Position Detection System Research Articles

Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management

A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual’s position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual’s position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants’ transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.

Receiver Position Detection System of Wireless Electric Vehicle Charger Based on Novel Active Beacon Structure

Receiver Position Detection System of Wireless Electric Vehicle Charger Based on Novel Active Beacon Structure

TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar.

Human Movement and Attitude Detection System Based on Flexible High Tensile Sensor of Single-Walled Carbon Nanotubes

Excellent mechanical properties and outstanding optical and electrical properties of carbon nanotubes influence the fabrication of suitable devices and micro-electronic parts and systems, and they have been broadly applied in related fields. This paper aims to study the application of flexible high-stretch sensors of single-walled carbon nanotubes in human motion and attitude detection systems. In this paper, algorithm for human motion detection is proposed according to the content of human motion posture detection, and the properties of single-walled carbon tubing are analyzed in detail. The experimental results show that with the increase of carbon nanotube tubing, the reaction speed and sensitivity increase gradually. The reaction speed and sensitivity were 39% when the content was 1.5 wt%, 42% when the content was 2.0 wt%, and 59% when the content was 2.5 wt%. In summary, it can be found that within a certain range, the increase of carbon nanotube content can effectively reduce the hysteresis error of the sensor. It makes the sensitivity of the flexible high-stretch sensor higher, thereby improving the accuracy of the human motion attitude detection system.

Relationship between the shape of cross‐inductive loop coils and the induced voltage for position detection system

AbstractMaglev trains require precise position data to control their propulsion systems. Specifically, sensing accurate phase based on position is necessary, for which cross‐inductive loop coils are used. To minimize phase errors, the envelope of the received voltage must be a sinusoidal wave depending on the position. This letter proposes the shapes of transmitting and receiving coils and presents, through simulation and experimentation, that the shapes of the transmitting and receiving coils and the induced voltage are in a convolution.

Novel Design of Multi-DOF Motor Position Estimation Based on Wireless Power Transmission Modeling

In this paper, a multi-coil wireless power transmission model is presented. The model is applied to the multi-degree-of-freedom (multi-DOF) motor rotor position detection system. The wireless power transmission (WPT) system is evenly distributed on the upper part of the rotor, and the receiving coil is placed on the rotor shaft. When the motor rotor rotates along the three motion states of pitch, spin and tilt, the receiving coil and multi-transmit coil will produce angle deviation. The precise mutual inductance estimation method can be used to obtain the rotor position quickly and avoid the complicated finite element calculation. The system can not only realize the pose estimation of the multi-DOF motor, but also provide flexible energy supplement for the manipulator. The WPT position measurement method is used for PID control of multi-DOF motor, and it is compared with the micro-electro-mechanical-system (MEMS) attitude measurement method. The mechanical sliding measurement method of the encoder is taken as a reference. The results show that the position detection method of WPT has high accuracy, and the estimation error is less than 2%.

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

Research on wireless power transmission of attitude detection system of MEMS sensor for spherical motor

Research on wireless power transmission of attitude detection system of MEMS sensor for spherical motor

End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN

An end face attitude detection system for special steel bars is designed to solve the problem of defect localization for steel bar grinding. A circle detection method based on improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed for calculating special steel bars’ end face attitude. Firstly, the images are subjected to edge detection, connected region marking, and improved DBSCAN in accordance with the image characteristics. After that, the arcs belonging to the same circle are clustered into the same category to create virtual connected regions. Then, circle parameters of a virtual connected region are clustered using an improved DBSCAN algorithm. The actual circle parameter is obtained by calculating the centroid of each category. Finally, the vector is generated under the set coordinate system, passing through the center of the circumcircle of the steel bar end and one endpoint of the two-dimensional code, and the angle of the vector is calculated to determine the attitude of the special steel bar’s end face. The experimental results demonstrate that the method can obtain an attitude angle resolution of 0.2 degrees with an error range of ±0.1 degrees. This will provide accurate defect localization support for the digitization and intelligence of the grinding platform on the special steel bar production line.

Triboelectricity Based Self‐Powered Digital Displacement Sensor for Aircraft Flight Actuation

AbstractThe utilization of unmanned aerial vehicles (UAVs) is on the rise across various industries. In such a scenario, the issue of flight safety for these UAVs becomes increasingly paramount. Currently, UAVs exhibit shortcomings in flight attitude perception compared to more mature manned aircraft, especially concerning the position sensing of flight actuation, which poses significant safety risks. Mature position monitoring solutions for flight actuation used in manned aircraft cannot be directly integrated into systems of UAV due to compatibility issues. This necessitates the development of new position sensing technologies to address this challenge. Triboelectric nanogenerators, with their advantages of miniaturization, self‐powering capabilities, and the ability to generate voltage‐level electrical signals, are chosen to form a part of the position detection system for sensors in UAVs. In this study, a self‐powered displacement sensor is developed that utilizes frictional charge separation signals. This sensor is specifically designed to monitor the position status of the flight actuators in UAV. With a compact volume of <11.1 cm3 and a weight of <9.5 g, this sensor is lightweight efficient and adaptable for practical applications.

An innovative and non-intrusive Hall effect sensor-based rotor position detection system for external rotor switched reluctance motor

An innovative and non-intrusive Hall effect sensor-based rotor position detection system for external rotor switched reluctance motor

Non‐Contact Triboelectric Nanogenerator

AbstractWith the rapid development of science and technology, there is an increasing demand for sustainable energy sources. Although triboelectric nanogenerators (TENGs) can realize self‐powered supply by using various weak mechanical energy, the device's robustness and reliability are seriously affected by frequent, direct, and long‐term mechanical shocks, as well as negative environmental factors during equipment operation. Therefore, researchers have developed non‐contact triboelectric nanogenerators (NC‐TENGs) based on the principle of electrostatic induction. This technology enables electricity generation via relative movement of friction layers even when they are not in direct contact, which consequently leads to enhance device robustness, prolonged service life, and broadened operation scenarios. In this review, the recent progress of NC‐TENG from fundamental theory to practical applications is systematically summarized. The basic structure and working principle of NC‐TENG are first introduced, followed by a discussion of the devices’ structural construction and performance optimization methods. Furthermore, applications of NC‐TENG in automatic force position detection system, non‐contact human‐machine interface, and electrical energy harvesting are reviewed. Finally, a brief prospectus for the future development of NC‐TENG is provided.

Novel method to detect the speed and position of the HTS Maglev train by using the TMR sensor and the magnet arrays

Magnetic levitation (Maglev) train’s position and speed detection system facilitates the control of the Maglev train. Yet nearly all previous schemes are unsuitable for the high temperature superconducting (HTS) Maglev train. New sensors and signal sources are expected to improve the system. Here the tunnel magneto resistance (TMR) sensor and the magnetic arrays are used to realize the speed and position detection and the improved automatic multiscale-based peak detection (AMPD) algorithm is applied. Furthermore, the algorithm’s adaptability to various operating conditions is verified and the system can be applied in different working conditions. Finally, a preliminary experimental test is carried out. Such a system, accompanied by the equipment and the algorithm, offers results of accuracies of 95%–97% for speed measurement and 1 cm–3 cm for positioning, providing an avenue to improve the performance of HTS Maglev train’s operating control system.

A Lightweight Vision-Based Measurement for Hand Gesture Information Acquisition

The utilization of hand gesture recognition (HGR) can achieve a simpler and more humanized human–computer interaction (HCI) system, which can be used to simplify some aspects of production life and can also be used for sign language interpretation robots. However, HGR has a highly customized design. It is a very time-consuming process to design and train a separate model for each set of datasets. Therefore, designing a hand gesture information (HGI) acquisition instrument to analyze the rigid body motion and internal posture of the hand at the same time is a necessary process. This article presents a lightweight vision-based measurement (VBM) system to achieve this function. The system is implemented based on the convolutional neural network (CNN) structure, the improved Kalman filter (KF) is used to compensate error of fast moving, and the testing effect is good. After testing, the improved hand position detection system has the highest recall rate in the single-stage network. Compared with the original you look only once (YOLO)-v3, the number of parameters is reduced by 46.7M, but the recall rate is increased by 0.28%, which solves the loss of hand detection under complex background. With the help of the improved KF algorithm, the average precision (AP) metric of the prediction box is improved by 2.0 compared with the previous one. Compared with the simple baseline, the area under curve (AUC) metric of the keypoints detector is improved by 0.5%.

Camera-based high precision position detection for hybrid additive manufacturing with laser powder bed fusion

Additive manufacturing (AM) in general and laser powder bed fusion (PBF-LB/M) in particular are becoming increasingly important in the field of production technologies. Especially the high achievable accuracies and the great freedom in design make PBF-LB/M interesting for the manufacturing and repair of gas turbine blades. Part repair involves building AM-geometries onto an existing component. To minimise the offset between component and AM-geometry, a precise knowledge of the position of the component in the PBF-LB/M machine is mandatory. However, components cannot be inserted into the PBF-LB/M machine with repeatable accuracy, so the actual position will differ for each part. For an offset-free build-up, the actual position of the component in the PBF-LB/M machine has to be determined. In this paper, a camera-based position detection system is developed considering PBF-LB/M constraints and system requirements. This includes finding an optimal camera position considering the spatial limitations of the PBF-LB/M machine and analysing the resulting process coordinate systems. In addition, a workflow is developed to align different coordinate systems and simultaneously correct the perspective distortion in the acquired camera images. Thus, position characteristics can be determined from images by image moments. For this purpose, different image segmentation algorithms are compared. The precision of the system developed is evaluated in tests with 2D objects. A precision of up to 30μm in translational direction and an angular precision of 0.021∘ is achieved. Finally, a 3D demonstrator was built using this proposed hybrid strategy. The offset between base component and AM-geometry is determined by 3D scanning and is 69μm.

Research on Intelligent Control System of Hydraulic Support Based on Position and Posture Detection

In the study of coal mining, the position and posture, the working status, and the straightness of the hydraulic support are critical to the safety of the coal mine. To detect and control the position of a hydraulic support, a model for solving the self-position and posture and a model for solving the relative position and pose of a hydraulic support based on the principle of three points determining a plane in space were established. Then, with the help of particle swarm optimization algorithm and Bang-Bang control algorithm, the hydraulic support position and the posture detection and control system are constructed. Finally, according to the requirements of sensor data collection and online monitoring of the position and posture of hydraulic support, a hydraulic support posture detection and control system is developed, and the simulation experiments of hydraulic support cylinder movement sample collecting, hydraulic support following motion and hydraulic support independent following machine are carried out on the self-developed three-machine intelligent control simulation experiment platform of the working face. However, the test under this simulation platform did not add the load of the top beam. The results show that the self-pose solution model and the relative-pose solution model of the hydraulic support can be used to quickly monitor the position and posture of the hydraulic support group. In the hydraulic support cylinder following motion movement, the largest following error of each cylinder is the balance cylinder, and its maximum error is 2.6 mm. In the hydraulic support independent following machine simulation, the maximum motion error of the support base is 2.2 mm. The position and posture detection and control system developed for hydraulic support can realize accurate detecting and control the position and posture of hydraulic support, as well as meet the requirements for industrial applications.

Spatial position detection system for high reflection strip transmission

Spatial position detection system for high reflection strip transmission

Investigation of a metrological atomic force microscope system with a combined cantilever position, bending and torsion detection system

Abstract. This article presents a new metrological atomic force microscope (MAFM) head with a new beam alignment and a combined one-beam detection of the cantilever deflection. An interferometric measurement system is used for the determination of the position of the cantilever, while a quadrant photodiode measures the bending and torsion of the cantilever. To improve the signal quality and reduce disturbing interferences, the optical design was revised in comparison to the systems of others (Dorozhovets et al., 2006; Balzer et al., 2011; Hausotte et al., 2012). The integration of the MAFM head in a nanomeasuring machine (NMM-1) offers the possibility of large-scale measurements over a range of 25mm×25mm×5 mm with sub-nanometre resolution. A large number of measurements have been performed by this MAFM head in combination with the NMM-1. This paper presents examples of the measurements for the determination of step height and pitch and areal measurement.

Utilizing the moiré deflectometry-based detection method improves the detection sensitivity for gold nanoparticles trapped by optical tweezers

Gold nanoparticles (GNPs) are very often used as handles for nanotechnological micromanipulation. In this regard, optical trapping of GNPs is of great importance, in which locating the trapped GNP within the focal spot with nanometer precision is crucial. Very recently, we have introduced a new position detection system for optical tweezers based on moiré deflectometry (MD). Here we show, both theoretically and experimentally, that an MD detection system could provide significantly larger detection sensitivity for a trapped GNP compared to that provided by conventional back focal plane (BFP) detection systems. For instance, for a trapped 200 nm GNP, the detection sensitivity provided by the MD detection method is 280% better than that provided by the BFP detection method.

Assessment of Sprint Parameters in Top Speed Interval in 100 m Sprint-A Pilot Study Under Field Conditions.

Improving performances in sprinting requires feedback on sprint parameters such as step length and step time. However, these parameters from the top speed interval (TSI) are difficult to collect in a competition setting. Recent advances in tracking technology allows to provide positional data with high spatio-temporal resolution. This pilot study, therefore, aims to automatically obtain general sprint parameters, parameters characterizing, and derived from TSI from raw speed. In addition, we propose a method for obtaining the intra-cyclic speed amplitude in TSI. We analyzed 32 100 m-sprints of 7 male and 9 female athletes (18.9 ± 2.8 years; 100 m PB 10.55–12.41 s, respectively, 12.18–13.31 s). Spatio-temporal data was collected with a radio-based position detection system (RedFIR, Fraunhofer Institute, Germany). A general velocity curve was fitted to the overall speed curve (vbase), TSI (upper quintile of vbase values) was determined and a cosine term was added to vbase within TSI (vcycle) to capture the cyclic nature of speed. This allowed to derive TSI parameters including TSI amplitude from the fitted parameters of the cosine term. Results showed good approximation for vbase (error: 5.0 ± 1.0%) and for vcycle (2.0 ± 1.0%). For validation we compared spatio-temporal TSI parameters to criterion values from laser measurement (speed) and optoelectric systems (step time and step length) showing acceptable RMSEs for mean speed (0.08 m/s), for step time (0.004 s), and for step length (0.03 m). Top speed interval amplitude showed a significant difference between males (mean: 1.41 m/s) and females (mean: 0.71 m/s) and correlations showed its independence from other sprint parameters. Gender comparisons for validation revealed the expected differences. This pilot study investigated the feasibility of estimating sprint parameters from high-quality tracking data. The proposed method is independent of the data source and allows to automatically obtain general sprint parameters and TSI parameters, including TSI amplitude assessed here for the first time in a competition-like setting.