scholarly journals Open-Source Software Library For Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics Using OpenSim

2021 ◽  
Author(s):  
Jere Lavikainen ◽  
Paavo Vartiainen ◽  
Lauri Stenroth ◽  
Pasi Karjalainen
Keyword(s):  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Inverse Kinematics ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Software Library ◽  
Inertial Measurement ◽  
Musculoskeletal Models ◽  
Full Body

Abstract An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.

scholarly journals Open-Source Software Library for Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics using OpenSim

2021 ◽  
Author(s):  
Jere Lavikainen ◽  
Paavo Vartiainen ◽  
Lauri Stenroth ◽  
Pasi Karjalainen
Keyword(s):  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Inverse Kinematics ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Software Library ◽  
Inertial Measurement ◽  
Musculoskeletal Models ◽  
Full Body

Abstract Background: An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Results: Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. Conclusions: The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.

scholarly journals Real-Time Vertical Ground Reaction Force Estimation in a Unified Simulation Framework Using Inertial Measurement Unit Sensors

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 88
Author(s):  
Elliot Recinos ◽  
John Abella ◽  
Shayan Riyaz ◽  
Emel Demircan
Keyword(s):  
Real Time ◽  
Inertial Measurement Unit ◽  
Human Motion ◽  
Whole Body ◽  
Measurement Unit ◽  
Ground Reaction Forces ◽  
Inertial Measurement ◽  
Musculoskeletal Models ◽  
Reaction Forces ◽  
Vertical Ground

Recent advances in computational technology have enabled the use of model-based simulation with real-time motion tracking to estimate ground reaction forces during gait. We show here that a biomechanical-based model including a foot-ground contact can reproduce measured ground reaction forces using inertial measurement unit data during single-leg support, single-support jump, side to side jump, jogging, and skipping. The framework is based on our previous work on integrating the OpenSim musculoskeletal models with the Unity environment. The validation was performed on a single subject performing several tasks that involve the lower extremity. The novelty of this paper includes the integration and real-time tracking of inertial measurement unit data in the current framework, as well as the estimation of contact forces using biologically based musculoskeletal models. The RMS errors of tracking the vertical ground reaction forces are 0.027 bodyweight, 0.174 bodyweight, 0.173 bodyweight, 0.095 bodyweight, and 0.10 bodyweight for single-leg support, single-support jump, side to side jump, jogging, and skipping, respectively. The average RMS error for all tasks and trials is 0.112 bodyweight. This paper provides a computational framework for further applications in whole-body human motion analysis.

scholarly journals Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

2018 ◽  
Vol 64 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Tong-Hun Hwang ◽  
Julia Reh ◽  
Alfred O. Effenberg ◽  
Holger Blume
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement

Development and Implementation of a Novel Digital Shot Sensor System

2013 ◽  
Vol 364 ◽  
pp. 228-232
Author(s):  
Wei Tian Wang ◽  
Quan Jun Song ◽  
Yu Man Nie ◽  
Bu Yun Wang ◽  
Hong Yu Ren ◽  
...  
Keyword(s):  
Real Time ◽  
Information Acquisition ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Sensor System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Kinetic Information ◽  
Shot Put

Kinetic information acquisition of shot throwing is significant for the train of shot put athletes. This paper presents a novel sensor system based on a 9 degrees of freedom inertial measurement unit, which provides attitude information of shot throwing in real time. The sensor system is designed with modularized structure and installed in the digital shot which has almost the same size and weight as the standard shot for females. A multi-target and multi-parameter information acquisition platform is constructed to acquire kinematics information. With the help of the sensor system, the coaches can combine attitude information with kinematics data to analyze the shot throwing movements.

scholarly journals Trends in Inertial Navigation Technologies

2021 ◽  
Vol 906 (1) ◽  
pp. 012069
Author(s):  
Stanislav Hodas ◽  
Jana Izvoltova ◽  
Donatas Rekus
Keyword(s):  
Real Time ◽  
Inertial Measurement Unit ◽  
Electronic Device ◽  
Inertial Navigation ◽  
Relative Orientation ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Below Ground ◽  
Autonomous Technology

Abstract The inertial measurement unit is an electronic device built-in practically in any controlled or autonomous technology used for land mapping. It is based on a combination of accelerometers and gyroscopes and sometimes magnetometers used for relative orientation and navigation. The paper is focused on functions and trends of an inertial measurement unit, which is a part of inertial navigation indicator of position and velocity of moving devices on the ground, above and below ground in real-time.

Visual servoing applied to real-time stabilization of a multi-rotor UAV

Robotica ◽  
2012 ◽  
Vol 30 (7) ◽  
pp. 1203-1212 ◽  
Author(s):  
Hugo Romero ◽  
Sergio Salazar ◽  
Rogelio Lozano
Keyword(s):  
Real Time ◽  
Inertial Measurement Unit ◽  
Visual Servoing ◽  
Vision System ◽  
Measurement Unit ◽  
Translational Velocity ◽  
Inertial Measurement ◽  
Second Stage ◽  
Yaw Angle ◽  
Flying Robot

SUMMARYIn this paper we address the problem of stabilization and local positioning of a four-rotor rotorcraft using computer vision. Our approaches to estimate the orientation and position of the rotorcraft combine the measurements from an Inertial Measurement Unit (IMU) and a vision system composed of a single camera. In the first stage, the vision system is used to estimate the position and yaw angle of the rotorcraft, while in the second stage the vision system is used to estimate the translational velocity of the flying robot. In both cases the IMU gives the pitch and roll angles at a higher rate. The technique used to estimate the position of the rotorcraft in the first stage combines the homogeneous transformation approach for the camera calibration process with the plane-based pose method for estimating the position. In the second stage, a navigation system using the optical flow is also developed to estimate the translational velocity of the aircraft. We present real-time experiments of stabilization and location of a four-rotor rotorcraft.

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