scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

2013 ◽  
Vol 24 (3) ◽  
pp. 199-211 ◽  
Author(s):  
Luciano Buonocore ◽  
Cairo Lúcio Nascimento Júnior ◽  
Areolino de Almeida Neto

2001 ◽  
Vol 38 (01) ◽  
pp. 65-69
Author(s):  
Thomas F. Fulton ◽  
Christopher J. Cassidy

The development of a navigation sensor data fusion algorithm for the autonomous underwater vehicle (AUV) Remus is described. Remus is a small, low-cost AUV designed and built at the Ocean Systems Laboratory of the Woods Hole Oceanographic Institute. The navigation sensors for Remus include an acoustic navigation system, a Doppler velocity sonar, and a compass. The data from these sensors are integrated in an extended Kalman filter, with the objective of producing a more accurate vehicle track. Postprocessing results using data from two recent field trials are presented.


2012 ◽  
pp. 762-769
Author(s):  
LUCIANO BUONOCORE ◽  
AEROLINO DE ALMEIDA NETO ◽  
CAIRO LÚCIO NASCIMENTO JÚNIOR

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1467 ◽  
Author(s):  
Wenbin Zhang ◽  
Youhuan Ning ◽  
Chunguang Suo

With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information.


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