Search Results (46)

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task. Full article

Stability during running has been recognized as a crucial factor contributing to running performance. This study aimed to investigate the effects of wearable equipment containing external loads on different body parts on running stability. Fifteen recreational male runners (20.27 ± 1.23 years, age range 19–22 years) participated in five treadmill running conditions, including running without loads and running with loads equivalent to 10% of individual body weight placed on four different body positions: forearms, lower legs, trunk, and a combination of all three (forearms, lower legs, and trunk). A tri-axial accelerometer-based smartphone sensor was attached to the participants’ lumbar spine (L5) to record body accelerations. The largest Lyapunov exponent (LyE) was applied to individual acceleration data as a measure of local dynamic stability, where higher LyE values suggest lower stability. The effects of load distribution appear in the mediolateral (ML) direction. Specifically, running with loads on the lower legs resulted in a lower LyE_ML value compared to running without loads (p = 0.001) and running with loads on the forearms (p < 0.001), trunk (p = 0.001), and combined segments (p = 0.005). These findings suggest that running with loads on the lower legs enhances side-to-side local dynamic stability, providing valuable insights for training. Full article

Slip and fall accidents are the leading cause of injuries for all ages, and for fatal injuries in adults over 65 years. Various factors, such as floor surface conditions and contaminants, shoe tread patterns, and gait behavior, affect the slip risk. Moreover, the friction between the shoe outsoles and the floor continuously changes as their surfaces undergo normal wear over time. However, continuous assessment of slip resistance is very challenging with conventional measurement techniques. This study addresses this challenge by introducing a novel approach that combines sensor fusion technology and machine learning techniques to create intelligent insoles designed for fall risk prediction. In addition, a state-of-the-art slip simulator, capable of mimicking the foot’s motion during a slip, was developed and utilized for the assessment of slipperiness between various shoes and floor surfaces. Data acquisition involved the collection of pressure data and three-axial accelerations using instrumented shoe insoles, complemented by friction coefficient measurements via the slip simulator. The collected dataset includes four types of shoes, three floor surfaces, and four surface conditions, including dry, wet, soapy, and oily. After preprocessing of the collected dataset, the simulator was used to train five different machine learning algorithms for slip risk classification. The trained algorithms provided promising results for slip risk prediction for different conditions, offering the potential to be employed in real-time slip risk prediction and safety enhancement. Full article

Anti-resonant hollow core fibers (AR-HCFs) provide a promising solution for photothermal spectroscopy and photoacoustic imaging applications. Here, the AR-HCF serves as a micro platform to induce the photothermal/photoacoustic effect. Since the Bragg structure can induce multiple AR effects compared with the general AR-HCF, we proposed a novel device, the AR-BHCF (AR-HCF with Bragg cladding), to enhance the excitation efficiency. The simulation and experimental results validate that the AR-BHCF dominates in having a stronger ability to confine the optical field in the air core indeed. Then, the acoustic signal stimulated by the photoacoustic effect will propagate along with the fiber axial, and part of it will penetrate out of the AR-BHCF. The results revealed that the transmission bandwidth of the acoustic wave in the AR-BHCF ranges from 1 Hz to 1 MHz, covering infrasound to ultrasound. In particular, a constant coefficient of 0.5 exists in the acoustic wave fading process, related to the propagation frequency and time. The acoustic signal can be monitored in real time, assisted by the ultra-highly sensitive sensor head. Therefore, BHCF-based devices combined with photoacoustic techniques may accelerate their sensing applications. Meanwhile, this scheme shines a light on the theoretical foundation of novel short-haul distributed acoustic sensing. Full article

Long-term corrosion, construction irregularities, road pressure and other reasons lead to various defects in urban sewer pipelines. Closed-circuit television (CCTV) and quick view (QV) are currently the most commonly used techniques to detect the internal state of the pipeline, but CCTV requires a large amount of capital investment and manpower costs, while QV is faced with the use of limitations and inaccurate positioning. The inspection of urban sewerage networks has long been a challenge for the relevant management authorities to overcome. To this end, in this study, an device was assembled using a six-axis MEMS gyroscope sensor as the core component to inspect and locate the breakage point of the pipe. Specifically, a six-axis MEMS gyroscope sensor is used as the core component along with a small lithium battery and a remote control switch assembled in a highly waterproof round box, and dropped into a laboratory to simulate a sewage pipe that has external water infiltration. Then the device is recovered and the SD card on which the data is stored is removed, the data is loaded to perform the coordinate conversion process and restore the trajectory and attitude of the device along its travel. The three axis axial acceleration of the device before and after passing through the infiltration point is analyzed for anomalies, as well as changes in the roll and pitch angle fluctuations of the device. Multiple experiments demonstrated that the six-axis MEMS gyro sensor response is very sensitive, generating data and storing it through the DATALOG module. With the reading and analysis of the data, when the pipeline is broken by external water intrusion, the axial acceleration value, pitch angle and roll angle of the device will change abruptly after flowing through the infiltration point, based on the analysis of these indicators the preliminary judgment of the extent of external water infiltration and locate the location of the infiltration point, potential applications of MEMS gyroscopic sensors in the field of sewerage are believed to be vast. Full article

Sensor- orientation is a critical aspect in a Human Activity Recognition (HAR) system based on tri-axial signals (such as accelerations); different sensors orientations introduce important errors in the activity recognition process. This paper proposes a new preprocessing module to reduce the negative impact of sensor-orientation variability in HAR. Firstly, this module estimates a consistent reference system; then, the tri-axial signals recorded from sensors with different orientations are transformed into this consistent reference system. This new preprocessing has been evaluated to mitigate the effect of different sensor orientations on the classification accuracy in several state-of-the-art HAR systems. The experiments were carried out using a subject-wise cross-validation methodology over six different datasets, including movements and postures. This new preprocessing module provided robust HAR performance even when sudden sensor orientation changes were included during data collection in the six different datasets. As an example, for the WISDM dataset, sensors with different orientations provoked a significant reduction in the classification accuracy of the state-of-the-art system (from 91.57 ± 0.23% to 89.19 ± 0.26%). This important reduction was recovered with the proposed algorithm, increasing the accuracy to 91.46 ± 0.30%, i.e., the same result obtained when all sensors had the same orientation. Full article

Wearable sensors are an emerging technology, with growing evidence supporting their application in sport performance enhancement. This study utilized data collected from a tri-axial inertial sensor on the wrist of ten recreational and eight professional basketball players while they performed free-throws, to classify their skill levels. We employed a fully connected convolutional neural network (CNN) for the classification task, using 64% of the data for training, 16% for validation, and the remaining 20% for testing the model’s performance. In the case of considering a single parameter from the inertial sensor, the most accurate individual components were upward acceleration (A_X), with an accuracy of 82% (sensitivity = 0.79; specificity = 0.84), forward acceleration (A_Z), with an accuracy of 80% (sensitivity = 0.78; specificity = 0.83), and wrist angular velocity in the sagittal plane (G_Y), with an accuracy of 77% (sensitivity = 0.73; specificity = 0.79). The highest accuracy of the classification was achieved when these CNN inputs utilized a stack-up matrix of these three axes, resulting in an accuracy of 88% (sensitivity = 0.87, specificity = 0.90). Applying the CNN to data from a single wearable sensor successfully classified basketball players as recreational or professional with an accuracy of up to 88%. This study represents a step towards the development of a biofeedback device to improve free-throw shooting technique. Full article

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently. Full article

The preferential use of one leg over another in performing lower-limb motor tasks (i.e., leg dominance) is considered to be one of the internal risk factors for sports-related lower-limb injuries. The current study aimed to investigate the effects of leg dominance on postural control during unipedal balancing on three different support surfaces with increasing levels of instability: a firm surface, a foam pad, and a multiaxial balance board. In addition, the interaction effect between leg dominance and surface stability was also tested. To this end, a tri-axial accelerometer-based smartphone sensor was placed over the lumbar spine (L5) of 22 young adults (21.5 ± 0.6 years) to record postural accelerations. Sample entropy (SampEn) was applied to acceleration data as a measure of postural sway regularity (i.e., postural control complexity). The results show that leg dominance (p < 0.001) and interaction (p < 0.001) effects emerge in all acceleration directions. Specifically, balancing on the dominant (kicking) leg shows more irregular postural acceleration fluctuations (high SampEn), reflecting a higher postural control efficiency or automaticity than balancing on the non-dominant leg. However, the interaction effects suggest that unipedal balancing training on unstable surfaces is recommended to reduce interlimb differences in neuromuscular control for injury prevention and rehabilitation. Full article

This paper discusses research on the dynamic response characteristics of a heavy-haul railway tunnel and the surrounding soil under the conditions of substrate health and a base void. The detection results of the base condition of 20 double-track tunnels for a heavy-haul railway show the main distribution law of base voids. Based on this, a 1:20 scale test model of a heavy-haul railway tunnel is established. The vibration load of the train is established by a vibration exciter arranged at the tunnel invert. The dynamic response and attenuation law of a heavy-haul railway tunnel lining structure and the surrounding soil are tested using acceleration sensors, strain gauges, and soil pressure boxes. The research results show that most of the diseases are concentrated below the heavy-haul line. The base void causes the peak acceleration of the nearby tunnel invert to increase by 55.6%. Tunnel annular construction joints reduce the conductivity of the vibration waves in the axial direction of the tunnel. The acceleration attenuation rate of the soil above the tunnel invert is significantly less than that under the invert. The base void reduces the acceleration of the nearby soil layer by 19.4% and increases the stress on the surface of the nearby tunnel invert by 21.3%, and the stress change amplitude increases by 0.55%. The tunnel structure in the area of the base void experiences fatigue damage. The base void causes the compaction and bearing capacity of the nearby soil to decrease and the softening speed of the tunnel basement soil layer to increase. Therefore, for the basement damage to heavy-haul railway tunnels, “early detection, early treatment” should be performed. Full article

Human activity recognition (HAR) extensively uses wearable inertial sensors since this data source provides the most information for non-visual datasets’ time series. HAR research has advanced significantly in recent years due to the proliferation of wearable devices with sensors. To improve recognition performance, HAR researchers have extensively investigated other sources of biosignals, such as a photoplethysmograph (PPG), for this task. PPG sensors measure the rate at which blood flows through the body, and this rate is regulated by the heart’s pumping action, which constantly occurs throughout the body. Even though detecting body movement and gestures was not initially the primary purpose of PPG signals, we propose an innovative method for extracting relevant features from the PPG signal and use deep learning (DL) to predict physical activities. To accomplish the purpose of our study, we developed a deep residual network referred to as PPG-NeXt, designed based on convolutional operation, shortcut connections, and aggregated multi-branch transformation to efficiently identify different types of daily life activities from the raw PPG signal. The proposed model achieved more than 90% prediction F1-score from experimental results using only PPG data on the three benchmark datasets. Moreover, our results indicate that combining PPG and acceleration signals can enhance activity recognition. Although, both biosignals—electrocardiography (ECG) and PPG—can differentiate between stationary activities (such as sitting) and non-stationary activities (such as cycling and walking) with a level of success that is considered sufficient. Overall, our results propose that combining features from the ECG signal can be helpful in situations where pure tri-axial acceleration (3D-ACC) models have trouble differentiating between activities with relative motion (e.g., walking, stair climbing) but significant differences in their heart rate signatures. Full article

Piezoelectric vibration energy harvester (PVEH) is a promising device for sustainable power supply of wireless sensor nodes (WSNs). PVEH is resonant and generates power under constant frequency vibration excitation of mechanical equipment. However, it cannot output high power through off-resonance if it has frequency offset in manufacturing, assembly and use. To address this issue, this paper designs and optimizes a PVEH to harvest power specifically from grid transformer vibration at 100 Hz with high power density of 5.28 μWmm⁻³g⁻². Some resonant frequency modulation methods of PVEH are discussed by theoretical analysis and experiment, such as load impedance, additional mass, glue filling, axial and transverse magnetic force frequency modulation. Finally, efficient energy harvesting of 6.1 V output in 0.0226 g acceleration is tested in grid transformer reactor field application. This research has practical value for the design and optimization process of tunable PVEH for a specific vibration source. Full article

Because the axial piston pump is often used in the aerospace and aviation fields, it is necessary to pay attention to its outlet pressure and flow characteristics. The parallel 2D piston pump proposed, based on the axial piston pump, has no structural flow ripple because it has a rail with a uniform acceleration and deceleration. Now, the pump is used in the special working conditions of the aerospace field, and it is required to meet the rated flow of 50 L/min, the rated load of 8 MPa, and an extremely low-pressure ripple. Based on CFD technology, this paper studies the pump’s outlet flow and pressure ripples through numerical simulation. According to the causes of the outlet pressure ripple, an improved geometry is determined to further reduce the outlet pressure ripple. Using a high-frequency pressure sensor to measure the outlet pressure ripple of the optimized pump prototype, it was found that the outlet pressure ripple rate of the prototype was only 6%. The parallel 2D piston pump has been proved by the simulation and test that its outlet pressure ripple is extremely low. However, it is not effective to reduce the outlet flow ripple by increasing the pre-pressure and reducing the backflow. In parallel 2D piston pumps, it is still necessary to find a new method to further reduce outlet pressure and flow ripples. Full article

Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill bearing by employing the improved sparrow search algorithm deep belief network (ISAA-DBN) with limited data samples. First, the fast spectral kurtosis approach is adopted to convert the non-stationary original vibration signals collected by the acceleration sensors installed at the axial and radial ends of the rolling mill bearings into two-dimensional (2D) spectral kurtosis time–frequency images with higher feature recognition, and the principal component analysis (PCA) technique is used to decrease the dimension of the data in order to achieve a high diagnosis rate with a limited number of samples. Subsequently, the sparrow search algorithm (SSA) is used to realize the intelligent optimized self-adaptive function of a deep belief network (DBN). Furthermore, the firefly disturbance algorithm is employed to improve the spatial search capability and robustness of SSA-DBN in order to achieve better performance of the ISSA-DBN method. Finally, the proposed approach is experimentally compared to other approaches used for diagnosis. The results show that the proposed approach not only retains the useful features of the data through dimension reduction but also improves the efficiency of the diagnosis and achieves the highest diagnosis accuracy with limited data samples. In addition, the optimal position of the sensor for diagnosing rolling mill roll faults is identified. Full article

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations. Full article