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Backpacks are common devices for carrying external posterior loads. However, relatively little is known about how these external loads affect the ability to recover from balance loss. In this exploratory investigation, 16 young adults (8 female, 8 male) performed forward and backward lean-and-release balance recovery trials, while wearing a backpack that was unloaded or loaded (at 15% of individual body weight). We quantified the effects of backpack loading on balance recovery in terms of maximum recoverable lean angles, center-of-mass kinematics, and temporal-spatial stepping characteristics. Mean values of maximum lean angles were 20° and 9° in response to forward and backward perturbations, respectively. These angles significantly decreased when wearing the additional load for only backward losses of balance. During backward losses of balance, the additional load decreased peak center-of-mass velocity and increased acceleration by ∼10 and 18% respectively, which was accompanied by ∼5% faster stepping responses and steps that were ∼9% longer, 11% higher, and had an ∼10% earlier onset. Thus, wearing a backpack decreases backward balance recovery ability and changes backward recovery stepping characteristics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot's contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

Flapping wings may encounter or 'capture' the wake from previous half-stroke, leading to local changes in the instantaneous aerodynamic force on the wing at the start of each half-stroke. In this paper, I developed a simple approach to integrating prediction of these wake capture effects into existing analytical quasi-steady models for hovering insect flapping flight. The local wake flow field is modelled as an additional induced velocity component normal to the stroke plane of the flapping motion that is blended/switched in at the start of each half-stroke. Comparison of model results against experimental data in the literature shows satisfactory agreement in predicting the wake capture lift and drag variations for eight different test cases. Sensitivity analysis shows that the form of the translation velocity time history has a significant effect on the magnitude of wake capture forces. Profiles that retain high translational velocity right up to stroke reversal evoke a much larger effect from wake capture compared with sinusoidal. This result is significant because while constant flapping translation velocity profiles can be generated in the laboratory, the very high accelerations required near stroke reversals incur high mechanical cost that prevents practical adoption in nature or engineered flapping flight vehicles.

Introduction: The role of real-time data capture (via telematics technology) is gathering prominence as a strategy to provide feedback to young drivers about important road safety issues., Method: A naturalistic driving study was undertaken to determine whether providing personalized feedback (via a smartphone app) to young provisional drivers aged 17-20 years living in metropolitan and regional Western Australia (WA) reduced their risky driving behavior compared to a control group who did not receive feedback. Speeding over the posted speed limit, harsh decelerations (braking), harsh accelerations and overall driving performance, were recorded continuously using the smartphone app during the 11-week study. Four separate Generalised Estimating Equations (GEE) linear regression models were undertaken after accounting for relevant confounders including driving exposure to determine the difference between the intervention and control group for the 4 driving outcomes obtained from the smartphone app., Results: The study found that there was no significant change in overall driving scores between the intervention and control groups (p = 0.35). However, the overall driving score significantly improved by 0.19 points for young provisional drivers who lived in regional areas compared to those in the metropolitan area (p = 0.05) after adjusting for potential confounders. There was also no significant change in harsh braking scores (p = 0.46) and harsh acceleration scores between the intervention and control groups (p = 0.26) However, harsh acceleration scores improved by 0.37 points for females compared to males (p = 0.04). Lastly, there was no significant change in speed scores between the control and intervention groups (p = 0.72). However, the speed scores of participants who lived in regional WA improved by 0.22 points compared to those in the metropolitan area (p = 0.02). Furthermore, for every 1,000 km travelled, speed scores worsened by -0.08 points (p < 0.01) regardless of group., Conclusions: The study did not find any statistical difference in the driving outcomes examined; however the treatment effects for feedback were consistently in the expected positive direction. Young drivers in regional WA also improved their speeding scores and overall driving performance scores compared to young drivers in the metropolitan area. Females, also significantly improved their harsh deceleration scores compared to males, regardless of group allocation. These results highlight the use of smartphone telematics as an opportunity to not only enhance the safety of provisional young drivers but also provide data-informed decision making and policy development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

This paper proposes the design of a 360° map establishment and real-time simultaneous localization and mapping (SLAM) algorithm based on equirectangular projection. All equirectangular projection images with an aspect ratio of 2:1 are supported for input image types of the proposed system, allowing an unlimited number and arrangement of cameras. Firstly, the proposed system uses dual back-to-back fisheye cameras to capture 360° images, followed by the adoption of the perspective transformation with any yaw degree given to shrink the feature extraction area in order to reduce the computational time, as well as retain the 360° field of view. Secondly, the oriented fast and rotated brief (ORB) feature points extracted from perspective images with a GPU acceleration are used for tracking, mapping, and camera pose estimation in the system. The 360° binary map supports the functions of saving, loading, and online updating to enhance the flexibility, convenience, and stability of the 360° system. The proposed system is also implemented on an nVidia Jetson TX2 embedded platform with 1% accumulated RMS error of 250 m. The average performance of the proposed system achieves 20 frames per second (FPS) in the case with a single-fisheye camera of resolution 1024 × 768, and the system performs panoramic stitching and blending under 1416 × 708 resolution from a dual-fisheye camera at the same time.

LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.

This study contributes towards a threshold for the onset of discomfort stemming from vibration that is transmitted to the human body by means of impulsive wave slamming in dynamic shipping environments. Wave slamming is a random, violent, non-linear event. Subjective and objective data were collected from two research voyages on a slamming-prone vessel. Full-scale vertical acceleration measurements were conducted near work and accommodation areas on the vessel. A daily diary survey was used to gather human responses among passengers. In addition, instantaneous slamming vibration comfort ratings were obtained by observers on the ship Bridge during a test sequence that purposefully induced slamming. The results indicate that two different approaches converged to the same VDV 1hr comfort threshold (0.41 m/s 1.75 and 0.43 m/s 1.75 ), which corresponds to the limit where 50% of respondents indicated discomfort. A similar analysis using the r.m.s. metric converged to an identical threshold (0.03 m/s 2 ), irrespective of the accumulated time of assessment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Human motion analysis requires information about the position and orientation of different parts of the human body over time. Widely used are optical methods such as the VICON system and sets of wired and wireless IMU sensors to estimate absolute orientation angles of extremities (Xsens). Both methods require expensive measurement devices and have disadvantages such as the limited rate of position and angle acquisition. In the paper, the adaptation of the drone flight controller was proposed as a low-cost and relatively high-performance device for the human body pose estimation and acceleration measurements. The test setup with the use of flight controllers was described and the efficiency of the flight controller sensor was compared with commercial sensors. The practical usability of sensors in human motion measurement was presented. The issues related to the dynamic response of IMU-based sensors during acceleration measurement were discussed.

Introduction: While micromobility vehicles offer new transport opportunities and may decrease fuel emissions, the extent to which these benefits outweigh the safety costs is still uncertain. For instance, e-scooterists have been reported to experience a tenfold crash risk compared to ordinary cyclists. Today, we still do not know whether the real safety problem is the vehicle, the human, or the infrastructure. In other words, the new vehicles may not necessarily be unsafe; the behavior of their riders, in combination with an infrastructure that was not designed to accommodate micromobility, may be the real issue., Method: In this paper, we compared e-scooters and Segways with bicycles in field trials to determine whether these new vehicles create different constraints for longitudinal control (e.g., in braking avoidance maneuvers)., Results: The results show that acceleration and deceleration performance changes across vehicles; specifically, e-scooters and Segways that we tested cannot brake as efficiently as bicycles. Further, bicycles are experienced as more stable, maneuverable, and safe than Segways and e-scooters. We also derived kinematic models for acceleration and braking that can be used to predict rider trajectories in active safety systems., Practical Applications: The results from this study suggest that, while new micromobility solutions may not be intrinsically unsafe, they may require some behavior and/or infrastructure adaptations to improve their safety. We also discuss how policy making, safety system design, and traffic education may use our results to support the safe integration of micromobility into the transport system., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The study of the vibration phenomenon of pebbles under turbulence is still a gap despite recent technological advancements in measurement capabilities. In this study, the vibration process of a fully exposed, isolated smart pebble on a rough bed was measured using a miniature inertial accelerometer and combined with simultaneous local measurements of the near-bed velocities. The paper conducts a series of experimental studies with different flow conditions. The test data match well with the manually observed phenomena, indicating the authenticity of the sediment vibration data collected by the measurement system. The test results show that the pebble motion (before entrainment) subjected to turbulence is a nonlinear vibration process, and its vibration types include in-situ vibration and ex-situ strong vibration. The probability distribution for the amplitude of vibration acceleration is well parameterized by the normal distribution. The vibration intensity tends to increase before approaching the threshold, but it weakens when approaching the point. The sediment vibration frequency is within 20-25 Hz, similar to the flow pulsation frequency and belongs to low-frequency vibration. The data indicate that the near-bed flow velocity is most directly related to the particle vibration events., (© 2023. The Author(s).)

Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA's TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s.

For accelerometers targeted in inertial navigation field, the DC bias error is the most destructive system error, affecting the final precision of long-term dead reckoning. This paper proposes a novel self-test and self-calibration technique for canceling out the DC bias error of the digital closed-loop accelerometers. The self-test of system DC bias is realized by injecting a 1-Bit ΣΔ modulated digital excitation and measuring the second-order harmonic distortion. As illustrated, the second-order harmonic distortion is related to the servo position deviation of the MEMS sensing element, which is one of the main causes of system DC bias error. The automatic capacitance compensation is carried out based on the amplitude and phase information of the detected second-order harmonic distortion, which can dynamically calibrate out the DC bias error. Test results show that there exists a near-linearity relationship between the system DC bias error and the second-order harmonic distortion, which is consistent with the proposed theoretical deduction. Based on the proposed method, the system DC bias error is effectively reduced from 150 to 4 mg, and unaffected by external acceleration bias.

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church.

On the example of a control system for an unmanned aerial vehicle, we consider the problems of filtering, smoothing and restoring derivatives of reference action signals. These signals determine the desired spatial path of the plant at the first approximation. As a rule, researchers have considered these problems separately and have used different methods to solve each of them. The paper aims to develop a unified approach that provides a comprehensive solution to mentioned problems. We propose a dynamic admissible path generator. It is constructed as a copy of the canonical control plant model with smooth and bounded sigmoid corrective actions. For the deterministic case, a synthesis procedure has been developed, which ensures that the output variables of the generator track a non-smooth reference signal. Moreover, it considers the constraints on the velocity and acceleration of the plant. As a result, the generator variables produce a naturally smoothed spatial curve and its derivatives, which are realizable reference actions for the plant. The construction of the generator does not require exact knowledge of the plant parameters. Its dynamic order is less than that of the standard differentiators. We confirm the effectiveness of the approach by the results of numerical simulation.

In this paper, the invasive speed selection of the monostable travelling wave for a three-component lattice Lotka-Volterra competition system is studied via the upper and lower solution method, as well as the comparison principle. By constructing several special upper and lower solutions, we establish sufficient conditions such that the linear or nonlinear selection is realized.

In this study, two nonsingular three-dimensional (3D) guidance strategies to intercept a stationary target with desired impact angles, subject to nonlinear coupled dynamics, field-of-view (FOV) limit and lateral acceleration bounds, are investigated. As a stepping stone, two novel sliding mode surfaces are designed, the convergence and boundedness of the two sliding mode surfaces can guarantee the impact-angle-constrained interception while maintaining the seeker's lock-on condition during the guidance. A novel auxiliary system is also presented to compensate for the effects caused by input saturation. In addition, the analytical achievable impact angles set is presented in terms of the initial engagement condition. Numerical simulations with various constraints, a realistic missile model and comparison study have been considered to show the feasibility and effectiveness of the proposed strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In this paper, we present a complete and efficient solution of guidance, navigation and control for a quadrotor platform to accomplish 3D coverage flight missions in mapped vineyard terrains. Firstly, an occupancy grid map of the terrain is used to generate a safe guiding coverage path using an Iterative Structured Orientation planning algorithm. Secondly, way-points are extracted from the generated path and added to them trajectory's velocities and accelerations constraints. The constrained way-points are fed into a Linear Quadratic Regulator algorithm so as to generate global minimum snap optimal trajectory while satisfying both the pointing and the corridor constraints. Then, when facing unexpected obstacles, the quadrotor tends to re-plan its path in real-time locally using an Improved Artificial Potential Field algorithm. Finally, a geometric trajectory tracking controller is developed on the Special Euclidean group SE(3). The aim of this controller is to track the generated trajectory while pointing towards predetermined direction using the vector measurements provided by the inertial unit. The performance of the proposed method is demonstrated through several simulation results. In particular, safe guiding paths are achieved. Obstacle-free optimal trajectories that satisfy the way-point position, the pointing direction, and the corridor constraints, are successfully generated with optimized platform snap. Besides, the implemented geometric controller can achieve higher trajectory tracking accuracy with an absolute value of the maximum error in the order of 10-3 m.

This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating," 0.99 for "lying," 0.93 for "walking," and 0.91 for "standing" behaviors. The optimal WS was 7 s for "eating" and "lying," and 3 s for "walking" and "standing." The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

The different acceleration components on the ISS that are responsible for the generation of convective motions in a fluid cell either in the presence of density gradients or in quasi-isodense processes, are analyzed. The NASA measurements of the quasi-steady and periodic acceleration on the ISS are considered and their effects on fluid-dynamic experiments are computed and discussed under different assumptions. In particular, numerical simulations are carried out to identify the relative importance of linear and pendular accelerations, due to possible rotations of the P/L around its center of mass. The effects caused by variable accelerations created by an isolation mount that exhibits an attenuation factor not constant within the payload volume, caused by the reaction forces of the umbilicals, are computed and analyzed., (c2005 Elsevier Ltd. All rights reserved.)

In recent years, cities are experiencing changes in the ways of moving around, increasing the use of micromobility vehicles. Bicycles are the most widespread transport mode and, therefore, cyclists' behaviour, safety, and comfort have been widely studied. However, the use of other personal mobility vehicles is increasing, especially e-scooters, and related studies are scarce. This paper proposes a low-cost open-source data acquisition system to be installed on an e-scooter. This system is based on Raspberry Pi and allows collecting speed, acceleration, and position of the e-scooter, the lateral clearance during meeting and overtaking manoeuvres, and the vibrations experienced by the micromobility users when riding on a bike lane. The system has been evaluated and tested on a bike lane segment to ensure the accuracy and reliability of the collected data. As a result, the use of the proposed system allows highway engineers and urban mobility planners to analyse the behaviour, safety, and comfort of the users of e-scooters. Additionally, the system can be easily adapted to another micromobility vehicle and used to assess pavement condition and micromobility users' riding comfort on a cycling network when the budget is limited.

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle's riding comfort and the physical-mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference.

Fouling and mud-pumping problems in ballasted track significantly degrade serviceability and jeopardize train operational safety. The phenomenological approaches for post hoc forensic investigation and remedies of mud pumps have relatively been well studied, but there still lacks studies on inherent mechanisms and ex ante approaches for early-age detection of mud pumps. This paper was aimed to exploring the feasibility of using particle acceleration responses to diagnose and identify early-age mud-pumping risks in real-world field applications. The innovative wireless sensors with 3D-printed shells resembling real shape of ballast particles were instrumented in the problematic railway section to monitor ballast particle movement prior to, during, and after maintenance operations, respectively. The real-time particle-scale acceleration data of ballast bed under both degraded and maintenance-restored clean conditions were recorded. The time histories, power spectra, and marginal spectra of 3D acceleration were comparatively analyzed. The results showed the 3D acceleration of ballast particles underneath rail-supporting tie plates displayed relatively clear periodicity of about 0.8 s with adjacent bogies regarded as a loading unit. The tamping operation was effective for compacting ballast bed laterally and improving the lateral interlocking of ballast particles, whereas the stabilizing operation was effective mainly in the lateral direction and for ballast particles underneath the sleepers. The mud pumps caused intensive particle-scale acceleration, and ballast particles underneath the sleepers were affected more severely than those in between adjacent sleepers. The ballast particles directly underneath tie plates exhibit dramatic acceleration variations due to maintenance operations as compared to those in other positions studied; hence, it seems promising to use particle-scale acceleration underneath tie plates as readily-implementable indicators for smart in-service track health monitoring.

The primary objective of the International Space Station (ISS) is to provide a long-term quiescent environment for the conduct of scientific research for a variety of microgravity science disciplines. This paper reports to the microgravity scientific community the results of an initial characterization of the microgravity environment on the International Space Station for increments 2 through 4. During that period almost 70,000 hours of station operations and scientific experiments were conducted. 720 hours of crew research time were logged aboard the orbiting laboratory and over half a terabyte of acceleration data were recorded and much of that was analyzed. The results discussed in this paper cover both the quasi-steady and vibratory acceleration environment of the station during its first year of scientific operation. For the quasi-steady environment, results are presented and discussed for the following: the space station attitudes Torque Equilibrium Attitude and the X-Axis Perpendicular to the Orbital Plane; station docking attitude maneuvers; Space Shuttle joint operation with the station; cabin de-pressurizations and the station water dumps. For the vibratory environment, results are presented for the following: crew exercise, docking events, and the activation/de-activation of both station life support system hardware and experiment hardware. Finally, a grand summary of all the data collected aboard the station during the 1-year period is presented showing where the overall quasi-steady and vibratory acceleration magnitude levels fall over that period of time using a 95th percentile benchmark., (Published by Elsevier Ltd.)

The concept of digital twin has been introduced for some time, yet one fundamental element of digital twin, digital material, has not been thoroughly studied. To interact with the physical product, the digital twin should always truthfully reflect the responses under various stimuli. In this paper, the deformation and fracture behavior of high impact polystyrene (HIPS) under the influencing factors of strain rate and stress triaxiality are studied to construct the material's digital model. A digital twin of air conditioner product is further built and tested under virtual drop test. Comparing to experimental results, the acceleration curve, crazing induced whitening and the fracture events can all be captured by the digital twin. Our work demonstrates the importance of material characterization as an essential step to construct an accurate digital twin and shows a promising future of digital twin in virtual testing to replace traditional "trial and error" experiments., (© 2022. The Author(s).)

The real-time identification of time-varying cable force is critical for accurately evaluating the fatigue damage of cables and assessing the safety condition of bridges. In the context of unknown wind excitations and only one available accelerometer, this paper proposes a novel cable force identification method based on an improved adaptive extended Kalman filter (IAEKF). Firstly, the governing equation of the stay cable motion, which includes the cable force variation coefficient, is expressed in the modal domain. It is transformed into a state equation by defining an augmented Kalman state vector with the cable force variation coefficient concerned. The cable force variation coefficient is then recursively estimated and closely tracked in real time by the proposed IAEKF. The contribution of this paper is that an updated fading-factor matrix is considered in the IAEKF, and the adaptive noise error covariance matrices are determined via an optimization procedure rather than by experience. The effectiveness of the proposed method is demonstrated by the numerical model of a real-world cable-supported bridge and an experimental scaled steel stay cable. Results indicate that the proposed method can identify the time-varying cable force in real time when the cable acceleration of only one measurement point is available.

This paper investigates the possibility of developing a new method for fault detection of a damper in the primary suspension of the railway vehicle, based on the analysis of the vertical vibration's response of the bogie. To this purpose, experimental data are used, along with results from numerical simulations regarding the Root Mean Square (RMS) accelerations measured/simulated in four reference bogie points-two points on the chassis, against the suspension, and two points located against the axle boxes. The experimental data are utilized to define the normal area of operating and the damper failure area in the bogie primary suspension, as well as a basis for validating the results of numerical simulations. The numerical simulations are developed on the basis of two original models of the vehicle-track system, rigid-flexible coupled type, which take into account the elasticity of the vehicle carbody and the elasticity of the wheel-rail contact: a reference model with 15 degrees of freedom, for simulating the bogie response to vertical vibrations for the normal operating of the primary suspension dampers, and an extended model with 20 degrees of freedom, for simulating the bogie vibration response to the failure damper of a primary suspension. The presented results show that there are clear premises on the possibilities of developing a fault detection method of any of the four dampers of the primary suspension corresponding to a vehicle bogie, based on the RMS accelerations measured only in two reference points of the bogie.

Dynamic displacement plays an essential role in structural health monitoring. To overcome the shortcomings of displacement measured directly, such as installation difficulty of monitoring devices, this paper proposes a smart reconstruction method, which can realize real-time intelligent online reconstruction of structural displacement. Unlike the existing approaches, the proposed algorithm combines the improved mode superposition methods that is suitable for complex beam structures with the Kalman filtering approach using acceleration and strain data. The effectiveness of the proposed multi-rate data fusion method for dynamic displacement reconstruction is demonstrated by both numerical simulation and model vibration experiment. Parametric analysis shows that the reconstruction error is only 5% when the noise signal to noise ratio is 5 dB, illustrating that the proposed algorithm has excellent anti-noise performance. The results also indicate that both the high-frequency and low-frequency components of the dynamic displacements can be accurately reconstructed through the proposed method, which has good robustness.

In this article, we address the determination of turning radius and lateral acceleration acting on a vehicle up to 3.5 t gross vehicle mass (GVM) and cargo in curves based on turning radius and speed. Global Navigation Satellite System with Inertial Navigation System (GNSS/INS) dual-antenna sensor is used to measure acceleration, speed, and vehicle position to determine the turning radius and determine the proper formula to calculate long average lateral acceleration acting on vehicle and cargo. The two methods for automatic selection of events were applied based on stable lateral acceleration value and on mean square error (MSE) of turning radiuses. The models of calculation of turning radius are valid for turning radius within 5-70 m for both methods of automatic selection of events with mean root mean square error (RMSE) 1.88 m and 1.32 m. The models of calculation of lateral acceleration are valid with mean RMSE of 0.022 g and 0.016 g for both methods of automatic selection of events. The results of the paper may be applied in the planning and implementation of packing and cargo securing procedures to calculate average lateral acceleration acting on vehicle and cargo based on turning radius and speed for vehicles up to 3.5 t GVM. The results can potentially be applied for the deployment of autonomous vehicles in solutions grouped under the term of Logistics 4.0.

The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an a prior unknown environment.

Civil infrastructure worldwide is subject to factors such as aging and deterioration. Structural health monitoring (SHM) can be used to assess the impact of these processes on structural performance. SHM demands have evolved from routine monitoring to real-time and autonomous assessment. One of the frontiers in achieving effective SHM systems has been the use of wireless smart sensors (WSSs), which are attractive compared to wired sensors, due to their flexibility of use, lower costs, and ease of long-term deployment. Most WSSs use accelerometers to collect global dynamic vibration data. However, obtaining local behaviors in a structure using measurands such as strain may also be desirable. While wireless strain sensors have previously been developed by some researchers, there is still a need for a high sensitivity wireless strain sensor that fully meets the general demands for monitoring large-scale civil infrastructure. In this paper, a framework for synchronized wireless high-fidelity acceleration and strain sensing, which is commonly termed multimetric sensing in the literature, is proposed. The framework is implemented on the Xnode, a next-generation wireless smart sensor platform, and integrates with the strain sensor for strain acquisition. An application of the multimetric sensing framework is illustrated for total displacement estimation. Finally, the potential of the proposed framework integrated with vision-based measurement systems for multi-point displacement estimation with camera-motion compensation is demonstrated. The proposed approach is verified experimentally, showing the potential of the developed framework for various SHM applications.

Despite the significant influence of ice conditions on results in the sport of curling, players and ice technicians lack a measurement device that would objectively measure ice quality during a curling competition. This paper presents such a new measurement method by using a device consisting an inertial measurement unit (IMU) attached to the handle of the curling stone and data processing software. IMU is used to measure the vibration of curling stone during its movement on the surface of the ice. The acceleration signal is recorded, and then the software calculates the value of so-called R parameter in frequency domain. The value of R allows one to determine if an ice sheet had been pebbled and if the shape of pebbles is suitable for the game of curling. The presented system was tested in various ice conditions-on both freshly prepared and used ice. Ice technicians and players may use the proposed system to decide whether the ice surface is suitable for play or if it should be remade.

Smartphone accelerometers and low-cost Global Navigation Satellite System (GNSS) equipment have faced rapid and important advancement, opening a new door to deformation monitoring applications such as landslide, plate tectonics and structural health monitoring (SHM). The precision potential and operational feasibility of the equipment play an important role in the decision making of campaigning for affordable solutions. This paper focuses on the evaluation of the empirical precision, including (auto)time correlation, of a common smartphone accelerometer (Bosch BMI160) and a low-cost dual frequency GNSS reference-rover pair (u-blox ZED-F9P) set to operate at high rates (50 and 5 Hz, respectively). Additionally, a high-rate (5 Hz) GPS-only baseline-based multipath (MP) correction is proposed for effectively removing a large part of this error and allowing to correctly determine the instrumental noise of the GNSS sensor. Furthermore, the benefit of smartphone-based validation for the tracking of dynamic displacements is addressed. The estimated East-North-Up (ENU) precision values (σ^) of ±7.7, 8.1 and 9.6 mms2 are comparable with the declared precision potential (σ) of the smartphone accelerometer of ±8.8mms2. Furthermore, the acceleration noise shows only mild traces of (auto)correlation. The MP-corrected 3D (ENU) empirical precision values of ±2.6, 3.6 and 6.7 mm were found to be better by 30-40% than the straight-out-of box precision of the GNSS sensor, attesting the usefulness of the MP correction. The GNSS sensors output position information with time correlation of typically tens of seconds. The results indicate exceptional precision potential of these low-power-consuming, small-scale, affordable sensors set to operate at a high-rate over small regions. The smartphone-based dynamic displacement validation shows that GNSS data of a low-cost sensor at a 5 Hz sampling rate can be successfully used for tracking dynamic processes.

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent.

Projectiles are subjected to a high acceleration shock at launch (20,000 g and higher) and can spin very fast. Thus, the components of onboard navigation units must therefore withstand such constraints in addition to being inexpensive. This makes only a few inertial sensors suitable for projectiles navigation. Particularly, rate gyroscopes which are gun-hardened and have an appropriate operating range are not widely available. On the other hand, magneto-resistive sensors are inexpensive and can satisfy both gun-hardening and operating range requirements, making them an alternative for angular estimation in guided projectiles. This paper presents a gyroless navigation algorithm for projectiles. The lack of gyroscope is handled by the usage of attitude kinematics computed over past attitude estimates of the filter, coupled with a measurement model based on magnetometer and GPS observations of the attitude. The observability of the attitude when considering non-calibrated magnetometers and its dependency on the initialization is addressed. Then, to cope with the initialization dependency of the filter, we proposed a multi-hypothesis initialization algorithm. In terms of performance, the algorithm is shown to provide a high-rate navigation solution with an interesting performance.

Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100-500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

Nowadays, engineers are widely using accelerometers to record the vibration of structures for structural verification purposes. The main obstacle for using these data acquisition systems is their high cost, which limits its use to unique structures with a relatively high structural health monitoring budget. In this paper, a Cost Hyper-Efficient Arduino Product (CHEAP) has been developed to accurately measure structural accelerations. CHEAP is a system that is composed of five low-cost accelerometers that are connected to an Arduino microcontroller as their data acquisition system. Test results show that CHEAP not only has a significantly lower price (14 times cheaper in the worst-case scenario) compared with other systems used for comparison but also shows better accuracy on low frequencies for low acceleration amplitudes. Moreover, the final output results of Fast Fourier Transformation (FFT) assessments showed a better observable resolution for CHEAP than the studied control systems.

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject's body and orientation measurements of body segments. Collected data include positions' measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters' set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

To achieve good performance, athletes need to synchronize a series of movements in an optimal manner. One of the indicators used to monitor this is the order of occurrence of relevant events in the movement timeline. However, monitoring of this characteristic of rapid movement is practically limited to the laboratory settings, in which motion tracking systems can be used to acquire relevant data. Our motivation is to implement a simple-to-use and robust IMU-based solution suitable for everyday praxis. In this way, repetitive execution of technique can be constantly monitored. This provides augmented feedback to coaches and athletes and is relevant in the context of prevention of stabilization of errors, as well as monitoring for the effects of fatigue. In this research, acceleration and rotational speed signal acquired from a pair of IMUs (Inertial Measurement Unit) is used for detection of the time of occurrence of events. The research included 165 individual strikes performed by 14 elite and national-level karate competitors. All strikes were classified as slow, average, or fast based on the achieved maximal velocity of the hand. A Kruskal-Wallis test revealed significant general differences in the order of occurrence of hand acceleration start, maximal hand velocity, maximal body velocity, maximal hand acceleration, maximal body acceleration, and vertical movement onset between the groups. Partial differences were determined using a Mann-Whitney test. This paper determines the differences in the temporal structure of the reverse punch in relation to the achieved maximal velocity of the hand as a performance indicator. Detecting the time of occurrence of events using IMUs is a new method for measuring motion synchronization that provides a new insight into the coordination of articulated human movements. Such application of IMU can provide additional information about the studied structure of rapid discrete movements in various sporting activities that are otherwise imperceptible to human senses.

This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16-30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.

Only one accelerometer is used in this paper for estimating the maximum inter-story drifts and time histories of the relative displacements of all stories of multi-degree-of-freedom (MDOF) shear structures under seismic excitation. The calculation based on the data of one sensor using a conventional method is unstable, and when modal coordinates are used, higher modes should be included, which is different from the estimation based on the responses recorded by many accelerometers. However, the parameters of the higher modes of structures are difficult to obtain from structures under small excitations. To overcome this difficulty, the recorded absolute acceleration is converted into the absolute displacement, and a state-space equation is formulated. Numerical simulations of a nine-story structure were conducted to check the applicability, robustness against environmental noise, and optimal installation location of the accelerometer of the proposed approach. In addition, the effects of the higher modes were analyzed in terms of the number of accelerometers and type of response. Finally, the proposed approach was validated in a simple experiment. The results indicate that it can accurately estimate the time histories of the relative displacements and maximum inter-story drifts of all floors when one accelerometer is used and just the first two modal parameters are incorporated in the model. Furthermore, the approach is robust against environmental noise.

Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs' reference frames, referring to IMUs' individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames' deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation.

This study investigated the effects of using a single-axis force gauge for push/pull force measurement on kinetic/kinematic measures associated with the exertion and assessed agreement between forces recorded from two technologies (single-axis gauge, three-dimensional hand transducer) and various test conditions via intraclass correlations. Independent measures included exertion type (push, pull, turn), test condition (natural/cart alone, using force gauge at fast/slow/self-selected paces), and cart weight (light, heavy). Dependent measures included mean angles of force application, peak forces recorded from both technologies, and cart velocity. Excellent agreement was observed between technologies (ICC = 0.998). Likewise, peak forces using the single-axis gauge at the fast pace agreed best with the natural test condition (ICC = 0.631). Forces should be measured using a faster initial acceleration and sustained velocity than is prescribed by the current standard if they are to accurately approximate forces relative to existing push/pull guidelines. Future work should also develop recommendations for measuring turning forces., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Loaded walking with a rucksack results in both gravitational and inertial forces of the load that must be borne by human carriers. The inertial force may be the source of metabolic burden and musculoskeletal injuries. This paper presents a lightweight backpack with a disturbance observer-based acceleration control to minimize the inertial force. The backpack was evaluated by seven participants walking on a treadmill at 5 km h -1 with a 19.4 kg load. Three experimental conditions were involved, including walking with a locked load (LOCKED), with an acceleration-controlled load (ACTIVE) using the designed backpack and walking with the same load using a rucksack (RUCKSACK). Our results showed that the ACTIVE condition reduces the load acceleration by 98.5% on average, and reduce the gross metabolic power by 8.0% and 11.0% as compared to LOCKED and RUCKSACK conditions respectively. The results demonstrate that the proposed active backpack can improve the loaded walking economy compared with a conventional rucksack in level-ground walking.

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation-while ignoring the inherent correlation in high-dimensional spaces-which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

Microelectromechanical system (MEMS) liquid sensors may be used under large acceleration conditions. It is important to understand the deformation of the liquid droplets under acceleration for the design and applications of MEMS liquid sensors, as this will affect the performance of the sensors. This paper presents an investigation into the deformation of a mercury droplet in a liquid MEMS sensor under accelerations and reports the relationship between the deformation and the accelerations. The Laminar level set method was used in the numerical process. The geometric model consisted of a mercury droplet of 2 mm in diameter and an annular groove of 2.5 mm in width and 2.5 mm in height. The direction of the acceleration causing the droplet to deform is perpendicular to the direction of gravity. Fabrication and acceleration experiments were conducted. The deformation of the liquid was recorded using a high-speed camera. Both the simulation and experimental results show that the characteristic height of the droplets decreases as the acceleration increases. At an acceleration of 10 m/s 2 , the height of the droplet is reduced from 2 to 1.658 mm, and at 600 m/s 2 the height is further reduced to 0.246 mm. The study finds that the droplet can deform into a flat shape but does not break even at 600 m/s 2 . Besides, the properties of the material in the domain surrounding the droplet and the contact angle also affect the deformation of the droplet. This work demonstrates the deformation of the liquid metal droplets under acceleration and provides the basis for the design of MEMS droplet acceleration sensors.

Monitor and classify behavioral activities in cows is a helpful support solution for livestock based on the analysis of data from sensors attached to the animal. Accelerometers are particularly suited for monitoring cow behaviors due to small size, lightweight and high accuracy. Nevertheless, the interpretation of the data collected by such sensors when characterizing the type of behaviors still brings major challenges to developers, related to activity complexity (i.e., certain behaviors contain similar gestures). This paper presents a new design of cows' behavior classifier based on acceleration data and proposed feature set. Analysis of cow acceleration data is used to extract features for classification using machine learning algorithms. We found that with 5 features (mean, standard deviation, root mean square, median, range) and 16-second window of data (1 sample/second), classification of seven cow behaviors (including feeding, lying, standing, lying down, standing up, normal walking, active walking) achieved the overall highest performance. We validated the results with acceleration data from a public source. Performance of our proposed classifier was evaluated and compared to existing ones in terms of the sensitivity, the accuracy, the positive predictive value, and the negative predictive value.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics., Competing Interests: We have the following competing interests: R. Paul Crawford’s company, Hegemony Technologies, has financial interests in the reported technology and may pursue commercial use of the reported findings. There are no patents or further products currently in development or marketed products to declare related directly to the results presented in this paper. Additionally, there are no current plans to pursue patents related to the work presented in this paper. Hegemony Technologies has no further products in development or marketed products to declare. None of these potentially competing interests alter our adherence to all the PLOS ONE policies on sharing data and materials.

Connected vehicles (CVs) exchange a variety of information instantly with surrounding vehicles and traffic facilities, which could smooth traffic flow significantly. The objective of this paper is to analyze the effect of CVs on running speed. This study compared the delay time, travel time, and running speed in the normal and the connected states, respectively, through VISSIM (a traffic simulation software developed by PTV company in German). The optimization speed model was established to simulate the decision-makings of CVs in MATLAB, considering the parameters of vehicle distance, average speed, and acceleration, etc. After the simulation, the vehicle information including speed, travel time, and delay time under the normal and the connected states were compared and evaluated, and the influence of different CV rates on the results was analyzed. In a two-lane arterial road, running speed in the connected state increase by 4 km/h, and the total travel time and delay time decrease by 5.34% and 16.76%, respectively, compared to those in the normal state. The optimal CV market penetration rate related to running speed and delay time is 60%. This simulation-based study applies user-defined lane change and lateral behavior rules, and takes different CV rates into consideration, which is more reliable and practical to estimate the impact of CV on road traffic characteristics.

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ADHD, result not only in differences in the activity levels but also in the activity patterns themselves. This paper proposes and trains a Recurrent Neural Network (RNN) to characterize the moment patterns for normally developing children and uses the trained RNN in order to assess differences in the movement patterns from children with ADHD. Each child is monitored for 24 consecutive hours, in a normal school day, wearing 4 tri-axial accelerometers (one at each wrist and ankle). The results for both medicated and non-medicated children with ADHD, and for different activity levels are presented. While the movement patterns for non-medicated ADHD diagnosed participants showed higher differences as compared to those of normally developing participants, those differences were only statistically significant for medium intensity movements. On the other hand, the medicated ADHD participants showed statistically different behavior for low intensity movements.