scholarly journals Portable Gait Lab: Estimating Over-Ground 3D Ground Reaction Forces Using Only a Pelvis IMU

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6363
Author(s):  
Mohamed Irfan Mohamed Refai ◽  
Bert-Jan F. van Beijnum ◽  
Jaap H. Buurke ◽  
Peter H. Veltink
Keyword(s):  
Inertial Measurement Unit ◽  
Center Of Mass ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Average Error ◽  
Ground Reaction Forces ◽  
Inertial Measurement ◽  
Reaction Forces ◽  
Error State ◽  
Over Ground Walking

As an alternative to force plates, an inertial measurement unit (IMU) at the pelvis can offer an ambulatory method for measuring total center of mass (CoM) accelerations and, thereby, the ground reaction forces (GRF) during gait. The challenge here is to estimate the 3D components of the GRF. We employ a calibration procedure and an error state extended Kalman filter based on an earlier work to estimate the instantaneous 3D GRF for different over-ground walking patterns. The GRF were then expressed in a body-centric reference frame, to enable an ambulatory setup not related to a fixed global frame. The results were validated with ForceShoesTM, and the average error in estimating instantaneous shear GRF was 5.2 ± 0.5% of body weight across different variable over-ground walking tasks. The study shows that a single pelvis IMU can measure 3D GRF in a minimal and ambulatory manner during over-ground gait.

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 The relationship between inertial measurement unit-derived ‘force signatures’ and ground reaction forces during cricket pace bowling

2018 ◽  
Vol 19 (3) ◽  
pp. 307-321 ◽  
Author(s):  
Samuel J. Callaghan ◽  
Robert G. Lockie ◽  
Warren A. Andrews ◽  
Robert F. Chipchase ◽  
Sophia Nimphius
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Ground Reaction Forces ◽  
Inertial Measurement ◽  
Reaction Forces ◽  
The Relationship

scholarly journals Predicting Ground Reaction Forces in Sprint Running Using a Shank Mounted Inertial Measurement Unit

Proceedings ◽  
2018 ◽  
Vol 2 (6) ◽  
pp. 199 ◽  
Author(s):  
David V. Thiel ◽  
Jonathan Shepherd ◽  
Hugo G. Espinosa ◽  
Megan Kenny ◽  
Katrien Fischer ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Ground Reaction Forces ◽  
Inertial Measurement ◽  
Sprint Running ◽  
Reaction Forces

Automated Calibration System for IMU Based on Database and LabVIEW

2013 ◽  
Vol 333-335 ◽  
pp. 2396-2400 ◽  
Author(s):  
Yan Deng ◽  
Chao Xing ◽  
Bin Zhou
Keyword(s):  
Inertial Measurement Unit ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Test Results ◽  
Calibration System ◽  
Inertial Measurement ◽  
Test Items ◽  
Long Time ◽  
Verification Test ◽  
Data Transferring

The calibration of Inertial Measurement Unit (IMU) is the premise of inertial navigation. It includes series of test items, takes a long time, and requires lots of data transferring and calculating operations. Traditional manual method is difficult to ensure its reliability and efficiency. This paper presented an automated IMU calibration system with three layers: the test hardware devices, the calibration software and the calibration database. Calibration software controlled the tests running in the temperature box, on the turntable, vibration table and marble horizontal table. Calibration database stored the test data and calibration parameters. Through the LabVIEW aided database technique, the system not only completed all the test items but also integrated and simplified the calibration procedure. The verification test results showed that the system improved the calibration efficiency and enhanced the calibration reliability greatly.

scholarly journals Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures

2019 ◽  
Vol 6 ◽  
pp. 205566831881345 ◽  
Author(s):  
Rezvan Kianifar ◽  
Vladimir Joukov ◽  
Alexander Lee ◽  
Sachin Raina ◽  
Dana Kulić
Keyword(s):  
Pose Estimation ◽  
Inertial Measurement Unit ◽  
Kinematic Model ◽  
Sensor Placement ◽  
Estimation Method ◽  
Calibration Procedure ◽  
The Body ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Inertial Measurement

Introduction Inertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated. Methods The sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation. Results After applying the protocol, the ankle, knee, and hip joint angle errors improved to [Formula: see text], and [Formula: see text], without the need for any other calibration. Conclusions Only a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.

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.

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