictive coding system where the association between belief and observed motion is determined. When motion is constant velocity, or believed to be computer-generated, coding occurs through top-down processes. When motion is naturalistic velocity, and believed to be human-generated, it is most likely coded by gaining access to bottom-up sensorimotor processes in the action-observation network.

Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.

A temporal hand gesture recognition system based on hog and motion trajectory

A real-time, rapid and robust gesture recognition system is usually hindered by difficulty of hand localization and complexity of hand gesture modeling, especially under complex background. For eliminating these obstacles, in this paper, we propose a method using histograms of oriented gradients features (HOG) and motion trajectory information for temporal hand gesture recognition in natural environment. We firstly localize hand in video stream based on hand detection by HOG and support vector machine algorithm (SVM). After hand localization, the motion trajectory information of consecutive hand gesture is extracted and a database of standard gestures is built. Finally, the Mahalanobis distance between input gesture and database is computed for recognition. As the experimental results shown, our method exhibits a good performance in real-time test.

Detection of Magnetic Object Movements by Flux Density Tracking Control

In this paper, a motion trajectory detecting method that permits tracking of magnetic objects is investigated by the magnetic flux density of a 3-D Hall probe. Inaccurate motion trajectory information may be produced physically due to the measured magnetic field signal altering as the distance between the Hall probe and the object changes. A magnetic flux feedback control is proposed such that the motion command of the control system is generated in real time by the coordinated geometry of the Hall probe and the magnetic object. The magnetic tracking control system consists of an inner position/velocity control loop and an outer magnetic control loop that employs a position vector expressed in terms of the magnetic flux density to detect the relative distance between a magnetic object and the Hall probe. The measurement of the Hall probe can, hence, be kept within an effective distance such that the motion trajectory of the measured magnetic object can be detected accurately for the case of large movements. An experimental setup with a five-axis servo mechanism is constructed for the validation study, in which the three-axis moving table is utilized to control the motion of the Hall probe, and the other two-axis moving table facilitates the movement of the magnetic object. A circular motion and a rhomboidal path are given to verify the improvement of the proposed detecting method.

SU‐GG‐T‐162: Using Tumor Motion Trajectory as a Constraint for 4D‐IMRT Treatment Planning

Purpose: To develop a 4D‐IMRT treatment planning technique by adding a tumor motion trajectory (TMTJ) as a constraint in a commercially available Treatment Planning System (TPS). The goal is to create an IMRT plan with a set of MLC segments catering for the motion of the patient anatomy extracted from 4D‐CT images. Method and Materials: 4D‐CT images were obtained from three lung cancer patients. The 4D protocol divided the breathing cycle into ten phases followed by a Maximum Intensity Projection (MIP) volume reconstruction. ITVs were contoured based on the MIP volumes. GTVs were contoured on all ten CT phases to generate a TMTJ, defined as the position of the centroid of the GTV on all phases. A tube shaped contour (TBC) with about 1 cm diameter along the TMTJ and inside the GTV was generated. TBC was added to the objective function as a constraint with minimum dose‐volume histogram (DVH) set to prescribed dose. IMRT plans were generated by using Pinnacle DMPO with and without the TMTJ constraint. Results: The IMRT plans generated with TMTJ constraints optimized the MLC segment shapes and weights for each beam with the TBC being forced/encouraged to inside of the segmented fields. The percentage of total MUs delivered to the largest area segments in each beam increased from 16.4% of the total MUs to 21.5%. The MU weight increased up to 20% by using TMTJ constrain. The DVHs for PTV and ORs, dose distributions and total MUs for plans with/without TMTJ constraints were very similar. Conclusion: The 4D tumor motion trajectory information can be used as a constraint in the Pinnacle DMPO to obtain tempospatially optimized IMRT plans. The method shows the potential to significantly reducing the dosimetric inaccuracies introduced by the interplay between organ motion and multileaf collimator (MLC) motion for IMRT.

Model-based temporal object verification using video

An approach to model-based dynamic object verification and identification using video is proposed. From image sequences containing the moving object, we compute its motion trajectory. Then we estimate its three-dimensional (3-D) pose at each time step. Pose estimation is formulated as a search problem, with the search space constrained by the motion trajectory information of the moving object and assumptions about the scene structure. A generalized Hausdorff (1962) metric, which is more robust to noise and allows a confidence interpretation, is suggested for the matching procedure used for pose estimation as well as the identification and verification problem. The pose evolution curves are used to assist in the acceptance or rejection of an object hypothesis. The models are acquired from real image sequences of the objects. Edge maps are extracted and used for matching. Results are presented for both infrared and optical sequences containing moving objects involved in complex motions.