A Real-Time Motion Control Tracking Mechanism for Satellite Tracking Antenna Using Serial Robot

To measure respiratory motion of the thoracic wall region in dogs using a real-time motion tracking system and compare the amount of respiratory motion between dogs positioned with and without a vacuum-formable cushion. 8 healthy adult mixed-breed dogs (median weight, 23 kg). Dogs were anesthetized and positioned in sternal and dorsal recumbency with and without a vacuum-formable cushion. Three-dimensional movement of anatomic landmarks was measured with a real-time motion capture system that tracked the locations of infrared light-emitting diodes attached externally to the dorsal or ventral and lateral aspects of the thoracic wall. Dogs positioned in sternal recumbency had significantly less cranial-to-caudal and left-to-right respiratory motion at the lateral aspect of the thoracic wall, compared with dogs positioned in dorsal recumbency, whether or not a cushion was used. For dogs treated in sternal recumbency, use of a cushion significantly increased the peak displacement vector (overall movement in 3-D space) for 3 of 4 marker locations on the dorsal thoracic wall. As respiratory rate increased, respiratory motion at the lateral and ventral aspects of the thoracic wall decreased when data for all dogs in dorsal recumbency were evaluated together. Associations between respiratory rate and respiratory motion suggested that the use of rapid, shallow ventilation may be beneficial for dogs undergoing highly conformal radiation treatment. These results provide a basis for further research on respiratory motion in anesthetized dogs.

Limb exercises are common in physical therapy to improve range of motion (RoM), strength, and flexibility of the arm/leg. To improve therapy outcomes and reduce cost, motion tracking systems have been used to monitor the user's movements when performing the exercises and provide guidance. Traditional motion tracking systems are based on either cameras or inertial measurement unit (IMU) sensors. Camera-based systems face problems caused by occlusion and lighting. Traditional IMU-based systems require at least two IMU sensors to track the motion of the entire limb, which is not convenient for use. In this paper, we propose a novel limb motion tracking system that uses a single 9-axis IMU sensor that is worn on the distal end joint of the limb (i.e., wrist for the arm or ankle for the leg). Limb motion tracking using a single IMU sensor is a challenging problem because 1) the noisy IMU data will cause drift problem when estimating position from the acceleration data, 2) the single IMU sensor measures the motion of only one joint but the limb motion consists of motion from multiple joints. To solve these problems, we propose a recurrent neural network (RNN) model to estimate the 3D positions of the distal end joint as well as the other joints of the limb (e.g., elbow or knee) from the noisy IMU data in real time. Our proposed approach achieves high accuracy with a median error of 7.2/7.1 cm for the wrist/elbow joint in leave-one-subject-out cross validation when tracking the arm motion, outperforming the state-of-the-art approach by more than 10%. In addition, the proposed model is lightweight, enabling real-time applications on mobile devices.Clinical relevance- This work has great potential to improve limb exercises monitoring and RoM measurement in home-based physical therapy. It is also cost effective and can be made available widely for immediate application.

Human activity recognition is a very active research on pervasive computing and mobile health application. Many human activity systems based on inertial measurement unit (IMU) sensor data were proposed in the past few years. These systems mainly use IMU sensor placed on he torso and limbs to collect data and utilize supervised machine learning algorithms on sensor data. One main issue of these systems is that wearing multiple on-body IMU sensors may bring inconvenience to users' daily life. The other issue of these exiting methods is that an activity recognition model that is trained on a specific subject does not work well when being applied to predict another subject's activities since IMU activity data always carry information that is specific to the human subject who conducts the activities. In our work, inspired by the principle of domain adaption, we proposed a new deep-learning activity recognition model based on an adversarial network which can remove the subject-specific information within the IMU activity data and extract subject-independent features shared by the data collected on different subjects. We also for the first time use data collected from insole based IMU sensors on 8 participants for 5 common activities to build a new real world human activity dataset which can minimize the inconvenience for users to wear. We conducted experiments with our new real-world dataset. Results show that our subject independent activity recognition model outperforms state-of-art supervised learning techniques and eliminates the effects of individual differences between subjects successfully. The average recognition accuracy under the leave-one-out (L1O) condition achieves 99.0% which is higher than the performance of traditional human activity recognition system based on CNNs.

The use of unmanned aerial vehicles (UAV) systems has increased in recent years. Therefore,studies on UAVs have increased today. In this direction, the production of UAV systems with domestic resources has gained importance. In this study, it is desired to develop a domestic and national flight control card and software. In the flight control board designed for the UAV, it is aimed to keep the vehicle in balance in the air. Accurate measurement of platform orientation plays an important role in many applications such as aerospace, robotics, navigation, marine, machine interaction [1]. Inertial Measurement Unit (IMU) sensor was used to accurately measure the orientation of the UAV. IMU sensor is widely used in UAVs due to its light weight and low energy consumption. In this direction, the need for a filter has emerged in the IMU sensor, which is used to accurately measure the orientation of the unmanned aerial vehicle. In this study, a complementary filter was applied on the IMU sensor. Thanks to this filter, it has been observed that the accuracy of the data received from the IMU sensor has increased. Based on the data obtained, a Proportional Integral Derivative (PID) algorithm was developed, and the vehicle was kept in balance. In this study, ARMCortex-M4 based STM32F407VG microcontroller and MPU6050 as IMU sensor were used. Keil-uVision5 compiler is preferred for software. As a result, high accuracy in the orientation detection of unmanned aerial vehicles was obtained by applying a complementary filter on the IMU sensor.

Designing a real-time visual tracking system to catch goldfishes is a very complex task because a fish’s precise relative 3D positions inside a water tank are difficult to determine. In this paper, we present a new visual servo control system using stereo vision to acquire the 3D positions of goldfishes. The continuously adaptive mean shift (CAMSHIFT) algorithm and optical flow vision tracking methods are applied to provide feedback of a tracked fish’s real-time position and motion trajectory in a water tank at a high frame-rate. Two low-cost, well calibrated cameras are used to track the goldfishes and a 5-axis robot manipulator is used to catch a specified goldfish. The designed visual tracking and servo system is less sensitive to lighting influences and thus performs more efficiently. Experiments of the proposed method yielded very good results as the real-time 3D vision successfully tracked two fishes and guided the manipulator to catching one of them in a net attached to its end-effector.

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

The increase in popularity of fitness and at-home fitness has illuminated a major deficiency with traditional fitness tracking; the absence of immediate motion analysis and corrective feedback. Many conventional methods of instruction, as well as wearables, lack the system feedback of prompting users for proper movements, which can lead to many real workout injuries. The proposed system is the "Real-Time Exercise Tracking System". The proposed project is an intelligent method of intervention that uses computer vision and deep learning to provide a solution to the problem of lacking immediate feedback. The objective of the system is to accurately assess the form a user is exercising in, and properly provide the user with effective feedback on their form in real-time. The system utilizes a combination of MediaPipe for estimating accurate user pose, and a machine learning model to assesses the required movements of the user. The application provides the user with real-time voice and visual feedback on motion awareness. The application will assess the user's exercise awareness in terms of AI-based form correction, real-time performance analytics, propose new workouts based on previous fitness activities, and mitigate risk of injuries with alerts. The project will contribute to filling the gap between unmonitored home activity and professional fitness education, thus creating a safer, more effective, and happy fitness experience for users. Keywords – Real-Time Tracking, Pose Estimation, Computer Vision, MediaPipe, Fitness Technology, Deep Learning, Form Correction

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95–96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Reliable detection and tracking of moving objects is essential for indoor robotics and smart environments. This paper presents a real-time 2D object detection and tracking system that uses a fixed planar LiDAR sensor combined with an inertial measurement unit (IMU), all implemented on a Raspberry Pi. The LiDAR is set up in a stationary position, which allows for continuous monitoring of a specific area and helps separate moving objects from static background structures based on motion. Unlike mobile sensing systems, this approach does not require estimating ego-motion. Instead, the IMU identifies and reduces measurement errors caused by mechanical vibrations and slight changes in orientation that can impact fixed LiDAR setups. Moving objects are detected by analysing consecutive range scans over time, grouped spatially using lightweight clustering, and tracked in the 2D plane with an efficient state model. While the system can cater to human-centric applications, it does not impose any object-specific shape or learning-based assumptions. This flexibility allows the framework to apply to a variety of moving objects. Experimental results in indoor settings show stable real-time performance even with limited computational resources. These findings confirm that low-cost LiDAR and IMU sensing is suitable for general object detection and tracking.

We report on the design of a real-time tracking system using precise space and time information of the hit. Tracking detectors with about 150 ps time resolution are already available and intense R&D is in progress to improve the performance. We describe the implementation of a real-time tracking algorithm in commercial FPGAs for a prototype tracking system based on 8 planes of silicon sensors used as a case study. We demonstrate that the use of the additional time information in the tracking algorithm allows to suppress noise hits while reducing fake track combinations and improving real-time track reconstruction. The system provides offline-like quality tracks with sub-microsecond latency and it is capable to determine the time of the track with picosecond resolution assuming 10 ps resolution for the hits.

We present an approach for real-time camera tracking with depth stream. Existing methods are prone to drift in sceneries without sufficient geometric information. First, we propose a new weight method for an iterative closest point algorithm commonly used in real-time dense mapping and tracking systems. By detecting uncertainty in pose and increasing weight of points that constrain unstable transformations, our system achieves accurate and robust trajectory estimation results. Our pipeline can be fully parallelized with GPU and incorporated into the current real-time depth camera tracking system seamlessly. Second, we compare the state-of-the-art weight algorithms and propose a weight degradation algorithm according to the measurement characteristics of a consumer depth camera. Third, we use Nvidia Kepler Shuffle instructions during warp and block reduction to improve the efficiency of our system. Results on the public TUM RGB-D database benchmark demonstrate that our camera tracking system achieves state-of-the-art results both in accuracy and efficiency.

This study aims to generate visually useful imagery by preventing cropping while maintaining resolution and minimizing the degradation of stability and distortion to enhance the stability of a video for Augmented Reality applications. The focus is placed on conducting research that balances maintaining execution speed with performance improvements. By processing Inertial Measurement Unit (IMU) sensor data using the Versatile Quaternion-based Filter algorithm and optical flow, our research first applies motion compensation to frames of input video. To address cropping, PCA-flow-based video stabilization is then performed. Furthermore, to mitigate distortion occurring during the full-frame video creation process, neural rendering is applied, resulting in the output of stabilized frames. The anticipated effect of using an IMU sensor is the production of full-frame videos that maintain visual quality while increasing the stability of a video. Our technique contributes to correcting video shakes and has the advantage of generating visually useful imagery at low cost. Thus, we propose a novel hybrid full-frame video stabilization algorithm that produces full-frame videos after motion compensation with an IMU sensor. Evaluating our method against three metrics, the Stability score, Distortion value, and Cropping ratio, results indicated that stabilization was more effectively achieved with robustness to flow inaccuracy when effectively using an IMU sensor. In particular, among the evaluation outcomes, within the “Turn” category, our method exhibited an 18% enhancement in the Stability score and a 3% improvement in the Distortion value compared to the average results of previously proposed full-frame video stabilization-based methods, including PCA flow, neural rendering, and DIFRINT.

A low-cost real-time two-dimensional PC-based ball tracking and control laboratory system is developed to teach real-time control system development with a real physical system instead of computer simulation. The system can be used for a single-student or a two-student competition for the best control algorithm. Training can be given on pattern recognition, modelling, stepper motor control, control strategies and other aspects.

Due to the rapid growth of computer science, image processing technology has recently been widely applied to various fields such as videoconferencing, computer vision and face tracking. However, the development of tracking systems is at a standstill because most studies have used the still camera and pure background. In an effort for advancement, this study proposes a wireless real-time tracking system based on background subtraction, the Gauss motion model, and the Otsu algorithm, to identify a moving object and then pursue it. This system consists of (a) a tracking platform with ARM and robot and (b) an image recognition system that regulates the location of the robot to trace the object and identifies the location of the object from the real-time image, respectively. In practice, the results of this study show that the proposed system can effectively trace the specific moving object and mark the object in the center of the view.

In this paper, approaches for the solution of parallel manipulators forward kinematics have been addressed. Current research on this topic has been analyzed to determine the status and unresolved issues of the research field. Articles on parallel manipulators forward kinematics were reviewed, and numerous approaches for resolving forward kinematics in particular have been identified and published, which may assist researchers in selecting the most appropriate technique for solving the problem in this domain. Researchers have also attempted to apply these methods to a variety of other parallel manipulator-related problems, such as inverse kinematics and dynamics. In robotics, serial manipulators were invented before parallel manipulators. Hence, methodologies for solving forward kinematics were first developed for serial manipulators and many methods are ineffective for parallel manipulators. In this paper, all identified methods can be used for solving forward kinematics of parallel and serial manipulators.