scholarly journals Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1995
Author(s):  
Xiang Fang ◽  
Benedikt Grüter ◽  
Patrick Piprek ◽  
Veronica Bessone ◽  
Johannes Petrat ◽  
...  
Keyword(s):  
State Constraints ◽  
Wearable Sensors ◽  
Measurement Data ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Shape Information ◽  
Video Recordings ◽  
Ski Jumping ◽  
Increasing Demand

To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm.

scholarly journals An Inertial Measurement Unit-Based Wireless System for Shoulder Motion Assessment in Patients with Cervical Spinal Cord Injury: A Validation Pilot Study in a Clinical Setting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1057
Author(s):  
Riccardo Bravi ◽  
Stefano Caputo ◽  
Sara Jayousi ◽  
Alessio Martinelli ◽  
Lorenzo Biotti ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Clinical Setting ◽  
Cervical Spinal Cord ◽  
Wearable Sensors ◽  
Cervical Spinal Cord Injury ◽  
Measurement Unit ◽  
Innovative Technology ◽  
Inertial Measurement ◽  
Cord Injury

Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence. Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients. However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer. Conventional goniometric measurements are operator-dependent and require significant time and effort. Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable. This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units—IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting. Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm. Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time. Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA). inter-tester reliability of IMUs and goniometer measurements was also investigated. Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients.

scholarly journals Dynamic Optimization Method for Broadband ADCP Waveform with Environment Constraints

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3768
Author(s):  
Yongshou Yang ◽  
Shiliang Fang
Keyword(s):  
Distance Estimation ◽  
Acoustic Doppler Current Profiler ◽  
Optimization Method ◽  
Measurement Range ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Dynamic Configuration ◽  
Optimal Velocity ◽  
Flood Warning ◽  
The Impact

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.

scholarly journals Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2673
Author(s):  
Weibo Huang ◽  
Weiwei Wan ◽  
Hong Liu
Keyword(s):  
Measurement Data ◽  
Calibration Method ◽  
Bundle Adjustment ◽  
Measurement Unit ◽  
Temporal Parameters ◽  
Work Routine ◽  
Initial States ◽  
Time Offset ◽  
Public Datasets ◽  
Metric Scale

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

scholarly journals Experimental Validation of Real-Time Ski Jumping Tracking System Based on Wearable Sensors

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7780
Author(s):  
Johannes Link ◽  
Sébastien Guillaume ◽  
Bjoern M. Eskofier
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Wearable Sensors ◽  
Absolute Error ◽  
Opening Angle ◽  
National Team ◽  
Ski Jumping ◽  
Accuracy And Precision ◽  
Real Time Tracking ◽  
Flight Parameters

For sports scientists and coaches, its crucial to have reliable tracking systems to improve athletes. Therefore, this study aimed to examine the validity of a wearable real-time tracking system (WRRTS) for the quantification of ski jumping. The tracking system consists of wearable trackers attached to the ski bindings of the athletes and fixed antennas next to the jumping hill. To determine the accuracy and precision of the WRRTS, four athletes of the German A or B National Team performed 35 measured ski jumps. The WRRTS was used to measure the 3D positions and ski angles during the jump. The measurements are compared with camera measurements for the in-flight parameters and the official video distance for the jumping distance to assess their accuracy. We statistically evaluated the different methods using Bland–Altman plots. We thereby find a mean absolute error of 0.46 m for the jumping distance, 0.12 m for the in-flight positions, and 0.8°, and 3.4° for the camera projected pitch and V-style opening angle, respectively. We show the validity of the presented WRRTS to measure the investigated parameters. Thus, the system can be used as a tracking system during training and competitions for coaches and sports scientists. The real-time feature of the tracking system enables usage during live TV broadcasting.

scholarly journals Valid and Reliable Barbell Velocity Estimation Using an Inertial Measurement Unit

2021 ◽  
Vol 18 (17) ◽  
pp. 9170
Author(s):  
Steffen Held ◽  
Ludwig Rappelt ◽  
Jan-Philip Deutsch ◽  
Lars Donath
Keyword(s):  
Inertial Measurement Unit ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Systematic Bias ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Intraclass Correlation Coefficients ◽  
Reliability Indicators

The accurate assessment of the mean concentric barbell velocity (MCV) and its displacement are crucial aspects of resistance training. Therefore, the validity and reliability indicators of an easy-to-use inertial measurement unit (VmaxPro®) were examined. Nineteen trained males (23.1 ± 3.2 years, 1.78 ± 0.08 m, 75.8 ± 9.8 kg; Squat 1-Repetition maximum (1RM): 114.8 ± 24.5 kg) performed squats and hip thrusts (3–5 sets, 30 repetitions total, 75% 1RM) on two separate days. The MCV and displacement were simultaneously measured using VmaxPro® and a linear position transducer (Speed4Lift®). Good to excellent intraclass correlation coefficients (0.91 < ICC < 0.96) with a small systematic bias (p < 0.001; ηp2 < 0.50) for squats (0.01 ± 0.04 m·s−1) and hip thrusts (0.01 ± 0.05 m·s−1) and a low limit of agreement (LoA < 0.12 m·s−1) indicated an acceptable validity. The within- and between-day reliability of the MCV revealed good ICCs (0.55 < ICC < 0.91) and a low LoA (<0.16 m·s−1). Although the displacement revealed a systematic bias during squats (p < 0.001; ηp2 < 0.10; 3.4 ± 3.4 cm), no bias was detectable during hip thrusts (p = 0.784; ηp2 < 0.001; 0.3 ± 3.3 cm). The displacement showed moderate to good ICCs (0.43 to 0.95) but a high LoA (7.8 to 10.7 cm) for the validity and (within- and between-day) reliability of squats and hip thrusts. The VmaxPro® is considered to be a valid and reliable tool for the MCV assessment.

scholarly journals INS/GPS for High-Dynamic UAV-Based Applications

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Junchuan Zhou ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Carrier Phase ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Processing Load ◽  
Estimation Errors ◽  
Integration Interval ◽  
Sequential Processing ◽  
Large Velocity ◽  
Different Types ◽  
High Dynamic

The carrier-phase-derived delta pseudorange measurements are often used for velocity determination. However, it is a type of integrated measurements with errors strongly related to pseudorange errors at the start and end of the integration interval. Conventional methods circumvent these errors with approximations, which may lead to large velocity estimation errors in high-dynamic applications. In this paper, we employ the extra states to “remember” the pseudorange errors at the start point of the integration interval. Sequential processing is employed for reducing the processing load. Simulations are performed based on a field-collected UAV trajectory. Numerical results show that the correct handling of errors involved in the delta pseudorange measurements is critical for high-dynamic applications. Besides, sequential processing can update different types of measurements without degrading the system estimation accuracy, if certain conditions are met.

scholarly journals Investigating the Benefits of Vector-Based GNSS Receivers for Autonomous Vehicles under Challenging Navigation Environments

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 121-137
Author(s):  
Haidy Y. F. Elghamrawy ◽  
Mohamed Tamazin ◽  
Aboelmagd Noureldin
Keyword(s):  
Autonomous Vehicles ◽  
Tracking System ◽  
Satellite System ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Positioning Accuracy ◽  
Gps Receivers ◽  
Car Industry ◽  
Vector Tracking ◽  
Global Navigation Satellite

There is a growing demand for robust and accurate positioning information for various applications, including the self-driving car industry. Such applications rely mainly on the Global Navigation Satellite System (GNSS), including the Global Positioning System (GPS). However, GPS positioning accuracy relies on several factors, such as satellite geometry, receiver architecture, and navigation environment, to name a few. In urban canyons in which there is a significant probability of signal blockage of one or more satellites and/or interference, the positioning accuracy of scalar-based GPS receivers drastically deteriorates. On the other hand, vector-based GPS receivers exhibit some immunity to momentary outages and interference. Therefore, it is becoming necessary to consider vector-based GPS receivers for several applications, especially safety-critical applications, including next-generation navigation technologies for autonomous vehicles. This paper investigates a vector-based receiver’s performance and compares it to its scalar counterpart in signal degraded conditions. The realistic simulation experiments in this paper are conducted on GPS L1 C/A signals generated using the SpirentTM simulation system to create a fully controlled environment to examine and validate the performance. The results show that the vector tracking system outperforms the scalar tracking in terms of position and velocity estimation accuracy in signal-degraded environments.

scholarly journals Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman Filters Using Geometrical Constraints

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2280 ◽  
Author(s):  
Sören Vogel ◽  
Hamza Alkhatib ◽  
Johannes Bureick ◽  
Rozhin Moftizadeh ◽  
Ingo Neumann
Keyword(s):  
State Constraints ◽  
Laser Scanner ◽  
Measurement Data ◽  
Satellite System ◽  
Sensor Systems ◽  
Measurement Models ◽  
Non Linear ◽  
Geometrical Constraints ◽  
Global Navigation Satellite ◽  
The Impact

Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.

scholarly journals Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 109
Author(s):  
Binbin Su ◽  
Christian Smith ◽  
Elena Gutierrez Farewik
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Measurement Unit ◽  
Deep Convolutional Neural Networks ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Gait Phase ◽  
Robotic Devices ◽  
Phase Recognition ◽  
Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

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