Objects In Three-dimensional Space Research Articles

THE METHOD OF OBSERVING MOVING OBJECTS

The article analyzes known algorithms for tracking moving objects. Based on an analysis of known algorithms for tracking moving objects, it was concluded that the best tracking quality in problems with a large number of observed objects is achieved by solutions built on the basis of probabilistic and hierarchical methods. Each of them has complementary advantages, which creates prospects for creating new algorithmic solutions built on the synergy of these approaches. The main task of promising tracking methods is that they should provide ease of scaling with an increase in the number of moving objects that need to be monitored, localize objects in three-dimensional space, and also be able to work with heterogeneous sensors. This approach has both purely technical advantages and those related to the availability of microelectronics components in modern geopolitical realities.

Design of Badminton Technical Movement Recognition System Based on Improved Agnes Algorithm

The Badminton Technical Movement Recognition System is a technology-driven solution aimed at identifying and analyzing various technical movements performed by badminton players during gameplay. Leveraging advanced sensors, motion tracking devices, and machine learning algorithms, this system captures and interprets data related to player movements, racket swings, footwork, and other key actions on the court. By analyzing this data in real-time or post-match, coaches, players, and analysts can gain valuable insights into performance, technique, and areas for improvement. The system's ability to recognize and quantify specific movements allows for detailed performance assessment, personalized training programs, and strategic game planning. The Badminton Technical Movement Recognition System serves as a powerful tool for enhancing player development and optimizing performance in the sport of badminton. The paper presents a comprehensive study on the application of the Multi-Modal AGNES algorithm across diverse domains, encompassing movement pattern estimation, modal feature extraction, coordinate estimation, and badminton feature estimation. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. For instance, the algorithm achieved an average accuracy of 90% in classifying movement patterns in a dataset of 1000 observations. Additionally, it accurately estimated modal features such as swing speed, racket angle, and shuttlecock speed with a mean error rate of less than 5%.

Equal Emphasis on Data and Network: A Two-Stage 3D Point Cloud Object Detection Algorithm with Feature Alignment

Three-dimensional object detection is a pivotal research topic in computer vision, aiming to identify and locate objects in three-dimensional space. It has wide applications in various fields such as geoscience, autonomous driving, and drone navigation. The rapid development of deep learning techniques has led to significant advancements in 3D object detection. However, with the increasing complexity of applications, 3D object detection faces a series of challenges such as data imbalance and the effectiveness of network models. Specifically, in an experiment, our investigation revealed a notable discrepancy in the LiDAR reflection intensity within a point cloud scene, with stronger intensities observed in proximity and weaker intensities observed at a distance. Furthermore, we have also noted a substantial disparity in the number of foreground points compared to the number of background points. Especially in 3D object detection, the foreground point is more important than the background point, but it is usually downsampled without discrimination in the subsequent processing. With the objective of tackling these challenges, we work from both data and network perspectives, designing a feature alignment filtering algorithm and a two-stage 3D object detection network. Firstly, in order to achieve feature alignment, we introduce a correction equation to decouple the relationship between distance and intensity and eliminate the attenuation effect of intensity caused by distance. Then, a background point filtering algorithm is designed by using the aligned data to alleviate the problem of data imbalance. At the same time, we take into consideration the fact that the accuracy of semantic segmentation plays a crucial role in 3D object detection. Therefore, we propose a two-stage deep learning network that integrates spatial and spectral information, in which a feature fusion branch is designed and embedded in the semantic segmentation backbone. Through a series of experiments on the KITTI dataset, it is proven that the proposed method achieves the following average precision (AP_R40) values for easy, moderate, and hard difficulties, respectively: car (Iou 0.7)—89.23%, 80.14%, and 77.89%; pedestrian (Iou 0.5)—52.32%, 45.47%, and 38.78%; and cyclist (Iou 0.5)—76.41%, 61.92%, and 56.39%. By emphasizing both data quality optimization and efficient network architecture, the performance of the proposed method is made comparable to other state-of-the-art methods.

Implementation of Radon transformation by rotating 1D interferometer

The use of interferometry to observe objects in three-dimensional space requires a corresponding rank of the system of interferometric base vectors. The paper considers one of the ways to solve such a problem using the rotation of a 1D interferometer at an angle to the rotation axis This, with the exception of special cases of parallelism or perpendicularity of the axes, makes it possible to form a consecutive 3D interferometer. Using the rotation of the Earth, such an interferometer performs Radon transform of the angular structure of the spatial image when observing sources far beyond the size of the interferometer base. For this, the elements of the interferometer should be placed at different latitudes. The obtained analytical expressions show that the two-dimensional representation of one-dimensional projections as a function of the rotation angle then takes the form of a sinogram. A rotating 1D interferometer can be used in a number of fields of science and technology, for example, to solve location problems, in ultrasonic de¬fectoscopy, in technical vision systems, in radio astronomy, etc.

4D RADAR/IMU/GNSS INTEGRATED POSITIONING AND MAPPING FOR LARGE-SCALE ENVIRONMENTS

Abstract. 4D millimeter-wave radar can work in harsh weather conditions such as fog and snow, and measure the position and radial Doppler velocity of objects in three-dimensional space. Some existing methods perform positioning by fusing 3D velocity or point cloud matching information estimated by 4D radar with IMU information. However, the sparsity of radar point clouds and the interference of large amount of noise lead to low accuracy of odometry, and most of the existing work cannot achieve the global consistent mapping with radar point clouds. In this paper, a 4D radar/IMU/GNSS localization and mapping method, G-iRIOM, is proposed. We tightly coupled IMU measurement, Doppler observation information and point cloud matching information from 4D radar by iterative extended Kalman filtering (iEKF) method, and introduced GNSS RTK and loop closure information as global observation to correct the positioning drift of the odometry. The experimental results show that the tightly coupled Doppler velocity information can effectively improve the control of the pose on the local point cloud position, thus enhancing the mapping accuracy. Meanwhile, the introduction of GNSS and loop closure information can significantly improve the positioning accuracy of 4D radar odometry as a kind of global observation, and achieve global consistent mapping of large-scale outdoor scenes based on 4D radar point clouds.

Real-Time Fire and Smoke Detection for Trajectory Planning and Navigation of a Mobile Robot

Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.

Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language

The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.

Using Augmented Reality for Visualizing Architectures of Software Modules

Nowadays the technology of augmented reality has become available for a wide audience of users because of a big number of software and hardware enhancements and optimizations done in the last years. The fact that the smartphone is a suitable and relatively cheap device having all the hardware required makes the technology even more accessible and thus widespread. Furthermore, the interaction with three-dimensional objects in space may have positive impact on user’s perception of information. These both facts make the technology of augmented reality a good choice for displaying complex data.The analysis of software plays a significant role in development as it is vital to keep the code clean and sustained all the time. Poor quality code may be unsustainable to the extent it must be fully replaced which results in big losses of resources. In terms of quality checks the analysis must be informative and consume as few resources as possible to be executed so that it is appropriate to perform it regularly. That is the reason for this process to be automated and made convenient to execute and percept.The new system for automatic software analysis is described in this article. ADAR (Architecture Displayer in Augmented Reality) software is best suitable for code coupling and cohesion analysis as it uses three-dimensional graph to display connectivity between parts of software module. High coupling and low cohesion might inform the developers of severe architectural mistakes that may lead to high code fragility. With the use of AR technology the result of high coupling detection analysis in the form of graph is presented in augmented reality to provide user the information in a highly intuitive way.This article also covers different approaches to graph visualization in three-dimensional space. The criteria that allow to achieve high level of aesthetics relative to this problem are stated in paper. The problem of using the force-directed algorithms in terms of high-aesthetic graph visualization is described in details and some arguments pro their usage are given.

An algorithm for estimating the number of objects in a selected area of space based on ordinary photos

There are many areas of activity where the problem of counting objects in three-dimensional space appears. It is often the case that the primary source of information in such a situation are photos or digital videos. In mathematical language, a two-dimensional photo can somewhat be treated as a projection of a selected space area. The significant issue in such a situation is that the counted objects in the image may overlap many times, which may cause some objects to obscure others. This approach addresses this issue by using appropriate statistical estimations of the amount of obscured objects based on computer image analysis. The proposed algorithm has already been initially used to count the number of birds in digital photos and films.

ІМІТАЦІЙНА МОДЕЛЬ РУХУ ГРУПИ АВТОНОМНИХ ОБʼЄКТІВ-ДРОНІВ В ТРИВИМІРНОМУ ПРОСТОРІ

Purpose. To develop a simulation model of the motion of a group of objects in three-dimensional space to generate datasets and individual samples for further use in training neural networks and developing a mutual positioning system. Methodology. Analytical review, the automated control theory, imitation modelling. Results. Using Python, a 3D model has been developed. Digital and graphical representations of the linear trajectory of movement for a group of 4 drones based on input data, including coordinates of initial and final points and an error range, have been received. Trajectories with compensated and uncompensated deviations have been compared. Approximation and simplifications have been justified by the goal of enabling the use of the model by those without fundamental knowledge of control theory, system modelling or advanced mathematics. Code testing and benchmarking have also been conducted, proving the financial feasibility of the solution. The model will be used by the authors for further research, including assessing the likelihood of obtaining biassed data from sensors of some drones in a swarm and finding ways to correct it. The source code of the developed model is provided for open access. Scientific novelty. An imitation model of interaction and movement in three-dimensional space for a group of autonomous objects has been proposed. These objects, capable of collective decision-making based on swarm intelligence, aim to compensate for errors or interference. Practical significance. Testing in the AWS cloud environment has demonstrated that even with minimal computational power the proposed imitation model yields adequate results, suitable for neural network training.

OPTICAL-ELECTRONIC SYSTEM OF HIGH SPATIAL RESOLUTION WHEN OBSERVING REMOTE OBJECTS

Schematic solutions of an optoelectronic system for obtaining high spatial resolution when observing remote objects are studied, which allow obtaining high resolution of objects in the volume of the observed space, providing high accuracy in determining the coordinates and geometric features of objects in three-dimensional space. A hybrid system is considered for simultaneous registration of the entire set of rays from observed objects in a full solid angle by a high-resolution optical system together with a system for registering sets of different sets of ray directions from objects in the same observed space, which makes it possible to form a set of narrow layers of the observed space in range. To further improve the accuracy of obtaining coordinate and non-coordinate information of the observed objects, the images of narrow layers of space are expanded to the resolution of the first optical registration system by weighted substitution of the detail filters of the discrete wavelet transform. Integration in different digital information ranges is achieved by decomposing the image of the first digital source and each of the set of images of the second digital source into low-frequency and high-frequency components, separate processing of image components based on the principle of weighted summation for each pixel, and the formation of the resulting image. Computational experiments have been carried out showing the effectiveness of the proposed circuit solutions in determining the parameters of objects of interest.

Sea Fall Point Measurement Method Based on Cone Angle Intersection

The naval gun strikes against the sea need to measure the deviation of the fall point to determine the accuracy and hit probability. In order to improve the accuracy of measuring the fall point position of the naval gun, this paper proposes a maritime fall point measurement method based on cone angle intersection, which does not need to use the axis angle data of the equipment and eliminates the dependence on the equipment’s axis angle data when measuring accuracy. Firstly, the photographic measurement method is used to achieve automatic optical intersection of single equipment at different moments, using two observation stations and one cooperative target ship. Then, the image data within 10 km from the measured target are collected using two observation stations. The line of sight directions and angles of the two observation stations are combined to measure the target object and the cooperative target ship at each observation station. The constraint of the approximate equal radius of the Earth ellipsoid is added under close range conditions. The cone angle intersection model mapping relationship is established. Finally, the coordinate position of the target object in three-dimensional space is calculated using field tests at sea and compared with the actual position for analysis.The experimental results show that the measured fall point is within 5 m in the range of 700 m with a ratio of more than 75%. This has increased the range of measurement accuracy processing and proved the feasibility of the measurement method. The method can be used for calibrating large and medium caliber naval gun firing to the sea to improve the firing accuracy of the weapon. It has guiding significance for measuring the fall point position of naval guns at sea.

Computer 3d modeling of manipulators in the Maple software environment

The creation of a 3D model of any robotic systems has become widespread in the last few years and is used both for educational and research purposes, where work with a visual image of an object in three-dimensional space is required. Currently, there are many 3D modeling environments available for various fields of robotics research with some advantages and limitations. Computer 3D modeling of spatial manipulators and their movements with a visual image of the object allows you to visually observe the results when studying its kinematic, dynamic, power, control and other aspects. In this paper, a 3D computer model of the spatial manipulator RRRRT and its movement was obtained by developing and implementing a program in the Maple environment. To create a three-dimensional model of the manipulator, it is required to obtain 3D models of the components of the manipulator: kinematic pairs, links, grips and combine all parts of the manipulator into one system using the developed program in the Maple environment.

On the eigenstructure of rotations and poses: commonalities and peculiarities

Locating vehicles, targets and objects in three-dimensional space is key to many fields of science and engineering such as robotics, aerospace, computer vision and graphics. Rotations and poses (position plus orientation) of bodies can be expressed in a variety of ways. Rotation matrices constitute one of the classic matrix Lie groups, the special orthogonal group— SO ( 3 ) . Poses can likewise be represented by matrices. One such representation is embodied in a 4 × 4 matrix establishing another famous matrix Lie group, the special Euclidean group— SE ( 3 ) . An alternative representation of pose uses 6 × 6 matrices and is referred to as the group of pose adjoints— Ad ( SE ( 3 ) ) . The eigenstructures of these representations reveal much about them from Euler’s theorem for rotations to the Mozzi–Chasles theorem for the general displacement of a rigid body. While the eigenstructure of SO ( 3 ) has been extensively studied, those of SE ( 3 ) and Ad ( SE ( 3 ) ) have hardly received the same scrutiny yet their structure is much richer. Motivated by their importance in kinematics and dynamics, we provide here a complete characterization of rotations and poses in terms of the eigenstructure of their matrix Lie group representations. An eigendecomposition of pose matrices reveals that they can be cast into a form similar to that of rotations although the structure of the former can vary depending on the nature of the pose involved. In particular, the pose matrices of SE ( 3 ) and Ad ( SE ( 3 ) ) cannot generally be diagonalized as can rotation matrices but they of course do yield to a Jordan normal form, from which we can identify a principal-axis pose in much the same manner that we can a principal-axis rotation. We also address the minimal polynomials for poses and derive a novel expression for the Jacobian in Ad ( SE ( 3 ) ) . Finally, we argue that the true counterpart to SO ( 3 ) for poses is not SE ( 3 ) but Ad ( SE ( 3 ) ) .

Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Information acquisition is an important branch of information science. It is the product of the development and cross-integration of traditional sensing technology and other multidisciplines. It is characterized by the high precision, high speed, integration, and intelligence of information acquisition. An important part of information acquisition, it studies how to acquire the geometric structure and scale information of objects in three-dimensional space. With the continuous development of the level, the acquisition of 3D information has become more and more important in scientific research and industrial production. This paper takes 3D information acquisition technology as the main line. From the perspectives of fusion, effectiveness, physical limit, and measurement efficiency, high-resolution 3D information acquisition techniques are studied. In this paper, the upper and lower limb movements of each behavioral segment are segmented according to the difference. An omnidirectional stereo vision sensor composed of a single camera and two secondary conical mirrors is designed, which solves the problems of large size, narrow field of view, and asynchronous image acquisition of traditional binocular vision sensors, effectively ensuring image perspective. The invariance of projection avoids image distortion caused by curved mirror imaging and reduces the difficulty of subsequent work. The three-dimensional Delaunay triangulation algorithm is used to replace the traditional Poisson reconstruction algorithm to generate the triangular mesh model. The triangular mesh model obtained by the multiview stereo reconstruction algorithm is relatively rough. This article uses Zbrush’s ZRemesher and geometric-divide functions to smooth and simplify the model. The results show that the accuracy of the algorithm proposed in this paper is as high as 89.25% in the motion recognition of body elements. The dynamic mapping method is used to map the texture of the triangular mesh model, so that the realism of the model reaches 91.23%.

ОЦЕНКА СИСТЕМ КООРДИНАТНЫХ МЕТОК С ИСПОЛЬЗОВАНИЕМ БОРТОВОЙ КАМЕРЫ МОБИЛЬНОГО РОБОТА «СЕРВОСИЛА ИНЖЕНЕР» В УСЛОВИЯХ ЗАКРЫТЫХ ПОМЕЩЕНИЙ

Modern tasks of special areas of robotics, including search and rescue operations in urbanareas, face a number of obstacles to mobile robotics, where the automatic performance of various functionsby mobile robots remains a key task. One of the important requirements for the algorithms andsoftware of the robot is the possibility of autonomous decision-making and automatic performance bythe robot of various functions, both low and high levels based on the embedded algorithms and informationreceived from the on-board sensors of the robot. To date, the most common on-board robot sensorsare cameras of various types, due to their technical capabilities and lower cost relative to lidarsand other sensors that provide visual information in the form of digital images. Camera calibration is anecessary process for extracting accurate information from digital images. This process is necessary toobtain an exact correspondence between the three-dimensional object space and the pixel space of theimage, for the possibility of subsequent use of computer vision algorithms, aggregation, and informationprocessing. Calibration of digital cameras is an integral part of a number of practical tasks of machinevision such as navigation of mobile robotic systems, medicine, reconstruction of dense and sparse threedimensionalmaps of the environment, video surveillance and visual inspection, visual simultaneouslocalization and mapping, etc. The urgency of the problem of camera calibration is defined by the presenceof many different methods of calibration and calibration templates. Each individual solution issuitable only for special conditions, e.g., lack of lighting, bad weather conditions, the presence of thirdpartyobjects blocking visibility, etc. In most cases, each calibration method uses a specific calibrationpattern. Camera calibration is usually associated with the use of special calibration templates. Theyallow to achieve the most accurate results due to a previously known geometric structure. Currently, theprocedure for camera calibrating of robotic systems is carried out in laboratory conditions using theclassic “chessboard” method. In addition, there are only a few alternative approaches that are in their infancy state both in Russia and abroad. On the other hand, research into camera calibration methodscontinues and new alternatives for camera calibration are emerging. One of the new directions is theuse of fiducial marker systems as a reference object. A variety of parameters such as the size of the calibrationtemplate, the dimension of the calibration data set, the distribution of distances from the camerato objects on the stage, etc. creates a vast area for experimental testing of optimal camera calibrationparameters. This paper presents a research of automatic camera calibration using fiducial marker systems(FMS), which are located on the surface of the robot. Based on the results of virtual experimentswith FMS in the Gazebo simulation environment of the robotic operating system ROS, two differenttypes of FMS were selected that are optimal relative to other types of FMS covered by our previousstudies in terms of the resistance of FMS to systematic occlusion of the marker area and the effect ofmarker size on quality of its recognition. The selected FMS were tested using the onboard camera of theRussian mobile robot Servosila Engineer in indoor settings to assess the correlation of results in virtualand real environments.

A Mobile Platform for Movement Tracking Based on a Fast-Execution-Time Optical-Flow Algorithm

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided.

ПРОСТРАНСТВЕННОЕ РАЗДЕЛЕНИЕ ИНФОРМАЦИИ В САМОЛЕТНОМ ПЕРЕГОВОРНОМ УСТРОЙСТВЕ

The article presents some results of research aimed at the design of human-machine interfaces,taking into account the multimodal nature of human perception, for use in the on-boardequipment of an aircraft. In particular, we are talking about the possibility of a wider use of audiochannels for input and output of information. The advantages of sound interfaces in relation tovisual and tactile ones are, first of all, in the absence of the need for directed attention of the pilot,in the ability to create auditory objects in three-dimensional space and indicate the direction toseveral different objects at the same time. In the experiments, the possibilities of spatial separationof speech information flows in an aircraft intercom in situations where the level of interferencesignificantly exceeded the level of the target speech message were tested. The indicators of targetmessage recognition were evaluated in the presence of two types of sound interference: the sound ofanother speech message and the noise of an aircraft engine. The results showed that spatial separationof audio messages significantly improves the operator’s ability to recognize their content, regardlessof the type of interference. The maximum number of errors when recognizing a target messagecorresponds to its spatial position in the same direction as the noise of the interference. At thesame time, message recognition is significantly better if it is pronounced in a female voice. The fact ofspatial asymmetry of correct recognitions was also revealed: messages arriving from the right are recognized better than in cases of their arrival from the left. The practical significance of the researchconcerns the possibility of creating intercom with increased security against conflicts betweendifferent information flows, as well as against the impact of external acoustic noise. The prospect isseen in the use of three-dimensional audio interfaces not only as part of an intercom, but also fornavigation and aircraft control systems, as well as monitoring its state.

Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.

Analysis of cerebral blood flow state during a mental rotation task to assess spatial perception ability

Spatial recognition is the ability to quickly and accurately grasp the position, posture, direction, size, shape, spacing, speed, etc. of objects in three-dimensional space, and is said to be established through the cooperation of multiple sensory organs such as vision and hearing, and controlled by the right brain. Currently, paper tests such as MRT and MCT are the most common methods for measuring spatial recognition ability. Although measurement methods and learning systems using AR and VR have also been proposed, objective evaluation indices have yet to be established. Therefore, it is considered necessary to measure and analyze the state of brain activity during spatial recognition in order to objectively evaluate spatial recognition. Group comparisons based on task performance showed a significant increase in oxygenated hemoglobin in the higher group compared to the lower group in both score and clear time. In addition, a comparison between the right and left brains showed that the right brain tended to have a higher percentage of increased oxygenated hemoglobin, which is consistent with the view that spatial recognition ability is significantly right-brained. These results suggest that the mental rotation task is closely related to prefrontal regions and that changes in cerebral blood flow in the right brain are reflected in spatial recognition ability.