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This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports. [ABSTRACT FROM AUTHOR]

The study presents a new approach for assessing plantarflexor muscles' function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles' function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total −0.1118, 0.1127 s, T-lower −0.1152, 0.1179 s, and T-total −0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles' functionality in those unable to perform tests in standing positions. [ABSTRACT FROM AUTHOR]

Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: "standing and eating", "standing and ruminating", and "laying and ruminating". Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study. [ABSTRACT FROM AUTHOR]

Chaotic dynamics, coexistence of attractors and nonlinear resonances are some of the most complex phenomena that affect the operation and performance of complex systems in general, and rotating machines in particular. This work deals with the analysis of the route to chaos and the coexistence of the attractors of a parametrically damped gyroscope. After having modeled the dynamics of the gyroscope under the influence of the control force, using numerical simulations, the types of resonances are sought, and then the amplitudes and the frequencies of the oscillations are determined. Subsequently, the study and the control of chaotic dynamics and the coexistence of gyroscope attractors are carried out using bifurcation diagrams, Lyapunov exponents and phase portraits using red fourth-order Runge–Kutta algorithm. It is shown that a high value of the parameter α considerably reduces the amplitude and the resonant frequency of the gyroscope and therefore can eliminate the resonance of the gyroscope when Ω = ω , Ω = 2 ω and Ω = 3 ω , while a large value of the parameter β or of the amplitude f of periodic excitation accentuates the resonance of the gyroscope in these cases. Similarly, an increase in the amplitude h of the parametric damping increases the resonance amplitude. Finally, the chaotic behavior and the coexistence of the attractors of the gyroscope studied depend on the values of each of the parameters α , β , f , h and the frequencies of the parametric excitation and the damping parametric force. [ABSTRACT FROM AUTHOR]

In response to the current low work efficiency of soil ridge-working machinery, as well as its poor stability, passability, and adaptability, this paper designs an independent leg-type working platform that can autonomously adjust its vehicle attitude through LiDAR scanning in a soil ridge-working environment. The platform, in terms of its mechanism and structural design, adopts dual parallelogram mechanisms, dual lead screw mechanisms, and independent column leg mechanisms, with a maximum adjustable ground clearance of 107 mm and a maximum wheelbase adjustment of 150 mm. A gyroscope is mounted at the center of the platform for attitude adjustment, ensuring the accurate data collection of the ultrasonic ranging module. Moreover, the platform adopts an adaptive adjustment method based on vehicle attitude and soil ridge shape parameters, obtaining soil ridge parameters through LiDAR and combining ultrasonic ranging module data with stepper motor pulse signals to obtain the absolute vehicle attitude parameters, using first and second linear regression methods to adjust the vehicle attitude and other working parameters. A prototype was also created, and the test data from the soil obtained through experiments show that, after leveling with the gyroscope leveling algorithm, the average value of the pitch angle is up to 0.6154°, and the average value of the roll angle is up to 0.9989°, with the maximum variance of the pitch angle being 0.0474° and the maximum variance of the tilt angle being 0.1320°. After the ultrasonic ranging module data are filtered by the Kalman filter, the maximum variance is 0.0304, and after applying the final fusion algorithm, the maximum variance is only 0.0085. The LiDAR measurement width value deviates from the actual width value by no more than 1.0 cm, and the LiDAR measurement height value deviates from the actual height value by no more than 1.0 cm. The platform's actual adjusted width deviates from the actual soil ridge width by no more than 2.0 cm, and the platform's actual adjusted height deviates from the actual soil ridge height by no more than 1.2 cm. This platform can improve the passability, adaptability, and stability of agricultural machinery in soil ridge work and provide technical references for subsequent related research. [ABSTRACT FROM AUTHOR]

The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring. [ABSTRACT FROM AUTHOR]

Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: “standing and eating”, “standing and ruminating”, and “laying and ruminating”. Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study.

Abstract This paper presents an optoplasmonic gyroscope that employs a novel bent-hybrid structure, and works double-sided. The proposed device is integrable without moving parts, and simply has a robust configuration. The detection mechanism is based on surface plasmon polaritons (SPPs), and the sensor consists of a laser, a bent-metal layer, and a photo-detector (PD). Based on the simulations, the proposed gyroscope provides significant characteristics of a measurement range of ± 45000°/s, an optical sensitivity of 6.025 µ/(°/s), a total sensitivity of 2.41 µA/W(°/s), an ultra-high resolution ability of 16.598 µ°/s, and an accuracy of 99.999%. The dimensions are 5 × 5 × 5 µm3, and the measurement time is 1 ms. The operational wavelength is at visible range of λ = 630 nm. Additionally, the effects of various parameters, including metal material, metal thickness, and laser wavelength, on the gyroscope performance are comprehensively studied.

The primary motivation behind this study is the aspiration to design a prosthetic foot that demonstrates enhanced functionality, enabling more active and prompt responses, particularly tailored for individuals with below-knee amputations. This goal underscores the intention to create a prosthetic foot with the capability to execute foot movements in a more natural and effective manner. A new 1D-ResCNN model has been proposed for the rapid and accurate classification of foot movements based on user intent in the context of a prosthetic limb. This research introduces an innovative approach by integrating inertial measurement units with deep learning algorithms to advance the development of more functional prosthetic feet, specifically tailored for below-knee amputees. Leveraging wearable technologies, this method allows for the prolonged monitoring of foot movements within the users' natural environments. The dual benefits of cost reduction and enhanced user experience are achieved through this combination of advanced technologies, providing a promising avenue for the evolution of prosthetic foot design and usage. The results obtained with this model are satisfying both in terms of speed and accuracy with 99.8% compared to other methods in the literature. [ABSTRACT FROM AUTHOR]

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use. [ABSTRACT FROM AUTHOR]

Adaptive active suspension systems, integral to specialized vehicles, hold significance for their stability and safety. This study introduces a novel adaptive active suspension system featuring four independently controlled wheels employing wheel-hub motors, hydraulic cylinders, pump motor power, controllers, and sensors. A rapid and, within a certain range, leveling and height adjustment control strategy is proposed for this system, utilizing the Kalman filter algorithm. Additionally, the paper examines the front-wheel Ackermann steering model and four-wheel reverse Ackermann transition model to enhance the suspension's stability. Subsequently, experiments on leveling and height adjustment are conducted, demonstrating the system's capability to swiftly and accurately rectify the vehicle's deviation angle within the specified threshold. Following adjustments made by the leveling and height control algorithm, the vehicle body promptly returns to the preset level state and designated height. The leveling control system holds broad applicability in intelligent agriculture, logistics handling, off-road equipment, and other domains, presenting significant practical utility. [ABSTRACT FROM AUTHOR]

In this article, the design and simulation of an optical gyroscope based on a hybrid ring resonator using the Sagnac effect are presented. In this design, by using a transfer matrix, numerical analysis, and tight-binding methods, as well as managing control factors in the sensor, such as the transfer coefficient, ring radius of each resonator, operating wavelength, and coupling coefficient, the sensor output parameters are investigated. The output parameters include sensitivity, angular random walk (ARW), minimum angular rotation, minimum group velocity, and the phase shift resulting from the power ratio changes. In this gyroscope, the maximum group velocity in terms of angular rotation with the increase of the number of rings is an exponential function in the range of 750–2500 m/s. The output phase changes as a linear function with an upward slope as the radius of the rigs increases in the range of 2130–3430 nm. Also, with the increase in the radius of the rings, the phase change relative to the operating wavelength decreases. By increasing the imaginary part of the coupling and transfer coefficients in this sensor, the changes in output power decrease compared to the output phase. The transmission peaks in the gyroscope output increase due to the rise in the number of rings. The sensitivity lowers with an increasing number of rings. The temperature affects the gyroscope output, and an increase in temperature causes a rise in ARW and a decrease in the signal-to-noise ratio. Also, the signal-to-noise ratio increases with the rise of the output power due to the transfer and coupling coefficients. [ABSTRACT FROM AUTHOR]

Existing homography and optical flow methods are erroneous in challenging scenes, such as fog, rain, night, and snow because the basic assumptions such as brightness and gradient constancy are broken. To address this issue, we present an unsupervised learning approach that fuses gyroscope into homography and optical flow learning. Specifically, we first convert gyroscope readings into motion fields named gyro field. Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details. Meanwhile, we propose a homography decoder module (HD) to combine gyro field and intermediate results of SGF to produce the homography. To the best of our knowledge, this is the first deep learning framework that fuses gyroscope data and image content for both deep homography and optical flow learning. To validate our method, we propose a new dataset that covers regular and challenging scenes. Experiments show that our method outperforms the state-of-the-art methods in both regular and challenging scenes. The code and dataset are available at https://github.com/lhaippp/GyroFlowPlus. [ABSTRACT FROM AUTHOR]

The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model, which is capable of predicting gyroscope counterweights under small-sample conditions. The results of our experiments demonstrate the effectiveness of the proposed approach in predicting dynamic gyroscope counterweight in its assembly process. Our approach surpasses current methods in mitigating repetition rates and enhancing the assembly efficiency of gyroscopes. [ABSTRACT FROM AUTHOR]

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein–Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky–Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein–Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs. [ABSTRACT FROM AUTHOR]

The motion of a rigid body (a gyroscope) is one of the key issues in classical mechanics. It remains a significant challenge, as evidenced by its extensive practical implementations in various scientific disciplines and engineering operations. It is important to obtain analytical solutions, as they provide solutions that depend directly on the system's parameters, which can be definitively interpreted. The coupling of numerical and analytical solutions allows for a more precise representation of the real phenomenon. The main objective of the article was to formulate analytical solutions for the motion of a Cardan suspension gyroscope subjected to controlling torque moments. Analytical solutions for the proposed mathematical model were developed using the Laplace transform and Green's function. Subsequently, they were validated by numerical tests. The obtained analytical solutions are universally applicable, regardless of the type of controlling moments. [ABSTRACT FROM AUTHOR]

Computational Fluid Dynamics (CFD) simulations were utilized in this study to comprehensively explore the fluid dynamics within an accelerated toroidal vessel, specifically those central to Particle Imaging Velocimetry Gyroscope (PIVG) technology. To ensure the robustness of our simulations, we systematically conducted grid convergence studies and quantified uncertainties, affirming the stability, accuracy, and reliability of our computational grid and results. Comprehensive validation against experimental data further confirmed our simulations' fidelity, emphasizing the model's fidelity. As the PIVG is set up to address the primary flow through the toroidal pipe, we focused on the interaction between the primary and secondary flows to provide insights into the relevant dynamics of the fluid. In our investigation covering Dean numbers (De) from 10 to 70, we analyzed diverse aspects, including primary flow, secondary flow patterns, pressure distribution, and the interrelation between primary and secondary flows within toroidal structures, offering a comprehensive view across this range. Our research indicated stability and fully developed fluid dynamics within the toroidal pipe under accelerated angular velocity, particularly for low De. Furthermore, we identified an optimal Dean number of 11, which corresponded to ideal dimensions for the toroidal geometry with a curvature radius of 25 mm and a cross-sectional diameter of 5 mm. This study enhances our understanding of toroidal fluid dynamics and highlights the pivotal role of CFD in optimizing toroidal vessel design for advanced navigation technologies. [ABSTRACT FROM AUTHOR]

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement patterns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this context, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be implemented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fitness trackers to gesture detectors aimed at disabled individuals (e.g., for sending alarms), promoting behavioral activation or healthy lifestyle habits. In this regard, for the development of artificial intelligence algorithms capable of effectively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a collection of files containing the signals captured by a Shimmer 3 sensor node, which includes an accelerometer, a gyroscope, a magnetometer and a barometer, during the execution of different typical hand movements. For that purpose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involving hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.

Reinforcement learning is a compelling area of research within machine learning because it enables the improvement of control strategies for future manipulation of dynamic systems, leveraging previous data even without a precise model of the system. It usually makes complex, model-free predictions from data alone, which is actually consistent with the purpose of control in that they both aim to design systems using richly structured perceptions to execute planning and control strategies that adequately adapt to changing environments. The robust trajectory tracking control of intricate mechanical systems presents a challenging problem that necessitates effective control methods. In this paper, we propose a novel nonlinear control strategy based on deep reinforcement learning to solve the trajectory tracking problem of a 3-degree-of-freedom (3-DOF) control moment gyroscope (CMG). First, dynamic modeling of the 3-DOF CMG is used as a policy solver for the reinforcement learning training environment, and transfer learning is employed to bridge the reality gap. Then, the hyperparameters and reward functions of the neural network are optimized using the asynchronous successive halving algorithm. Ultimately, the twin delay depth determination policy gradient algorithm is trained in simulation to yield an agent capable of tracking user-defined trajectory routes as a nonlinear controller for the system. Both simulation and experimental results show that the proposed method works well for both high-frequency and low-frequency varying trajectory tracking control, and that the proposed method has better response speed and robustness than classic linear parameter-varying control methods and the state-of-the-art nonlinear parameter-varying method and the neural network-based feedback linearization adaptive control method. [ABSTRACT FROM AUTHOR]

This paper deals with the action of the fractional order derivative on nonlinear resonances and its analysis on the chaotic dynamics of an excited rotating gyroscope has been investigated. After the mathematical modeling of the system, we determined the possible resonances of the system using the method of multiple scales. The stability conditions for each resonance were obtained using the Routh-Hurwitz criterion. The different system parameters have been studied and it is concluded that the different resonance states can be controlled by each of the system parameters. Moreover, using bifurcation diagrams, Lyapunov exponents, phase portraits and time series, it was shown that for certain values of the parameters the considered gyroscope exhibits several rich dynamics. The chaos has been reduced, even eliminated with the progressive increase of the other parameters, in particular of the order μ of the fractional derivative up to a certain value. [ABSTRACT FROM AUTHOR]

The article describes the on-board control-measurement system designed to record the main flight parameters of a micro-convertiplane type unmanned aerial vehicle and the element base used for this purpose. The methodology of conducting tests in stationary mode through the on-board control-measurement system was developed and tests were carried out. The engine's diagnostic check and condition assessment has been performed using the on-board control-measuring system. The possibility of on-ground check of the micro unmanned aerial vehicles power elements before and after the flight, as well as the possibility of monitoring the flight events that may occur in the air during the flight has been shown. The data on the power consumption and temperature of each engine in the stationary mode, the direction of the aircraft axes and the velocity vector have been recorded by mean of the developed on-board control-measurement system, as well as time dependence diagrams of the corresponding parameters have been built in comparative order based on the obtained values. A methodology has been developed for the diagnostics of the engine operation of the micro-unmanned aerial vehicle before and during the flight. It has been shown that the temperature, power consumption and number of revolutions of the engines included in the power system of the aircraft, as well as the aircraft axes and velocity vector direction data is recorded during the flight, by mean of the on-board control-measurement system designed to collect data on the flight-technical parameters of the convertiplane-type micro-unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

This paper reports a structural configuration of a capacitive gyroscope having 1-DOF drive and anchored 2-DOF sense modes that allows a wide sense bandwidth and high gain without much scaling down the mass on which the sensing comb fingers are attached. The use of the additional anchoring beam in the sense direction also causes the enhancement in the coupling strength of sense mode resonance frequencies. This device has been mathematically modeled considering decoupled frame anchoring effect, designed and then fabricated by using the economical UV-LIGA technology with nickel as a key structural layer of 9-μm thickness with unequally spaced sense comb fingers with 4 µm and 12 µm capacitive gap, respectively. The overall miniature device size is 3 mm × 3 mm. Vibration characterization of the fabricated devices shows sense mode resonances at 4.96 kHz and 5.58 kHz and drive resonance at 5.48 kHz. [ABSTRACT FROM AUTHOR]

Road abnormalities can be caused by man-made and natural disasters that affect the safety of drivers and damage vehicles. Therefore, several automatic road monitoring approaches have been proposed to monitor the road surface and detect road abnormalities like potholes. However, low accuracy in detecting the pothole in low-light conditions is taken as the main problem in this work. To address this issue, we presented an Improved Long Short Term Memory model (ILSTM) that combines a three-layer deep LSTM with a One-Dimensional Local Binary Pattern (1D-LBP) layer to detect the presence of potholes during low-light conditions and extract features such as the number of neighbouring samples and pixel values. The proposed strategy collects the pothole data from accelerometer and gyroscope sensor data using a smart phone. This model is used to classify the sensor data and label it either as a pothole or normal. Besides, this makes classification possible and extracts the location of the pothole. Evaluation results demonstrate that the proposed ILSTM approach is also robust to low lighting conditions with a detection accuracy of 99% and requires less execution time in classifying potholes and non-pothole regions on the pothole dataset collected with the help of an accelerometer and a gyroscope. [ABSTRACT FROM AUTHOR]

Gyroscopes are devices used for orientation determination however mechanical gyroscopes are used for orientating and balancing via gyroscopic torque. This torque is generated by the precession speed given to the rotating objects from one of the axes other than the rotation axes. In this study, the gyroscopic torque was calculated using dynamic analysis onto a single flywheel mechanical gyroscope design created. Free vibration frequencies were determined by modal analysis. Dynamic responses for the movement of the gyroscope with a flywheel, which is mounted vertically and balances the weight torque, are investigated on the basis of classical (Newtonian) mechanics. When the flywheel rotates in the range of 0-250 rad/s, it does not correspond to any natural frequency. Torque of 0.468 Nm is offset by a precession velocity of 0.922 rad/s. Nutation oscillations disappears when torsional damping is given to the joint where the gyroscope is attached to the ground. The free vibration frequencies change in the damped state. [ABSTRACT FROM AUTHOR]

Due to the gyroscopic torque production ability, mechanical gyroscopes are frequently used for balancing fully suspended or single/two-wheeled land vehicles such as airplanes and spacecraft. They produce gyroscopic torque thanks to the flywheel rotating at high speed. Precession is required to control this torque. In the study, 415 precession data were collected by performing a rigid dynamic analysis of a mechanical gyroscope. A non-linear autoregressive artificial neural network (NAR) is used to estimate this velocity. In the model obtained, the correlation value was 0.998 and the root mean square of error (RMSE) value was 0.016 rad/s. A high linear relationship was detected between the model output and the data set. The NAR network has eliminated the need for any pre-processing on the data. The network parameters used and the estimation performances obtained with this model are presented in the study. [ABSTRACT FROM AUTHOR]

(1) Background: Vibrotactile stimulation has been studied for tremor, but there is little evidence for Essential Tremor (ET). (2) Methods: This research employed a dataset from a previous study, with data collected from 18 individuals subjected to four vibratory stimuli. To characterise tremor changes before, during, and after stimuli, time and frequency domain features were estimated from the signals. Correlation and regression analyses verified the relationship between features and clinical tremor scores. (3) Results: Individuals responded differently to vibrotactile stimulation. The 250 Hz stimulus was the only one that reduced tremor amplitude after stimulation. Compared to the baseline, the 250 Hz and random frequency stimulation reduced tremor peak power. The clinical scores and amplitude-based features were highly correlated, yielding accurate regression models (mean squared error of 0.09). (4) Conclusions: The stimulation frequency of 250 Hz has the greatest potential to reduce tremors in ET. The accurate regression model and high correlation between estimated features and clinical scales suggest that prediction models can automatically evaluate and control stimulus-induced tremor. A limitation of this research is the relatively reduced sample size. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app's setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage. [ABSTRACT FROM AUTHOR]

In this paper, using a MOEMS cantilever, an opto-plasmonic gyroscope is introduced. A metallic grating, as the substrate for surface plasmon polaritons, is employed on the cantilever. The mechanical structure converts input angular velocity into a change of the light incidence angle, then reflected optical power is measured by a photo-detector. Subsequently, the output current of the sensor is modulated according to the input angular velocity. This robust system has a simple and effective structure, and the simulation results propose characteristics including optical sensitivity of 0.13 nW / (∘ / s) , total sensitivity of 0.05 nA / (∘ / s) , ultra-wide measurement range of ± 567228217.18 ∘ / s , and resolution of 0.77 ∘ / s . Moreover, the operating wavelength is λ = 630 nm with a minimum required laser power of 73 pW, and the designed structure has dimensions of 15 × 30 × 6 μ m 3 . Furthermore, a comprehensive study on the effects of various mechanical and optical parameters on the performance of the gyroscope is presented, and a comparative study is done. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms' performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period. [ABSTRACT FROM AUTHOR]

This paper proposes an AI-based sensory glove system aimed at recognizing Bengali sign language (BaSL) in order to assist the Bengali speech disabled community to overcome the communication barrier. In the proposed design, several sensors such as flex, accelerometer, and gyroscopes were embedded in a hand glove worn by a speech-disabled person to capture the signals generated from the gestures. Two different architectures were proposed to identify the corresponding Bengali word from Bengali sign, one based solely on a convolutional neural network (CNN), and the other - a combination of CNN and long short-term memory (LSTM) network. From the experiment results of the prototype on sign samples related to 41 different Bengali words, it was observed that the average recognition accuracy of the prototype incorporated with CNN and LSTM based architecture was 94.73%, while it is 90.34% for the prototype with CNN based architecture. Experiment results also demonstrated user independent features of the sensory glove system. Moreover, analysis of the performance of the AI-based sensory glove system in terms of latency, user comfort, and battery backup revealed its competitive features compared to other commercially available devices.

This study aims to investigate the alternative model structure based on a feature selection algorithm on multiple features-framework of human activity recognition (HAR) via wearable sensor-based modality. Neighborhood component analysis (NCA) is a linear transformation that maximizes the accuracy of specified classification events used as the benchmark for the proposed algorithm. Also, the effect of different combinations of sensor configurations of two, three, and all four sensors on the performance of the developed model was studied. The effectiveness and shortcomings of best sensor configuration were highlighted. Results were compared between different sensor configurations and benchmark HAR dataset. To maximize the regularization of NCA, fine-tuning the algorithm to maximize relevance and minimize redundancy (MRMR) was proposed. Results demonstrated that RNCA-MRMR could establish an efficient algorithm that can satisfy the model validation tests with significant advantages over feature number and predictive accuracy at 93.5%, 93.7%, and 94.5% for two, three, and all four sensors respectively. Furthermore, the adaptability of RNCA-MRMR to different data characteristics has ensured an optimal and task-specific representation of the data. In essence, the combined strength of RNCA and MRMR provides a versatile and effective approach for extracting meaningful features and enhancing the overall performance of machine learning models.

The spraying construction robot is unable to include information about ground leveling in the map when it is created, and when the robot operates according to the built map, the spraying clamping fixture at the working end of the spraying construction robot can not be parallel to the wall due to the lack of ground information. In order to compensate the posture error of the spraying fixture relative to the wall, a multi-sensor fusion method is proposed based on the unscented Kalman filter to compensate the posture of the spraying fixture.The state equation of the fixture posture is constructed from the data measured by the displacement measurement sensor， the equation of fixture posture measurement is constructed from the data measured by the gyroscope, and the optimal estimation of the fixture posture is obtained by using the unscented Kalman filter algorithm and transferring them to the robot, so as to achieve the purpose of compensating the posture error of the spraying fixture. Finally, the experimental platform is built to verify the feasibility of the error compensation system. The experimental results show that the positional error between the spraying fixture and the wall after error compensation is reduced to 0.005°.

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). This study explores active learning integrated with machine learning, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the HAR lacks promising performance, which could make it difficult to make highly accurate recognition. This paper proposes a novel approach to predict human activity from the Heterogeneity Human Activity Recognition (HHAR) dataset that integrates active learning with machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope of smartwatches. The experiments are evaluated on 3 iterations where the results demonstrated that the proposed approach predicts human activities with the highest F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional approaches and baseline.

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.

The study presents a new approach for assessing plantarflexor muscles’ function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles’ function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total −0.1118, 0.1127 s, T-lower −0.1152, 0.1179 s, and T-total −0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles’ functionality in those unable to perform tests in standing positions.

In response to the current low work efficiency of soil ridge-working machinery, as well as its poor stability, passability, and adaptability, this paper designs an independent leg-type working platform that can autonomously adjust its vehicle attitude through LiDAR scanning in a soil ridge-working environment. The platform, in terms of its mechanism and structural design, adopts dual parallelogram mechanisms, dual lead screw mechanisms, and independent column leg mechanisms, with a maximum adjustable ground clearance of 107 mm and a maximum wheelbase adjustment of 150 mm. A gyroscope is mounted at the center of the platform for attitude adjustment, ensuring the accurate data collection of the ultrasonic ranging module. Moreover, the platform adopts an adaptive adjustment method based on vehicle attitude and soil ridge shape parameters, obtaining soil ridge parameters through LiDAR and combining ultrasonic ranging module data with stepper motor pulse signals to obtain the absolute vehicle attitude parameters, using first and second linear regression methods to adjust the vehicle attitude and other working parameters. A prototype was also created, and the test data from the soil obtained through experiments show that, after leveling with the gyroscope leveling algorithm, the average value of the pitch angle is up to 0.6154°, and the average value of the roll angle is up to 0.9989°, with the maximum variance of the pitch angle being 0.0474° and the maximum variance of the tilt angle being 0.1320°. After the ultrasonic ranging module data are filtered by the Kalman filter, the maximum variance is 0.0304, and after applying the final fusion algorithm, the maximum variance is only 0.0085. The LiDAR measurement width value deviates from the actual width value by no more than 1.0 cm, and the LiDAR measurement height value deviates from the actual height value by no more than 1.0 cm. The platform’s actual adjusted width deviates from the actual soil ridge width by no more than 2.0 cm, and the platform’s actual adjusted height deviates from the actual soil ridge height by no more than 1.2 cm. This platform can improve the passability, adaptability, and stability of agricultural machinery in soil ridge work and provide technical references for subsequent related research.