Movement Monitoring System Research Articles

Aircraft Surface Movement and Operation Monitoring Systems in General Aviation and Commercial Airports: A State-of-the-Art Review

Aircraft Surface Movement and Operation Monitoring Systems in General Aviation and Commercial Airports: A State-of-the-Art Review

Rancang Bangun Sistem Pemantau Gerak Otot Tangan Pada Pasien Stroke Berbasis Internet Of Things (IoT)

Monitoring hand muscle movements in stroke patients is a crucial aspect of the rehabilitation process to track therapy progress. However, manual monitoring by medical personnel requires significant time and effort. To address this issue, this study aims to design and develop a hand muscle movement monitoring system based on the Internet of Things (IoT). The system is designed to monitor finger movements using flex sensors attached to a glove, with data transmitted wirelessly to a web-based monitoring platform. The methodology used in this research involves the development of both hardware and software to record and transmit finger movement data in real-time. The system is also equipped with notifications via buzzers and LEDs to assist patients in adhering to therapy schedules set by medical professionals. Testing was conducted on stroke patients and healthy individuals to measure the system's effectiveness. The results indicate that this system can accurately and consistently monitor hand muscle movements in stroke patients. Trials conducted over five consecutive days on stroke patients showed that the average finger measurements ranged from 9.11 to 9.97, with significant differences in finger strength compared to healthy individuals. These findings suggest that this monitoring system is effective in supporting the rehabilitation process of stroke patients, enabling medical personnel to monitor and evaluate patient progress more accurately.

Human movement monitoring system for classification of strength exercises and verification of their execution technique

Human movement analysis is critical to optimizing sports training and influencing exercise intensity and effectiveness. In the age of modern technology, more and more advanced systems are emerging to support coaches and expand the range of analysis performed. This article aims to verify that artificial intelligence, together with machine learning algorithms, can accurately classify exercises in a dynamic gym environment and effectively assess the correctness of their performance. For the initial analysis of movement, the Google MediaPipe Pose model was used, which was responsible for detecting the human silhouette and determining the coordinates of the position of critical joints. Based on these coordinates, the angles between each joint were calculated, and then their sequences were further analyzed. The sequences were analyzed using the following three algorithms: support vector machine (SVM), dense neural network, and LSTM recurrent network. As a result, the system based on recurrent LSTM networks achieved the best prediction efficiency of approximately 98%, enabling accurate exercise classification. Subsequently, verification of the activities' correctness was also carried out, and the system, based on recursive LSTM networks, again achieved the best efficiency, this time equal to 96% on average for all exercises. On this basis, it was concluded that the discussed approach enables practical analysis of human movement, which can significantly improve training methods and facilitate coaching work.

Research and application on in-situ monitoring of strata movement in submarine cold seeps area based on three-dimensional MEMS sensor network

Accurate monitoring of strata movement is crucial for understanding the geomorphological evolution of the cold seeps. However, the complex environments and inherent challenges of deep-sea operations present significant obstacles for existing observation methods. In this paper, we present a strata movement monitoring system based on a three-dimensional micro-electromechanical system (MEMS) sensor network that includes multiple short and long sensor arrays. The movement vector is reconstructed using triaxial-acceleration and cubic Hermite interpolation. In laboratory simulations with vertical and horizontal displacement of 250 mm and 100 mm, respectively, the root mean square error of the reconstruction model is 3.74 mm. Two sensor arrays were deployed and operated for 4 days in cold seeps in South China Sea, and the observed displacement showed micro-tremors, and the displacement ranges in the x- and y- directions were (−0.4 mm, 0.18 mm) and (−0.43 mm, 0.19 mm), respectively. These results affirm the feasibility of the proposed system.

Continuous postural and movement monitoring system for wheelchair users assessment

The aim of this study is to develop a system to assist in diagnosing individuals with reduced mobility. Specifically, the posture of wheelchair users, which is closely related to their functional status, can be adversely affected by everyday movements and obstacles. To address this, the study proposes a combined monitoring device that tracks real-time users posture and wheelchair movement (distance, orientation, vibrations...) and quantifies the relationship between both. Tests were conducted on various environments, such as smooth floors, ramps, pavements, tramways, and lifts, to establish the relationship between wheelchair motion and user posture. Specific evaluation metrics were used to assess this relationship and the device’s ability to identify it. The results confirm the success of the combined approach, as the system can detect the impact of external agents on user posture. This robust and reliable device helps professionals quantify users functional status and understand the challenges faced by wheelchair users.

Development and field evaluation of a low-cost bridge bearing movement monitoring system

Development and field evaluation of a low-cost bridge bearing movement monitoring system

Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision).

Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment. Fifty frail and prefrail adults were recruited from the Brigham and Women's Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (Romberg, semitandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually and by the multimodal movement monitoring system's activity recognition and scoring algorithms, which were previously trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as the degree of agreement between the actual and system-identified activities. In an exploratory analysis of a subset of functional activity data, the accuracy of a preliminary activity-scoring algorithm was also evaluated. Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2(2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69, 0.83]). Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates inertial measurement unit and vision data. Further algorithm training with ecologically valid data and a kitted system that is independently usable by patients are needed before fully autonomous, functional movement assessment is realizable. Functional movement assessments that can be administered in the home without a clinician present have the potential to democratize these evaluations and improve care access.

Retracted: Research on Fitness Movement Monitoring System Based on Internet of Things.

[This retracts the article DOI: 10.1155/2022/5120556.].

Wayside wheelset lateral motion detection and vehicle hunting instability evaluation

Wheel profile wear, or bogie suspension failure will result in hunting instability of the vehicle system which will lead to dramatic changes in the lateral motion of the wheelset. The occurrence of hunting instability will worsen the ride performance of the vehicle or even cause the danger of wheelset derailment. Thus, the in-time and effective detection of wheelset lateral motion is very important for the vehicle hunting stability evaluation. By installing a certain amount of displacement sensors on the rail side along the track and detecting the distance between the wheel and rail when the wheelset passing by, the lateral displacements of the wheelset in measuring points can be obtained. The detection method of wheelset lateral motion by the wayside is studied, which includes the measuring principle, structure design, sensor installation, data acquisition and data analysis, and the application of the method to on-site detection for typical instability cases are demonstrated. At the same time, innovate on the existing on-board hunting instability monitoring method, proposed an evaluation method of hunting instability for wayside lateral movement monitoring system. The results show that: installing 20 eddy current sensors on both sides of the straight track to measure the lateral displacement of the wheelset can meet the requirement of detecting the vehicle hunting instability. At the same time, analyzing the measurement data and fitting the maximum value of the signal collected by each sensor can obtain the lateral trajectory of the wheelset. The wheelset lateral displacement can be used to monitor the occurrence of vehicle hunting instability if periodic motion is detected. The amplitude of the motion can determine the hunting instability level, and the frequency can be used to describe the wheel wear level and calculate the wheel/rail contact equivalent conicity. This method is able to provide a reliable data support for the vehicle operation safety. The proposed monitoring method and monitoring index are also of reference value to the development of other wayside hunting instability monitoring systems.

Research on an Identification Method for Wheelset Coaxial Wheel Diameter Difference Based on Trackside Wheelset Lateral Movement Detection

The wheelset coaxial wheel diameter difference is one of the most common wheel faults of railway vehicles. The existence of the wheelset coaxial wheel diameter difference may lead to the off-load operation of vehicles, resulting in abnormal wheel tread wear, leading to the deterioration of the wheel–rail contact relationship, resulting in the deterioration of the vehicle’s operating stability and comfort, and even leading to an increase in the derailment coefficient, affecting the running safety. In order to monitor the freight car wheelset coaxial wheel diameter difference online, a vehicle–track coupling dynamics model based on a trackside detection method was established, and the response of rail lateral displacement under the condition of the wheelset coaxial wheel diameter difference was analyzed. The results show that the existence of the wheelset coaxial wheel diameter difference can lead to a deviation in the vehicle’s run, with an increase in the wheelset coaxial wheel diameter difference and an increase in the lateral offset of wheelset increases. The impact of vehicle unbalance loading on the lateral movement of the wheelset is much smaller than that of the wheelset coaxial wheel diameter difference. The existence of the wheelset coaxial wheel diameter difference can be better reflected by detecting the wheelset’s lateral displacement. On straight line, the variation of lateral displacement has no infection of vehicle speed, but shows a quadratic growth trend with the wheelset coaxial wheel diameter difference. Based on this, the mapping relationship between the wheelset coaxial wheel diameter difference and wheelset lateral displacement can be obtained. Through a mapping relationship, the size of the wheelset coaxial wheel diameter difference can be reversed precisely through the detection of a trackside lateral movement monitoring system. The reliability of the identification method was verified with a specific test on the trackside monitoring system.

Initial strategy of an assistive technology for rehabilitation and monitoring of elderly patients

This article presents an initial assistive technology strategy for the rehabilitation and monitoring of elderly patients, focused on lower limb muscle recovery, from a condition of low gait stability to recovery without the use of crutches. The project is in progress, and its first module, the recovery one, measures the flexion and extension of the lower limbs in degrees and sends the data to a database for analysis by the physiotherapist. The second module consists of the design of support for a telephone integrated to the crutch, to facilitate the integration with existing applications, also containing an embedded system for integration with the first module. When the patient is already on crutches, the cell phone and an embedded system coupled to the patient are used, which send signals in case the patient falls. The results are still partial and from isolated parts of the project, so that, after the completion of the modules, it will be possible to integrate and quantitatively analyze the results. The voltage source showed a current output that was maintained despite the load and without a voltage drop. The movement monitoring system through the camera recognized the knee flexion movement. The initial architecture of the system managed to include a system involving computer graphics, and screens that concern the human-computer interface. The crutch and the telephone support underwent usability tests that proved basic mechanical resistance to not present elementary difficulties in sustaining a person and a cellular device.

Optimizing internal biosecurity on pig farms by assessing movements of farm staff

For internal biosecurity, it is important to separate different age groups in a pig farm and to stick to specific working lines when visiting the barns. Currently, there is no research on the movements of farm staff on pig farms. The objectives of this observational study were to assess movements of farm staff on pig farms, to assess risky movements and to investigate whether movements differ according to time (week of the batch farrowing system (BFS) and weekday vs. weekend) and unit (farrowing, gestation/insemination, nursery, and fattening unit). Five commercial sow farms participated and on each farm, an internal movement monitoring system was installed. Detection points were installed throughout the farm and workers had to wear a personal beacon. Movement data were collected from 1 December 2019 until 30 November 2020. The following sequence of movements was considered as safe: (1) dressing room, (2) farrowing, (3) gestation/insemination, (4) nursery, (5) fattening, (6) quarantine, and (7) cadaver storage. Movements in the opposite direction were considered as risk, unless a dressing room was visited in between. The total number of movements differed according to week of the BFS, and was highest in insemination and farrowing week. The percentage of risky movements was influenced by week of the BFS for two farms, and was highest around weaning. The percentage of risky movements varied between farms and ranged from 9 to 38%. There were more movements on a weekday compared to a weekend day. There were more movements towards the farrowing and gestation/insemination unit in insemination and farrowing week compared to other weeks of the BFS, but week of the BFS had no impact on movements towards nursery and fattening unit. This study showed that there were a lot of (risky) movements on pig farms and that these movements varied according to week of the BFS, day of the week, and unit. This study creates awareness, which could be a first step in optimizing working lines. Future research should focus on why certain risky movements occur and how these can be avoided to achieve better biosecurity and higher health status on farms.

Motion Analysis Using Global Navigation Satellite System and Physiological Data

Motion analysis by wearable sensors forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body and the simultaneous acquisition of signals from further sensors. The application is related to the monitoring of physical activity and the analysis of the heart rate dynamics during the run at route segments of different slopes with changing speed, forming an alternative to the heart monitoring on the treadmill ergometer. The proposed computational method includes digital methods of data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The detection of the patterns of motion includes segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This method of classification provides a comparison of neural networks, support vector machine, Bayesian, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface suggest the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems as well.

Wearable Leg Movement Monitoring System for High-Precision Real-Time Metabolic Energy Estimation and Motion Recognition

Comprehensive and quantitative assessment of human physical activity in daily life is valuable for healthcare, especially for those who suffer from obesity and neurological disorders or are at high risk of dementia. Common wearable devices, e.g., smartwatches, are insufficient and inaccurate for monitoring highly dynamic limb movements and assessing human motion. Here, we report a new wearable leg movement monitoring system incorporating a custom-made motion sensor with machine learning algorithm to perceive human motion accurately and comprehensively during diverse walking and running actions. The system enables real-time multimodal perceptions of personal identity, motion state, locomotion speed, and energy expenditure for wearers. A general law of extracting real-time metabolic energy from leg movements is verified although individual gaits show differences. In addition, we propose a novel sensing configuration combining unilateral lower leg movement velocity with its angular rate to achieve high accuracy and good generalizability while simplifying the wearable system. Advanced performances in personal identification (accuracy of 98.7%) and motion-state recognition (accuracy of 93.7%) are demonstrated. The wearable system also exhibites high-precision real-time estimations of locomotion speed (error of 3.04% to 9.68%) and metabolic energy (error of 4.18% to 14.71%) for new subjects across various time-varying conditions. The wearable system allows reliable leg movement monitoring and quantitative assessment of bodily kinematic and kinetic behaviors during daily activities, as well as safe identity authentication by gait parameters, which would greatly facilitate smart life, personal healthcare, and rehabilitation training.

Metasurface-based augmented reality headset equipped with an eye movement monitoring system

In this paper, a metasurface-based waveguide display equipped with an eye movement monitoring system is presented. In the suggested device, the functions of the eye movement system and AR are completely independent of each other and are designed in two separate sections at wavelengths 775 nm and 635 nm respectively. In the next part, in order to investigate the effects of the shape of the waveguide on FOV and efficiency, a multifunctional display system comprise of a single rectangular waveguide with two sensitive polarization channels are designed to operate as an AR and eye movement monitoring systems simultaneously at visible and IR wavelengths respectively. In both devices, monitoring eye movements can be done over the range of − 24° to 24° and the digital images are displaced in the user’s FOV based on horizontal eye positions. Although the first suggested system is heavier than one, its FOV is almost more than twice of the second system. The results indicate that metasurface-based waveguide technology can be considered as an appropriate platform for developing wearable eye movement systems.

An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %∼52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %∼54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.

A High-Precision Extensometer System for Ground Displacement Measurement Using Fiber Bragg Grating

The design and performance of an innovative high-precision extensometer system, fabricated inexpensively using 3D printing technology, are discussed in this paper. In the development of the extensometer, an embedded Fiber Bragg Grating (FBG) strain sensor was 3D printed using a thermoplastic polyurethane (TPU) filament, which was used as the primary sensing element of the extensometer system, taking advantage of its excellent flexibility and high sensitivity to variations in localized strain. In the performance assessment carried out, the results obtained during the experimental test and validation have demonstrated that it could be used very effectively to measure strain variations, with an average wavelength responsivity of 0.0158 nm/cm (for displacement) and very high linearity (up to 99&#x0025;). Furthermore, the protection integrated into the sensor systems design makes it well-suited for in-the-field applications, such as monitoring ground displacements which can lead to dangerous slippages of sloped earthworks. In addition, a field testing of the extensometer under simulated conditions has shown that a Fiber Bragg Grating (FBG)-based approach could be applied effectively to the measurement of strain, offering a wavelength responsivity of 0.0012 nm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula> (for strain-sensitive FBGs) under both dry and wet soil conditions. Moreover, taking advantage of the high (&#x007E;99&#x0025;) linearity, the extensometer is a reliable instrument for use in different underground conditions, creating an easy-to-use ground movement monitoring system which then enables an excellent representation of the displacement profile of the earth to be made.

Research on Fitness Movement Monitoring System Based on Internet of Things.

A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.

SISTEM KEAMANAN MOBIL MENGGUNAKAN GLOBAL POSITIONING SYSTEM BERBASIS RASPBERRY PI 3

The current car security system is still lacking to minimize owner concerns. To anticipate the theft of a car, it requires the addition of a security system that can find out where the location of the car when it was stolen using Global Positioning System (GPS) technology. Through the concept of IoT (Internet of Things) which can support a vehicle movement monitoring system wirelessly integrated using Raspberry Pi as a control system for car security. When a burglar opens the car door by force, the system installs a magnetic sensor as a notification to the owner via Telegram. Cars that have been successfully taken by thieves, the owner can turn off the engine with the help of a Relay on a raspberry pi device.

Soil Movement Monitoring System Based on IoT using Fuzzy Logic

Landslide is one of the disasters that often occurs in several areas in Indonesia, especially in hilly areas, valleys, and volcanoes. Soil conditions in some parts of Indonesia are classified as prone to landslides. The latest data from the Central Statistics Agency related to landslides in 2018 occurred as many as 10,246 events with the highest incidence on the island of Java IoT-based ground motion monitoring using fuzzy logic is a tool that is able to detect ground movements that can trigger landslides. The manufacture of this tool is based on the ig-norance of the community in predicting the occurrence of landslides. To avoid this, an early warning tool is needed in the delivery of information that is easily understood by anyone, especially the public. This tool consists of a Microcontroller, Weather Sensor, Rain Sensor, Ground Movement Sensor, and GSM Shield as well as programs to make it hap-pen. This system was created to provide information to the public directly in land-slide-prone areas. With this early warning system, it is hoped that people who are in landslide-prone loca-tions will know more quickly and can monitor the condition of landslide-prone areas so that they will be more alert to possible dangers that come suddenly, especially fatalities, can be minimized. Through this tool can also be known when the weather is cloudy, raining as well as movement or signs of ground movement, can be monitored and monitored automatically. directly by everyone from mobile phones through "SIPEGERTA" Land Movement System in Wonosalam District, Jombang Regency