Acceleration Signals Research Articles

Choosing Optimal Cutoff Frequencies for Filtering Linear Acceleration and Angular Velocity Signals Associated with Head Impacts Measured by Instrumented Mouthguards

Head impact sensors worn in the mouth are popular because they couple directly to the teeth and provide six-degree-of-freedom head measurements. Mouthpiece signal filters have conventionally used cutoff frequencies lower than recommended practices (Society of Automotive Engineers, SAE J211-1) to eliminate extraneous noise when measuring with live subjects. However, there is little information about the effects of filter choice on the accuracy of signals measured by instrumented mouthpieces. Lack of standardization in head impact measurement device post-processing techniques can result in data that are not comparable across studies or device brands. This study sought optimal filter cutoff frequencies for six-degree-of-freedom measurements made at the teeth using instrumented mouthguards. We collected linear acceleration and angular velocity signals at the head center of gravity (CG) using laboratory-grade instrumentation. We also collected and filtered similar six-degree-of-freedom measurements from an instrumented mouthguard using 24 cutoff frequencies, from 25 to 600 Hz. We transformed the measurements to linear acceleration at the center of gravity of the head (CG) using all kinematic variables at the teeth, optimizing linear and angular mouthguard cutoff frequencies with one equation. We calculated the percent error in transformed peak resultant linear acceleration and minimized the mean and standard deviation in error. The optimal cutoff frequencies were 175 Hz for linear acceleration and 250 Hz for angular velocity. Rigid impacts (3–5 ms duration) had higher optimal cutoff frequencies (175 Hz linear acceleration, 275 Hz angular velocity) than padded impacts (10–12 ms duration; 100 Hz linear acceleration, 175 Hz angular velocity), and all impacts together (3–12 ms duration; 175 Hz linear acceleration, 250 Hz angular velocity). Instrumented mouthpiece manufacturers and researchers using these devices should consider these optimal filter cutoff frequencies to minimize measurement error. Sport-specific filter criteria for teeth-based sensors may be warranted to account for the difference in optimal cutoff frequency combination by impact duration.

Research on basketball footwork recognition based on a convolutional neural network algorithm

ObjectiveThe purpose of this paper is to utilize a convolutional neural network (CNN) to identify the types of basketball footwork of athletes as a way to assist in the training of basketball players' footwork and to improve their performance in the game. MethodsA traditional CNN algorithm was improved to a dual-model CNN (DMCNN) algorithm, where convolutional feature extraction was performed separately on both the acceleration and angular velocity data of footwork. The two features were then merged and subjected to principle component analysis (PCA) dimensionality reduction for identifying different types of footwork. In subsequent simulation experiments, ten basketball players' footwork data were collected using sensors. The improved CNN algorithm was used for footwork recognition and compared with the support vector machine (SVM) and traditional CNN algorithms. ResultsThe experimental results showed that the acceleration and angular velocity signals of different basketball footwork had distinct differences. The comprehensive recognition precision of DMCNN for footwork types was 98.8 %, and the comprehensive recall rate and overall F value were 97.8 % and 98.2 %, respectively. Its recognition time was 1.23 s. For the traditional CNN algorithm, the comprehensive precision was 87.5 %, the comprehensive recall rate was 85.7 %, and the overall F value was 86.6 %. Its recognition time was 1.99 s. As for the SVM algorithm, the comprehensive precision was 74.2 %, the comprehensive recall rate was 73.2 %, and the overall F value was 73.7 %. The recognition time was 3.68 s. NoveltyThe novelty of this article lies in using two separate CNNs to extract convolutional features from acceleration and angular velocity, respectively. These features are then combined and reduced dimensionality using PCA, thereby improving both recognition accuracy and efficiency.

Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.

Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13-0.70Hz) and detect respiratory events as transient (10-90s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea-hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA.

The Effects of Age and Height on Gait Smoothness in Adolescent Athletes.

(1) Background: Despite evidence of increased rates of sports injury during the years surrounding peak growth in adolescents, little is known regarding the relationship between adolescent growth and gait stability. The aim of this study was to gain a better understanding of how chronological age and height relate to gait stability in both male and female adolescents. (2) Methods: Participants (N = 67; females: n = 34, ages 8.7-15.9 years; males: n = 33, ages 10.0-16.7 years) completed two trials of treadmill walking at varying speeds: the preferred walking speed and 30% above and below. Trials were separated by a bout of fatiguing exercises. HarmonicRatios of the trunk, calculated from acceleration signals taken during walking, were used to quantify gait stability. Data were separated by sex and relationships between height and chronological age, and HarmonicRatios were assessed using multiple linear regression. (3) Results: Females' HarmonicRatios improved with chronological age both before and after fatigue. Males' HarmonicRatios increased with chronological age before fatigue; however, this effect was eliminated post-fatigue. Females' height was negatively associated with HarmonicRatios post-fatigue. Males' height was positively associated with HarmonicRatios pre-fatigue. (4) Conclusions: The study findings suggest sex differences in the effects of fatigue on gait stability during adolescence. In both sexes, HarmonicRatios increased with chronological age. These improvements were eliminated for males and altered for females with fatigue. The results of this study indicate the need for the reevaluation of sports progression based on chronological age in adolescents.

Research on identification method of transformer winding material based on vibration characteristic

At present, there are shoddy transformers in the market, of which the windings are replaced by aluminum and copper clad aluminum. The commissioning of these transformers may cause poor power supply performance and excessive winding heating. The existing detection methods of windings are complex, time-consuming to operate and destructive. Therefore, a winding material identification method based on vibration characteristics is proposed. Firstly, the vibration accelerations of transformer cores and windings with different winding materials are theoretically derived. Furthermore, through the coupling simulation of magnetic field and structure field of distribution transformers, the calculated vibration characteristics of copper, aluminum, and copper clad aluminum are verified. Finally, by comparing the time domain, the frequency domain and time-frequency domain of acceleration signals, preliminary identification of winding materials is conducted, which lays a theoretical foundation for establishing a precise identification model for winding materials in the future. This work provides guarantee for the safe operation of the distribution network.

Displacement of mining vibrating screen obtained from acceleration based on improved S–G filter

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky–Golay (S–G) filter is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the window length of the S–G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

Experimental study on damping characteristics of the particle dampers with damping rod and soft inside wall

In the perpendicular simple sinusoidal excitation, the damping characteristics of the soft-wall particle damper with a damping rod were experimentally investigated by controlling the acceleration amplitude and vibration frequency of the damper motion and measuring the force and acceleration signals. The experimental results show that the loss power of the damper increases monotonically with the rise of the excitation acceleration amplitude, and the effective mass shows the change of “gentle decrease-critical point-decrease”. As the driving frequency rises, the loss power progressively decreases, and the effective mass does not change significantly. By analyzing the damping characteristics of different dampers, it shows that the damping effectiveness of the soft inside wall particle damper with a damping rod is obviously better.

A vibration-based Machine Learning type Structural Health Monitoring methodology for populations of composite aerostructures under uncertainty

A robust to uncertainty Machine Learning (ML) based Structural Health Monitoring methodology for populations of composite aerostructures is postulated. The methodology is founded upon a number of unsupervised ML algorithms for damage detection and a supervised counterpart for damage characterization. Damage detection is specifically based on two types of Healthy Subspace representations: A Multiple Model (MM) and a varying radii Hyper-Sphere (HS) type. Both are built upon response-only vibration acceleration and/or strain signals at properly selected sensor locations. Based on them, Multiple Input Single Output (MISO) Transmittance Function AutoRegressive with eXogenous (TF-ARX) excitation data driven models representing the partial structural dynamics are obtained. Decision making is then based on the model parameter vector that may be transformed and reduced via Principal Component Analysis (PCA). Damage detection is achieved via multi-level information fusion using acceleration and/or strain sensors. Damage characterization, referring to damage type, location, and level determination, is achieved via a hierarchical cosine similarity based algorithm. The methodology is successfully assessed via hundreds of experiments using a population of small-scale composite coupons for the detection and characterization of Delamination and Impact damage under material/manufacturing, temperature, excitation, and experimental uncertainty.

General spectral characteristics of human activity and its inherent scale-free fluctuations

The scale-free nature of daily human activity has been observed in different aspects; however, the description of its spectral characteristics is incomplete. General findings are complicated by the fact that—although actigraphy is commonly used in many research areas—the activity calculation methods are not standardized; therefore, activity signals can be different. The presence of 1/f noise in activity or acceleration signals was mostly analysed for short time windows, and the complete spectral characteristic has only been examined in the case of certain types of them. To explore the general spectral nature of human activity in greater detail, we have performed Power Spectral Density (PSD) based examination and Detrended Fluctuation Analysis (DFA) on several-day-long, triaxial actigraphic acceleration signals of 42 healthy, free-living individuals. We generated different types of activity signals from these, using different acceleration preprocessing techniques and activity metrics. We revealed that the spectra of different types of activity signals generally follow a universal characteristic including 1/f noise over frequencies above the circadian rhythmicity. Moreover, we discovered that the PSD of the raw acceleration signal has the same characteristic. Our findings prove that the spectral scale-free nature is generally inherent to the motor activity of healthy, free-living humans, and is not limited to any particular activity calculation method.

The parameter identification of metro rail corrugation based on effective signal extraction and inertial reference method

Rail corrugation is a severe track damage and failure problem in metro lines. So, the accurate assessment of rail corrugation is significant for the safety and comfort of vehicles. It is an efficient means to detect rail corrugation through the acceleration signal. However, the acceleration signal contains the mixed signal caused by vehicle component noise and wheel polygonal wear, resulting in poor rail corrugation detection accuracy. Therefore, a new method is presented to extract the effective signal components and accurately identify the rail corrugation. Firstly, the Cubic mapping, modified sine algorithm, and Lévy flight are introduced into the dung beetle optimizer algorithm. A new improved dung beetle optimizer (IDBO) algorithm is presented to optimize the hyperparameters of the VMD algorithm, which can increase the signal decomposition performance of the VMD. Secondly, the fast independent component analysis method separates the independent components (ICs) from the VMD modes and original acceleration signal. Based on the selection criteria of ICs, the dominant signal components related to rail corrugation are determined. The rail corrugation amplitude can be obtained through the inertial reference method. The experimental results demonstrate that the presented method has better rail corrugation recognition accuracy, the root mean square error is smaller than the comparison method, and the corrugation wavelength of the straight track is larger than the curve track.

A quantitative detection method for wheel polygonization of heavy-haul locomotives based on a hybrid deep learning model

Wheel polygonization poses severe threat to the operational safety of railway vehicles. To improve running safety, condition monitoring of wheel polygonization based on axle box acceleration (ABA) signals is an effective method. However, the signals usually contain strong noise, making it difficult to diagnose polygonal wheels quantitatively. To address these issues, this paper presents a quantitative detection method for wheel polygonization based on a deep learning (DL) model that integrates Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM). The CNN is responsible for automatically extracting the sensitive spatial features from the original input and it can retain effective signal components. The LSTM is introduced to fuse the local features and conduct sequence modeling for polygonal wear amplitude estimation. Both dynamics simulations and field tests are carried out to verify the feasibility of the proposed method. The detection results indicate that the proposed model can accurately estimate the wear amplitudes.

Possibility of Monitoring the Dynamic Impact of the Rolling Stock on the Track Superstructure Using Strain Gauges and Vibrometry

One of the priority tasks in development of railway transport is to raise its efficiency by increasing the time between repairs for both the track superstructure and the rolling stock. Solving this problem is impossible without timely, complete, and reliable information about the dynamics of interaction of the rolling stock with the infrastructure. To organise monitoring of dynamic processes, completely different types of primary transducers are used: resistance strain gauges, fibre-optic sensors, dynamometers (force sensor pads), accelerometers, acoustic emission sensors. This implies relevance of the scientific and technical problem of comparative testing of sensors of different types to assess the information content of their signals and justify the criteria for choosing primary transducers when solving specific monitoring problems.The objective of the study is to comparatively test removable resistive strain sensors, optical polarisation strain sensors and accelerometers under a passing train and to evaluate their information content to control rail depression and detect defects on the running surface of wagon wheels.The study using finite element modelling and a physical model of a rail as of a beam on an elastic foundation, substantiates the relationship between longitudinal deformations and vertical accelerations of the rail foot. Comparative tests of removable strain-resistive and optical polarisation strain sensors and accelerometers were held on an experimental section of track under a passing train. A signal processing algorithm has been developed and the equivalence of strain gauges and vibrometers for determining the depression of a rail under a passing train has been proven. Acomparison has been made of the pulse components of the signals of deformation and vibration acceleration during movement of a wheel with a defect on the running surface, and the requirements for the frequency characteristics of the sensors and their mounting on the rail surface have been substantiated.

Deep encoder–decoder network based data-driven method for impact feedback rendering on head during earthquake

In safety training simulators, realistic haptic feedback is essential to make people construct accurate situation awareness through experiencing. In this regard, this paper presents a new and innovative system that provides the haptic experience of falling objects on user’s head during an earthquake. Special focus was on the accurate reproduction of impact feedback when various objects fall on the head. To this end, we propose a novel data-driven approach. This approach first collects 3-axis acceleration signals during real collision under several impact velocities. Afterward, 3D acceleration data is abstracted to a 1D acceleration profile using our novel max–min extraction approach. The impact signal for an arbitrary velocity is interpolated using a deep convolutional bidirectional long short-term memory encoder–decoder model. Rendering hardware is also implemented using high performance voice-coil vibrotactile actuator. Numerical and subjective evaluations are carried out to evaluate the performance of the proposed approach.Kindly check and confirm the edit made in the title.I confirm the edit is okay.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Authors Given name: [Joolekha Bibi] Last name: [Joolee], Given name: [Mohammad Shadman] Last name: [Hashem]. Also, kindly confirm the details in the metadata are correct.Yes, the author names are presented accurately and in the correct sequence.

Vibration-Based SHM in the Synthetic Mooring Lines of the Semisubmersible OO-Star Wind Floater under Varying Environmental and Operational Conditions.

As the industry transitions toward Floating Offshore Wind Turbines (FOWT) in greater depths, conventional chain mooring lines become impractical, prompting the adoption of synthetic fiber ropes. Despite their advantages, these mooring lines present challenges in inspection due to their exterior jacket, which prevents visual assessment. The current study focuses on vibration-based Structural Health Monitoring (SHM) in FOWT synthetic mooring lines under uncertainty arising from varying Environmental and Operational Conditions (EOCs). Six damage detection methods are assessed, utilizing either multiple models or a single functional model. The methods are based on Vector Autoregressive (VAR) or Transmittance Function Autoregressive with exogenous input (TF-ARX) models. All methods are evaluated through a Monte Carlo study involving 1100 simulations, utilizing acceleration signals generated from a finite element model of the OO-Star Wind Floater Semi 10 MW wind turbine. With signals from only two measuring positions, the methods demonstrate excellent results, detecting the stiffness reduction of a mooring line at levels 10% through 50%. The methods are also tested for healthy cases, with those utilizing TF-ARX models achieving zero false alarms, even for EOCs not encountered in the training data.

Numerical and experimental research on vibration signal characteristics of water pipeline leakage

In the leak detection of water pipelines, interference and environmental noise directly affect the effectiveness of leak detection and the accuracy of leak point location, so it is necessary to study the real signal. Previous studies mostly rely on a single test to improve the positioning accuracy by collecting vibration signals for denoising, but denoising will also affect the real signal, and the actual detection situation is complex, so it is difficult to deal with all working conditions only through the test. With the rapid development of computer technology, the combination of numerical simulation and experiment is expected to solve this problem. Therefore, this paper takes the acceleration signal as the research object, collects and simulates the leakage signals under different pressure strengths by combining experiments with numerical simulation, and verifies the feasibility and accuracy of a numerical simulation model. The generation of leakage vibration acceleration under different pressure and different leakage hole radius is simulated. Research findings: a. the leakage signal energy has a power function relationship with the pressure and an exponential function relationship with the leakage radius; b. the characteristic frequency is the variable of the characteristics of pipeline and leakage hole, which is independent of the pressure; c. the power of each peak frequency has a step-by-step change trend with the energy of the signal; d. the power of acceleration signal concentrates to a higher frequency with the increase of pressure and the decrease of leakage radius. This study provides a basis for identifying, detecting, and extracting leakage signals in a real noise environment, helps to improve the accuracy of acoustic emission signal detection, and can provide a theoretical basis for pipeline leakage detection research.

Pantograph–Catenary Interaction Prediction Model Based on SCSA-RBF Network

As a traditional numerical simulation method for pantograph–catenary interaction research, the pantograph–catenary finite element model cannot be applied to the real-time monitoring of pantograph–catenary contact force, and the computational cost required for the multi-parameter joint optimization of the pantograph–catenary system with the finite element model is very high. In this paper, based on the selective crow search algorithm–radial basis function (SCSA-RBF) network, the time-domain signal of the panhead acceleration, which can be obtained in real-time through non-contact test technology, is taken as the boundary condition to directly solve the pantograph dynamic equation and a data-physics coupling model that can quickly predict the pantograph–catenary interaction is proposed. The prediction model is trained and verified using the dataset generated through the finite element model. Furthermore, the prediction model is applied to the multi-parameter joint optimization of six pantograph dynamic parameters and nine pantograph dynamic parameters, considering nonlinear panhead stiffness, and optimization suggestions under various speeds and filtering frequencies are given.

ACCELERATIONS RECORDED BY LOW-FREQUENCY WEARABLE SENSORS AS EFFECTIVE DISCRIMINATORS OF KNEE AND HIP OSTEOARTHRITIS

Wearable inertial sensors can detect abnormal gait associated with knee or hip osteoarthritis (OA). However, few studies have compared sensor-derived gait parameters between patients with hip and knee OA or evaluated the efficacy of sensors suitable for remote monitoring in distinguishing between the two. Hence, our study seeks to examine the differences in accelerations captured by low-frequency wearable sensors in patients with knee and hip OA and classify their gait patterns.We included patients with unilateral hip and knee OA. Gait analysis was conducted using an accelerometer ipsilateral with the affected joint on the lateral distal thighs. Statistical parametric mapping (SPM) was used to compare acceleration signals. The k-Nearest Neighbor (k-NN) algorithm was trained on 80% of the signals' Fourier coefficients and validated on the remaining 20% using 10-fold cross-validation to classify the gait patterns into hip and knee OA.We included 42 hip OA patients (19 females, age 70 [63–78], BMI of 28.3 [24.8–30.9]) and 59 knee OA patients (31 females, age 68 [62–74], BMI of 29.7 [26.3–32.6]). The SPM results indicated that one cluster (12–20%) along the vertical axis had accelerations exceeding the critical threshold of 2.956 (p=0.024). For the anteroposterior axis, three clusters were observed exceeding the threshold of 3.031 at 5–19% (p = 0.0001), 39–54% (p=0.00005), and 88–96% (p = 0.01). Regarding the mediolateral axis, four clusters were identified exceeding the threshold of 2.875 at 0–9% (p = 0.02), 14–20% (p=0.04), 28–68% (p < 0.00001), and 84–100% (p = 0.004). The k-NN model achieved an AUC of 0.79, an accuracy of 80%, and a precision of 85%.In conclusion, the Fourier coefficients of the signals recorded by wearable sensors can effectively discriminate the gait patterns of knee and hip OA. In addition, the most remarkable differences in the time domain were observed along the mediolateral axis.

Research on the Innovation of Teaching Mode of Orienteering Sports Courses in Colleges and Universities Based on Mobile Intelligent Terminals

Abstract This paper studied motion state recognition algorithms using acceleration signals (ACC). At the same time, research is also being conducted on ECG motion artifact suppression algorithms and respiration rate detection algorithms. Based on this, we designed a mobile intelligent terminal sports app that allows users to view physiological signals and sports status in real time at any time. In college and university orienteering classes, the mobile intelligent terminal movement app is used to teach the sport. The results of comparing the experimental group and the control group in the orienteering course show that the mobile intelligent terminal orienteering teaching makes a significant difference in the men’s physical quality, except for the seated forward body flexion (P<0.05). There were significant differences in the women’s group in terms of 50 m running (P = 0.013) and 800 m (P = 0.042). The experiment improved the dimensions of the Orienteering Campus Short Distance Test, Learning Motivation Indicator, and Emotional Management.

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion.

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.

Vibration Control of Structure by State Switching of a Floater using Acceleration Signals

Vibration Control of Structure by State Switching of a Floater using Acceleration Signals