scholarly journals Front-Crawl Instantaneous Velocity Estimation Using a Wearable Inertial Measurement Unit

Sensors ◽  
2012 ◽  
Vol 12 (10) ◽  
pp. 12927-12939 ◽  
Author(s):  
Farzin Dadashi ◽  
Florent Crettenand ◽  
Grégoire P. Millet ◽  
Kamiar Aminian
Keyword(s):  
Inertial Measurement Unit ◽  
Velocity Estimation ◽  
Instantaneous Velocity ◽  
Measurement Unit ◽  
Front Crawl ◽  
Inertial Measurement

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 Globally stable velocity estimation using normalized velocity measurement

2019 ◽  
Vol 39 (1) ◽  
pp. 143-157
Author(s):  
Elias Bjørne ◽  
Edmund F Brekke ◽  
Torleiv H Bryne ◽  
Jeff Delaune ◽  
Tor Arne Johansen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Velocity Estimation ◽  
Nonlinear Observer ◽  
Measurement Unit ◽  
Velocity Measurements ◽  
Nonlinear Methods ◽  
Inertial Measurement ◽  
Globally Stable

The problem of estimating velocity from a monocular camera and calibrated inertial measurement unit (IMU) measurements is revisited. For the presented setup, it is assumed that normalized velocity measurements are available from the camera. By applying results from nonlinear observer theory, we present velocity estimators with proven global stability under defined conditions, and without the need to observe features from several camera frames. Several nonlinear methods are compared with each other, also against an extended Kalman filter (EKF), where the robustness of the nonlinear methods compared with the EKF are demonstrated in simulations and experiments.

scholarly journals Micro air vehicle local pose estimation with a two-dimensional laser scanner: A case study for electric tower inspection

2017 ◽  
Vol 10 (2) ◽  
pp. 127-156
Author(s):  
Carlos Viña ◽  
Pascal Morin
Keyword(s):  
Inertial Measurement Unit ◽  
Position Control ◽  
Micro Air Vehicles ◽  
Indoor Navigation ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Two Dimensional ◽  
Inertial Measurement ◽  
Inspection Tasks ◽  
Air Vehicles

Automation of inspection tasks is crucial for the development of the power industry, where micro air vehicles have shown a great potential. Self-localization in this context remains a key issue and is the main subject of this work. This article presents a methodology to obtain complete three-dimensional local pose estimates in electric tower inspection tasks with micro air vehicles, using an on-board sensor set-up consisting of a two-dimensional light detection and ranging, a barometer sensor and an inertial measurement unit. First, we present a method to track the tower’s cross-sections in the laser scans and give insights on how this can be used to model electric towers. Then, we show how the popular iterative closest point algorithm, that is typically limited to indoor navigation, can be adapted to this scenario and propose two different implementations to retrieve pose information. This is complemented with attitude estimates from the inertial measurement unit measurements, based on a gain-scheduled non-linear observer formulation. An altitude observer to compensate for barometer drift is also presented. Finally, we address velocity estimation with views to feedback position control. Validations based on simulations and experimental data are presented.

Velocity Estimation for Quadrupeds Based on Extended Kalman Filter

2014 ◽  
Vol 621 ◽  
pp. 525-532 ◽  
Author(s):  
Man Tian Li ◽  
Cong Wei Wang ◽  
Peng Fei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Random Error ◽  
Inertial Measurement Unit ◽  
Quadruped Robot ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Forward Kinematics ◽  
Random Errors ◽  
Inertial Measurement

Measuring robots’ real-time velocity correctly is important for locomotion control. Inertial Measurement Unit (IMU) is widely used for velocity measurement. Limited by the bias and random error, IMU alone often can’t meet the requirement. This paper makes use of Extended Kalman Filter (EKF) to fuse kinematics and IMU, and inhibits the drift successfully. We calibrate the bias and recognize the random errors of IMU. Then the forward kinematics of legs is established and the EKF algorithm for velocity estimation is designed based on IMU and kinematics. Finally, the presented algorithm is validated in simulation and on a quadruped robot based on hydraulic driver in trotting gait.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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