Real-time Motion Research Articles

The application and challenges of virtual reality technology in creative advertising design

Abstract With the advancement of science and technology, virtual reality (VR) technology has been widely used in advertising. However, its practical application and challenges in creative advertisement must be studied deeply. The study aims to analyze the effects of VR technology on advertisement design and the technical difficulties it faces. The research methods include camera calibration of virtual advertisements, motion tracking, and calculation of lighting effects, etc. The results show that the use of VR technology of the results show that the virtual advertisement using VR technology can effectively improve the correct recall rate (by 12%) and the correct re-recognition rate (by 10.5%) of the advertisement brand. At the technical level, real-time motion tracking of billboards is achieved, and the average error is controlled within 0.6 pixels. The consistency of illumination of virtual advertisements can be effectively improved using the inverse illumination method. The conclusions show that virtual reality technology has great potential in the design of creative advertisements. Still, at the same time, it also faces the challenges of the accuracy of the camera calibration, the efficiency of the motion tracking algorithm, and simulation of the illumination effect.

Design and Testing of an Active Balancing Board for Exergames

In recent years, there has been a growing recognition of the importance of balance and coordination in maintaining overall health, particularly among the elderly population. As societies around the world experience demographic shifts with an increasing number of elderly individuals, age-related declines in balance and coordination can lead to an elevated risk of falls and fall-related injuries, making it a significant public health concern. The need for effective balance rehabilitation and fall prevention strategies has become paramount. This paper presents a motion platform designed for balance rehabilitation exercises using exergames. The system consists of a 3 degrees of freedom (3DOF) platform actuated by four servomotors controlled by Omron-DeltaTau real-time motion controller. As a novelty with respect to other State of Art (SoA) actuated platforms, in the present work the movements of the user's body, which determine the platform motion, are tracked by exploiting a markerless motion capture system with an RGB camera and pose-estimation algorithm MediaPipe. The human displacement is used to generate the input for the roll rotation of the platform according to a one d.o.f. transfer function, whose parameters can be set to get different levels of difficulties in the exercise. The device offers therefore the flexibility to set different parameters to modify the level of the users’ training. The proposed device has the potential for a larger variety of rehabilitation activities and exergames. This paper describes the elements adopted for the implementation of this human-in-the-loop system, presents some preliminary tests to explore the influence of transfer function parameters on human and platform movements, and concludes with future development possibilities.

Development of a Blended Physiotherapeutic Module for Young Adults with Non-Specific Chronic Low Back Pain: A Feasibility Study

Introduction: Non-Specific Chronic Low Back Pain (NSCLBP) is a prevalent non-communicable disease in India, burdening individuals psychologically and socially. Due to economic constraints, people prefer self-management rather than seeking professional advice. Therefore, their go-to search strategy for self-management is social media platforms. Many researchers have identified a lack of evidence in these social media videos. Aim: To develop and evaluate the feasibility of a blended physiotherapeutic module for young adults with NSCLBP. Materials and Methods: This feasibility study was conducted at Nitte Institute of Physiotherapy, NITTE (Deemed to be University), Deralakatte, Mangaluru, Karnataka, India from July 2023 to December 2023. This study involved three phases: identifying modifiable risk factors for exercise adherence in individuals with NSCLBP, developing a module, and evaluating its feasibility. Twenty-four young individuals with low back pain were given the blended physiotherapeutic module for four weeks, consisting of three sessions per week. The first two sessions were conducted online, and the following session was face-to-face. The data were analysed using SPSS software (SPSS Inc.; Chicago, IL) version 29.0.10. The collected data were summarised using Descriptive Statistics: frequency, percentage; mean, and Standard Deviation (SD). To compare the Quebec Back Pain Disability Scale (QBPDS) and Numerical Pain Rating Scale (NPRS) scores, repeated measures ANOVA was employed. The Bonferroni test was utilised for pairwise comparisons of QBPDS and NPRS scores. A p-value <0.05 was considered significant. Results: In this study, the average percentage of individuals who performed exercises for four weeks was 22 (91.7%), indicating high adherence. There was a significant difference (p-value < 0.05) in the QBPDS as well as the NPRS from baseline to weeks -2, 3, and 4. Conclusion: The developed blended physiotherapeutic module for NSCLBP individuals was found to be feasible. Positive results were identified in reducing pain and disability after two weeks. This study aids individuals struggling with physiotherapy exercises in a digitalised society, suggesting that future research could incorporate real-time movement analysis and regional language databases and software that should be validated.

Exploration of Physical Education Teaching Reform in Colleges and Universities in the Context of Deep Learning

Abstract Deep learning is an important path for the digital reform of college sports teaching. In this context, the study is first conducted to acquire sports data in colleges and universities. Openpose technology is used to collect skeletal point coordinate data and process it to construct a human sports model. Then, based on the similarity comparison of sports poses, the generated sports sequences are filled to simulate the real human sports sequences and data, and the DTW algorithm is used to match the sports sequences and data with the features, eliminate the inconsistency between the length of the poses to be matched and the template pose sequences, and then give the scores of the poses to realize the evaluation of the learners’ sports performance. Combining the principles and functions mentioned above, we construct a system for evaluating sports teaching and analyze its application examples. The distribution of the distance between the quaternion-based swing action and the standard swing action is within the range of 0.18~0.25, and the distance between various types of actions and the standard action is not large. The professional scoring was 5.199 points higher than the DTW-based stance score of 90.183 points. In addition, in the experimental group and the control group, there is a significant difference in the analysis of students’ movement skills assessment, P<0.05, indicating that compared with the traditional teaching mode, the deep learning-based physical education teaching system has a strong relevance as well as the ability to provide real-time movement feedback and movement correction for students’ movement practice.

Construction of an Interactive Platform for Piano Teaching in Virtual Reality Environment

Abstract Under the impact of the wave of new technology, the form of piano teaching is no longer limited to the classroom and paper textbooks, and modern information technology, such as the Internet and virtual reality, is constantly integrated into traditional piano teaching. This paper utilizes the Unity3D engine to create a virtual reality environment for piano teaching interaction, based on VR technology, and to construct an interactive platform for piano teaching. Firstly, based on binocular stereo vision for the three-dimensional reconstruction of the teaching interactive scene, based on the improvement of the LeNet5 network for the user’s head gesture tracking, the constructed virtual scene realizes the real-time movement with the movement of the human head. The test set images using CPU-i76700HQ and GTX960 graphics cards consume 78ms and 25ms per frame, respectively, which meets the real-time system requirements. In this paper, the improved LeNet5 algorithm for head pose estimation without face detection is applicable and has a high detection accuracy of 99.31%, which is better than the algorithms such as Adaboost+Hough, LBF+BP, CNN+Heatmaps, DCNN, and FSA-Net, and is able to satisfy the real-time detection requirements of the virtual scene. The virtual piano teaching interactive platform has an average rating of more than 4 in attractiveness, subjectivity, interactivity, and convenience, but the running speed is only 3.46, so it is necessary to strengthen the technical support and improve the software and hardware level to break through the development bottleneck. Traditional piano teaching is given new vitality and kinetic energy by the interactive platform constructed in this paper for piano teaching in a virtual reality environment.

An Integrated Autonomous Dynamic Navigation Approach toward a Composite Air-Ground Risk Construction Scenario.

Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air-ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated potential-field-based risk assessment approach is proposed to evaluate the threat severity of the environmental obstacles. Meanwhile, the self-adaptive dynamic window approach is suggested to manage the real-time motion planning solution for air-ground risks. By designing the multi-objective velocity sample window, we constrain the vehicle's speed planning instructions within reasonable limits. Combined with a hierarchical decision-making mechanism, this approach achieves effective obstacle avoidance with multiple drive modes. Simulation results demonstrate that, in comparison with the traditional dynamic window approach, the proposed method offers enhanced stability and efficiency in risk avoidance, underlining its notable safety and effectiveness.

Microwave-assisted DES fabrication of lignin-containing cellulose nanofibrils and its derived composite conductive hydrogel

Deep eutectic solvents (DES) have been regarded as green solvents in the biorefinery of lignocellulosic biomass, but long duration time has severely limited efficiency. The microwave-assisted DES pretreatment along with enzymatic hydrolysis and high-pressure homogenization process was proposed to produce lignin-containing cellulose nanofibrils (LCNF) from corncob. Benefiting from microwave-assisted DES pretreatment, the duration time was greatly shortened; meanwhile the effects of different kinds of DES on the resultant LCNF were investigated. The results showed that, the microwave-assisted DES fabricated LCNF (M-LCNF) was successfully obtained, exhibiting good nano size, thermal stability, colloidal stability, and fluorescence. M-LCNF was further introduced into phytic acid (PA) enhanced poly(acrylamide-co-acrylic acid) (P(AM-co-AA)) network and constructed composite conductive hydrogels (PLP). The obtained hydrogels exhibited good mechanical strength, UV blocking ability, fluorescence, and conductivity. A simple battery assembled with the resultant PLP as electrolyte had an out voltage of 2.41 V. The composite conductive hydrogel showed good sensing performance towards different stimuli (e.g., stretching and compression) and human motions in real time. It is expected that this research would provide an alternative way for green fabrication of LCNF and potential application of LCNF in flexible sensors.

Multi-Modal Multi-Channel American Sign Language Recognition

In this paper, we propose a machine learning-based multi-stream framework to recognize American Sign Language (ASL) manual signs and nonmanual gestures (face and head movements) in real time from RGB-D videos. Our approach is based on 3D Convolutional Neural Networks (3D CNNs) by fusing the multi-modal features including hand gestures, facial expressions, and body poses from multiple channels (RGB, Depth, Motion, and Skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3D CNN model. We collected a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera. Each video consists of 100 ASL manual signs, along with RGB channel, Depth maps, Skeleton joints, Face features, and HD face. The dataset is fully annotated for each semantic region (i.e. the time duration of each sign that the human signer performs). Our proposed method achieves 92.88% accuracy for recognizing 100 ASL sign glosses in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on a large-scale dataset, ChaLearn IsoGD, achieving the state-of-the-art results.

Optimal planning target margin for prostate radiotherapy based on interfractional and intrafractional variability assessment during 1.5T MRI-guided radiotherapy.

We analyzed daily pre-treatment- (PRE) and real-time motion monitoring- (MM) MRI scans of patients receiving definitive prostate radiotherapy (RT) with 1.5 T MRI guidance to assess interfractional and intrafractional variability of the prostate and suggest optimal planning target volume (PTV) margin. Rigid registration between PRE-MRI and planning CT images based on the pelvic bone and prostate anatomy were performed. Interfractional setup margin (SM) and interobserver variability (IO) were assessed by comparing the centroid values of prostate contours delineated on PRE-MRIs. MM-MRIs were used for internal margin (IM) assessment, and PTV margin was calculated using the van Herk formula. We delineated 400 prostate contours on PRE-MRI images. SM was 0.57 ± 0.42, 2.45 ± 1.98, and 2.28 ± 2.08 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively, after bone localization and 0.76 ± 0.57, 1.89 ± 1.60, and 2.02 ± 1.79 mm in the LR, AP, and SI directions, respectively, after prostate localization. IO was 1.06 ± 0.58, 2.32 ± 1.08, and 3.30 ± 1.85 mm in the LR, AP, and SI directions, respectively, after bone localization and 1.11 ± 0.55, 2.13 ± 1.07, and 3.53 ± 1.65 mm in the LR, AP, and SI directions, respectively, after prostate localization. Average IM was 2.12 ± 0.86, 2.24 ± 1.07, and 2.84 ± 0.88 mm in the LR, AP, and SI directions, respectively. Calculated PTV margin was 2.21, 5.16, and 5.40 mm in the LR, AP, and SI directions, respectively. Movements in the SI direction were the largest source of variability in definitive prostate RT, and interobserver variability was a non-negligible source of margin. The optimal PTV margin should also consider the internal margin.

Development and Validation of an Artificial Intelligence-Based Motion Analysis System for Upper Extremity Rehabilitation Exercises in Patients with Spinal Cord Injury: A Randomized Controlled Trial.

In this study, we developed an AI-based real-time motion feedback system for patients with spinal cord injury (SCI) during rehabilitation, aiming to enhance their interest and motivation. The effectiveness of the system in improving upper-limb muscle strength during the Thera band exercises was evaluated. The motion analysis program, including exercise repetition counts and calorie consumption, was developed using MediaPipe, focusing on three key motions (chest press, shoulder press, and arm curl) for upper extremity exercises. The participants with SCI were randomly assigned to the experimental group (EG = 4) or control group (CG = 5), engaging in 1 h sessions three times a week for 8 weeks. Muscle strength tests (chest press, shoulder press, lat pull-down, and arm curl) were performed before and after exercises. Although both groups did not show significant differences, the EG group exhibited increased strength in all measured variables, whereas the CG group showed constant or reduced results. Consequently, the computer program-based system developed in this study could be effective in muscle strengthening. Furthermore, these findings may serve as a valuable foundation for future AI-driven rehabilitation exercise systems.

Drosophila flying in augmented reality reveals the vision-based control autonomy of the optomotor response

For walking, swimming, and flying animals, the optomotor response is essential to stabilize gaze. How flexible is the optomotor response? Classic work in Drosophila has argued that flies adapt flight control under augmented visual feedback conditions during goal-directed bar fixation. However, whether the lower-level, reflexive optomotor response can similarly adapt to augmented visual feedback (partially autonomous) or not (autonomous) over long timescales is poorly understood. To address this question, we developed an augmented reality paradigm to study the vision-based control autonomy of the yaw optomotor response of flying fruit flies (Drosophila). Flies were placed in a flight simulator, which permitted free body rotation about the yaw axis. By feeding back body movements in real time to a visual display, we augmented and inverted visual feedback. Thus, this experimental paradigm caused a constant visual error between expected and actual visual feedback to study potential adaptive visuomotor control. By combining experiments with control theory, we demonstrate that the optomotor response is autonomous during augmented reality flight bouts of up to 30min, which exceeds the reported learning epoch during bar fixation. Agreement between predictions from linear systems theory and experimental data supports the notion that the optomotor response is approximately linear and time invariant within our experimental assay. Even under positive visual feedback, which revealed the stability limit of flies in augmented reality, the optomotor response was autonomous. Our results support a hierarchical motor control architecture in flies with fast and autonomous reflexes at the bottom and more flexible behavior at higher levels.

A Unified Real-Time Motion Generation Algorithm for Approximate Position Analysis of Planar N-Bar Mechanisms

Abstract This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degrees-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general-purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods—fast dyadic decomposition and relatively slower optimization-based—to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees-of-freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method.

Fast Convergent Ali Baba and Forty Thieves Algorithm for Inverse Kinematic Solution of 7-DOF Robotic Manipulator

Inverse kinematics is a crucial topic in robotics, enabling robots to calculate the joint angles required to achieve the desired end effector position and orientation. Solving the inverse kinematics problem quickly with high accuracy is vital for robot manipulators. If sufficient speed is provided, the real-time motion planning task of robot manipulators can be achieved. Real-time motion planning of complex robot manipulators is not possible with classical mathematical methods. Overcoming this problem will provide many benefits in the design and control of robot manipulators. In contemporary research, metaheuristic approaches have become widely employed for addressing the inverse kinematics problem. This investigation utilizes the efficient and simple Ali Baba and the Forty Thieves (AFT) algorithm to resolve the inverse kinematics problem. To increase convergence speed of AFT, a parameter has been used in early iterations of the algorithm to prevent thieves from randomly searching within the search area to find Ali Baba when they realized they had been deceived. Additionally, an approach has been proposed regarding the accuracy of the information brought by the Marjaneh. Finally, the inverse kinematics solution of the 7-DOF robot manipulator was carried out comparatively.

Research on Teleoperated Virtual Reality Human-Robot Five-Dimensional Collaboration System.

In the realm of industrial robotics, there is a growing challenge in simplifying human-robot collaboration (HRC), particularly in complex settings. The demand for more intuitive teleoperation systems is on the rise. However, optimizing robot control interfaces and streamlining teleoperation remains a formidable task due to the need for operators to possess specialized knowledge and the limitations of traditional methods regarding operational space and time constraints. This study addresses these issues by introducing a virtual reality (VR) HRC system with five-dimensional capabilities. Key advantages of our approach include: (1) real-time observation of robot work, whereby operators can seamlessly monitor the robot's real-time work environment and motion during teleoperation; (2) leveraging VR device capabilities, whereby the strengths of VR devices are harnessed to simplify robot motion control, significantly reducing the learning time for operators; and (3) adaptability across platforms and environments: our system effortlessly adapts to various platforms and working conditions, ensuring versatility across different terminals and scenarios. This system represents a significant advancement in addressing the challenges of HRC, offering improved teleoperation, simplified control, and enhanced accessibility, particularly for operators with limited prior exposure to robot operation. It elevates the overall HRC experience in complex scenarios.

Proton-exchange membrane fuel cell design for in-situ depth-sensitive X-ray absorption spectroscopy

We have built a proton exchange membrane hydrogen fuel cell optimized for angle-resolved X-ray absorption spectroscopy. This cell allows in-situ fluorescence measurements during electrochemical operation with minimal trade-offs in cell performance while reaching automotive current densities. The fluorescence signal can be collected from wide angles to extract depth information from the probed atomic species such as Pt, Co, and Ni, crucial to highly efficient FC. This cell is designed to assess the connection between the ionic drag/diffusion and the performance loss by following the real-time movement of the species through the membrane electrode assembly.

Chemical Resistant Yarn with Hierarchical Core–Shell Structure for Safety Monitoring and Tunable Thermal Management in High-Risk Environments

Chemical resistant textiles are vital for safeguarding humans against chemical hazards in various settings, such as industrial production, chemical accidents, laboratory activities, and road transportation. However, the ideal integration of chemical resistance, thermal and moisture management, and wearer condition monitoring in conventional chemically protective textiles remains challenging. Herein, the design, manufacturing, and use of stretchable hierarchical core–shell yarns (HCSYs) for integrated chemical resistance, moisture regulation, and smart sensing textiles are demonstrated. These yarns contain helically elastic spandex, wrapped silver-plated nylon, and surface-structured polytetrafluoroethylene (PTFE) yarns and are designed and manufactured based on a scalable fabrication process. In addition to their ideal chemical resistance performance, HCSYs can function as multifunctional stretchable electronics for real-time human motion monitoring and as the basic element of intelligent textiles. Furthermore, a desirable dynamic thermoregulation function is achieved by exploiting the fabric structure with stretching modulation. Our HCSYs may provide prospective opportunities for the future development of smart protective textiles with high durability, flexibility, and scalability.

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches. This study introduces a multisensor fusion-based digital twin (MFDT) for localized quality prediction in the robotic L-DED process. The data used in multisensor fusion includes features extracted from a coaxial melt pool vision camera, a microphone, and an off-axis short wavelength infrared thermal camera. The key novelty of this work is a spatiotemporal data fusion method that synchronizes multisensor features with the real-time robot motion data to achieve localized quality prediction. Optical microscope (OM) images of the printed part are used to locate defect-free and defective regions (i.e., cracks and keyhole pores), which serve as ground truth labels for training supervised machine learning (ML) models for quality prediction. The trained ML model is then used to generate a virtual quality map that registers quality prediction outcomes within the 3D volume of the printed part, thus eliminating the need of physical inspections by destructive methods. Experiments show that the virtual quality map closely matches the actual quality observed by OM. Compared to traditional single-sensor-based quality prediction, the MFDT has achieved a significantly higher quality prediction accuracy (96%), a higher ROC-AUC score (99%), and a lower false alarm rate (4.4%). As a result, the MFDT is a more reliable method for defect prediction. The proposed MFDT also lays the groundwork for our future development of a self-adaptive hybrid processing strategy that combines machining with AM for defect removal and quality improvement.

Exploration on flexible wearable sensor motion monitoring based on novel functional polymer conjugated materials.

With the continuous development of flexible electronics, multi-functional device integration, artificial intelligence technology, etc., flexible wearable sensors are playing an increasingly important role in people's daily motion monitoring. However, current flexible wearable sensors have problems such as low accuracy, poor real-time performance, and poor stability in motion monitoring, which seriously hinder the better application of flexible wearable sensors and are not conducive to the collection and monitoring of motion signals. To this end, this paper designed a flexible wearable sensor motion monitoring system and tested its performance through the preparation and performance research of new functional polymer conjugated materials. The research results show that the motion monitoring system designed based on the new functional polymer conjugated material flexible wearable sensor has good monitoring accuracy and real-time performance. When the output data is 500 pieces, the running monitoring accuracy reaches 95.4%, and the monitoring feedback time is 0.321s; the high jump movement monitoring accuracy rate reaches 97%, and the monitoring feedback time is 0.287s; the long jump movement monitoring accuracy rate reaches 96%, and the monitoring feedback time is 0.296s. This shows that the flexible wearable sensor motion monitoring system of this paper has better performance and can meet the current demand for accuracy and real-time motion monitoring. This study highlights the impact of new functional polymer conjugated materials on flexible wearable sensors, helping to further solve the deficiencies of flexible wearable sensors in sports monitoring and promote their better development.

Real-time yaw-misalignment calibration and field-test verification of wind turbine via machine learning methods

It has become a general consensus that nacelle-mounted LiDAR can be used to calibrate the yaw misalignment or drive the real-time yaw motions for wind turbines, which would improve the power-generation efficiency. The advantage of LiDAR utilization is that the accuracy of inflow wind measurement would be greatly improved, while its disadvantage is that the cost remains high and the data validity is not sufficiently high. In this paper, an efficient machine learning method for estimating LiDAR measurement is developed to establish the real-time yaw calibration framework and sustain the LiDAR rolling utilization. Firstly, the correlation of LiDAR measurement with SCADA features is analyzed to estimate LiDAR measurement using only SCADA data. Secondly, several machine learning algorithms are studied for performance comparison, and the dependence of each algorithm on data size is also analyzed. Experimental results show that, the proposed XGBoost algorithm has high accuracy, requires less data, and can quickly calibrate the yaw misalignment. Finally, the field testing is held for a commercial 2 MW wind turbine to verify the effectiveness. The field-test results show that the proposed method is feasible for industrial applications and can improve the annual theoretical power generation by 3.66% compared to the situation without calibration, which also provides an executable and economical solution for LiDAR replacement planning.

IOT Pada Purwarupa Sistem Keamanan Pintu Kantor Dengan Sensor RFID dan PIR Berbasis Telegram

The proposed office door security system combines RFID (Radio-Frequency Identification) Sensor and PIR (Passive Infrared) Sensor technology with Telegram-based communication. The design of this system requires RFID and PIR sensors to be formed in order to produce a system design that is made to meet the needs of system users. The aim of this system is to increase the security level of access to office space by combining identification of access owners via RFID cards and motion detection using PIR sensors. Communication through the Telegram platform is used to provide real-time notifications to access owners when there is activity at the office door. The control system for this device is controlled by the Esp32 Microcontroller, using 4 inputs, namely Rfid, PIR, Keypad and telegram sensors, as well as 5 outputs, namely Selenid Doorlock, Buzzer, LCD, Relay and telegram. By using the esp32 microcontroller to process data and the telegram application for IoT as a link between devices and smartphones. With 2 sensors as logic inputs for the esp32 microcontroller, the number of outputs that occur is 5, namely in the form of a sound from a buzzer as a warning, and a PIR as a sensor to open the door from the inside. When opening the RFID with a card tag, then the selenoid doorlock will open. Overall, the design of this office door security system has the potential to improve security and monitoring in the office space. By combining the technology of RFID sensors, PIR sensors and Telegram-based communication, this system enables accurate identification, real-time motion detection and fast interaction between the security system and the access owner. So that Telegram is successful in sending notifications if someone enters, that is, they will appear. "User Name Open Door: according to the registered UID", but if it fails "Unregistered User Failed Login Attempt" The LCD will also issue an output if someone has registered their RFID tag or not