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Bonfiglio A, Tacconi D, Bongers RM, Farella E. Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation. Front Bioeng Biotechnol 2024; 12:1385750. [PMID: 38835976 PMCID: PMC11148670 DOI: 10.3389/fbioe.2024.1385750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/17/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint. Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA). Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly. Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.
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Affiliation(s)
- Alessandro Bonfiglio
- Information Engineering and Computer Science Department (DISI), University of Trento, Trento, Italy
- Euleria Health, Rovereto, Italy
- Energy Efficient Embedded Digital Architectures, Fondazione Bruno Kessler (FBK), Trento, Italy
| | | | - Raoul M Bongers
- Department of Human Movement Sciences, University Medical Center Groningen, Groningen, Netherlands
| | - Elisabetta Farella
- Energy Efficient Embedded Digital Architectures, Fondazione Bruno Kessler (FBK), Trento, Italy
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2
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He T, Yang T, Konomi S. Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:3123. [PMID: 38793976 PMCID: PMC11125183 DOI: 10.3390/s24103123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Human motion capture technology, which leverages sensors to track the movement trajectories of key skeleton points, has been progressively transitioning from industrial applications to broader civilian applications in recent years. It finds extensive use in fields such as game development, digital human modeling, and sport science. However, the affordability of these sensors often compromises the accuracy of motion data. Low-cost motion capture methods often lead to errors in the captured motion data. We introduce a novel approach for human motion reconstruction and enhancement using spatio-temporal attention-based graph convolutional networks (ST-ATGCNs), which efficiently learn the human skeleton structure and the motion logic without requiring prior human kinematic knowledge. This method enables unsupervised motion data restoration and significantly reduces the costs associated with obtaining precise motion capture data. Our experiments, conducted on two extensive motion datasets and with real motion capture sensors such as the SONY (Tokyo, Japan) mocopi, demonstrate the method's effectiveness in enhancing the quality of low-precision motion capture data. The experiments indicate the ST-ATGCN's potential to improve both the accessibility and accuracy of motion capture technology.
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Affiliation(s)
- Tianjia He
- Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
| | - Tianyuan Yang
- Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
| | - Shin’ichi Konomi
- Faculty of Arts and Science, Kyushu University, Fukuoka 819-0395, Japan
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3
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Hughes GTG, Camomilla V, Vanwanseele B, Harrison AJ, Fong DTP, Bradshaw EJ. Novel technology in sports biomechanics: some words of caution. Sports Biomech 2024; 23:393-401. [PMID: 33896368 DOI: 10.1080/14763141.2020.1869453] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Gerwyn T G Hughes
- Department of Kinesiology, University of San Francisco, San Francisco, CA, USA
| | - Valentina Camomilla
- Department of Movement, Human and Health Science, University of Rome "Foro Italico", Rome, Italy
| | - Benedicte Vanwanseele
- Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Andrew J Harrison
- Biomechanics Research Unit, University of Limerick, Limerick, Ireland
| | - Daniel T P Fong
- National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Elizabeth J Bradshaw
- Centre for Sport Research, School of Exercise and Nutrition Science, Deakin University, Melbourne, Australia
- Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, New Zealand
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4
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Taetz B, Lorenz M, Miezal M, Stricker D, Bleser-Taetz G. JointTracker: Real-time inertial kinematic chain tracking with joint position estimation. OPEN RESEARCH EUROPE 2024; 4:33. [PMID: 38953016 PMCID: PMC11216284 DOI: 10.12688/openreseurope.16939.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/22/2024] [Indexed: 07/03/2024]
Abstract
In-field human motion capture (HMC) is drawing increasing attention due to the multitude of application areas. Plenty of research is currently invested in camera-based (markerless) HMC, with the advantage of no infrastructure being required on the body, and additional context information being available from the surroundings. However, the inherent drawbacks of camera-based approaches are the limited field of view and occlusions. In contrast, inertial HMC (IHMC) does not suffer from occlusions, thus being a promising approach for capturing human motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to the underlying skeleton model. This work contributes to calibration-free IHMC, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot. The estimated and derived quantities, global and relative sensor orientations, joint positions, and segment lengths can be exploited for human motion analysis and anthropometric measurements, as well as in the context of hybrid markerless visual-inertial HMC.
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Affiliation(s)
- Bertram Taetz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
- IT & Engineering, International University of Applied Sciences, Erfurt, Thuringia, 99084, Germany
| | - Michael Lorenz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Markus Miezal
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Didier Stricker
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
| | - Gabriele Bleser-Taetz
- Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany
- IT & Engineering, International University of Applied Sciences, Erfurt, Thuringia, 99084, Germany
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5
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Wang F, Jia R, He X, Wang J, Zeng P, Hong H, Jiang J, Zhang H, Li J. Detection of kinematic abnormalities in persons with knee osteoarthritis using markerless motion capture during functional movement screen and daily activities. Front Bioeng Biotechnol 2024; 12:1325339. [PMID: 38375453 PMCID: PMC10875007 DOI: 10.3389/fbioe.2024.1325339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
Background: The functional movement screen (FMS) has been used to identify deficiencies in neuromuscular capabilities and balance among athletes. However, its effectiveness in detecting movement anomalies within the population afflicted by knee osteoarthritis (KOA), particularly through the application of a family-oriented objective assessment technique, remains unexplored. The objective of this study is to investigate the sensitivity of the FMS and daily activities in identifying kinematic abnormalities in KOA people employing a markerless motion capture system. Methods: A total of 45 persons, presenting various Kellgren-Lawrence grades of KOA, along with 15 healthy controls, completed five tasks of the FMS (deep squat, hurdle step, and in-line lunge) and daily activities (walking and sit-to-stand), which were recorded using the markerless motion capture system. The kinematic waveforms and discrete parameters were subjected to comparative analysis. Results: Notably, the FMS exhibited greater sensitivity compared to daily activities, with knee flexion, trunk sagittal, and trunk frontal angles during in-line lunge emerging as the most responsive indicators. Conclusion: The knee flexion, trunk sagittal, and trunk frontal angles during in-line lunge assessed via the markerless motion capture technique hold promise as potential indicators for the objective assessment of KOA.
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Affiliation(s)
- Fei Wang
- Department of Anatomy, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Nanchang Medical College, Nanchang, China
| | - Rui Jia
- Department of Anatomy, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Department of Rehabilitation Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiuming He
- Zhongshan Torch Development Zone People’s Hospital, Zhongshan, China
| | - Jing Wang
- Zhongshan Torch Development Zone People’s Hospital, Zhongshan, China
| | - Peng Zeng
- Zhongshan Torch Development Zone People’s Hospital, Zhongshan, China
| | - Hong Hong
- Department of Anatomy, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Jiang Jiang
- Department of Anatomy, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hongtao Zhang
- Zhongshan Torch Development Zone People’s Hospital, Zhongshan, China
| | - Jianyi Li
- Department of Anatomy, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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Pacher L, Carcreff L, Armand S, Chatellier C, Vauzelle R, Fradet L. Gait kinematics based on inertial measurement units with the sensor-to-segment calibration and multibody optimization adapted to the patient's motor capacities, a pilot study. Gait Posture 2024; 108:275-281. [PMID: 38171183 DOI: 10.1016/j.gaitpost.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 11/09/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION Inertial Measurement Units (IMUs) offer a promising alternative to optoelectronic systems to obtain joint lower-limb kinematics during gait. However, the associated methodologies, such as sensor-to-segment (S2S) calibration and multibody optimization, have been developed mainly for, and tested on, asymptomatic subjects. RESEARCH QUESTION This study proposes to evaluate two personalizations of the methodology used to obtain lower-body kinematics from IMUs with pathological subjects: S2S calibration and multibody optimization. METHODS Based on previous studies, two decision trees were developed to select the best (in terms of accuracy and repeatability) S2S methods to be performed by the patient given his/her abilities. The multibody optimization was personalized by limiting the kinematic chain range of motion to the results of the subject's clinical examination. These two propositions were tested on 12 patients with various gait deficits. The patients were equipped with IMUs and reflective markers tracked by an optoelectronic system. They had to perform the postures and movements selected by the decision trees then walk back and forth along a walkway. Gait kinematics obtained from the IMUs directly (referred to as Direct kinematics), and after multibody optimization performed via the OpenSim software using the generic range of motion (referred to as Generic Optimized kinematics), and using the personalized range of motion (referred to as Personalized Optimized kinematics) were compared to those obtained with the Conventional Gait Model through Root Mean Square Errors (RMSE), Correlation Coefficients (CC) and Range of Motion differences (ΔROM). RESULTS The RMSEs were smaller than 8.1° in the sagittal plane but greater than 7.4° in the transverse plane. The CCs, between 0.71 and 0.99 in the sagittal plane, deteriorate sharply in the frontal and transverse planes where they only measured between 0.15 and 0.68. The ΔROMs were mostly below 8.3°. Optimized kinematics did not improve compared to Direct kinematics. SIGNIFICANCE The personalization of the proposed S2S calibration method showed encouraging results, whereas multibody optimization did not impact the resulting joint kinematics.
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Affiliation(s)
- Léonie Pacher
- Robotique, Biomécanique, Sport, Santé, Institut PPrime, UPR 3346 CNRS-Université de Poitiers, Futuroscope, France; Equipe SYstèmes et réseaux de COMmunications Optique et Radio, Institut XLIM UMR CNRS 7252, Futuroscope, France
| | - Léna Carcreff
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Stéphane Armand
- Kinesiology Laboratory, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Christian Chatellier
- Equipe SYstèmes et réseaux de COMmunications Optique et Radio, Institut XLIM UMR CNRS 7252, Futuroscope, France
| | - Rodolphe Vauzelle
- Equipe SYstèmes et réseaux de COMmunications Optique et Radio, Institut XLIM UMR CNRS 7252, Futuroscope, France
| | - Laetitia Fradet
- Robotique, Biomécanique, Sport, Santé, Institut PPrime, UPR 3346 CNRS-Université de Poitiers, Futuroscope, France.
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7
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Weston AR, Antonellis P, Fino PC, Hoppes CW, Lester ME, Weightman MM, Dibble LE, King LA. Quantifying Turning Tasks With Wearable Sensors: A Reliability Assessment. Phys Ther 2024; 104:pzad134. [PMID: 37802908 DOI: 10.1093/ptj/pzad134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 07/05/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023]
Abstract
OBJECTIVE The aim of this study was to establish the test-retest reliability of metrics obtained from wearable inertial sensors that reflect turning performance during tasks designed to imitate various turns in daily activity. METHODS Seventy-one adults who were healthy completed 3 turning tasks: a 1-minute walk along a 6-m walkway, a modified Illinois Agility Test (mIAT), and a complex turning course (CTC). Peak axial turning and rotational velocity (yaw angular velocity) were extracted from wearable inertial sensors on the head, trunk, and lumbar spine. Intraclass correlation coefficients (ICCs) were established to assess the test-retest reliability of average peak turning speed for each task. Lap time was collected for reliability analysis as well. RESULTS Turning speed across all tasks demonstrated good to excellent reliability, with the highest reliability noted for the CTC (45-degree turns: ICC = 0.73-0.81; 90-degree turns: ICC = 0.71-0.83; and 135-degree turns: ICC = 0.72-0.80). The reliability of turning speed during 180-degree turns from the 1-minute walk was consistent across all body segments (ICC = 0.74-0.76). mIAT reliability ranged from fair to excellent (end turns: ICC = 0.52-0.72; mid turns: ICC = 0.50-0.56; and slalom turns: ICC = 0.66-0.84). The CTC average lap time demonstrated good test-retest reliability (ICC = 0.69), and the mIAT average lap time test-retest reliability was excellent (ICC = 0.91). CONCLUSION Turning speed measured by inertial sensors is a reliable outcome across a variety of ecologically valid turning tasks that can be easily tested in a clinical environment. IMPACT Turning performance is a reliable and important measure that should be included in clinical assessments and clinical trials.
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Affiliation(s)
- Angela R Weston
- Department of Physical Therapy & Athletic Training, University of Utah, Salt Lake City, Utah, USA
- Army-Baylor University Doctoral Program in Physical Therapy, Fort Sam Houston, San Antonio, Texas, USA
| | - Prokopios Antonellis
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, Utah, USA
| | - Carrie W Hoppes
- Army-Baylor University Doctoral Program in Physical Therapy, Fort Sam Houston, San Antonio, Texas, USA
| | - Mark E Lester
- Department of Physical Therapy, University of Texas Rio Grande Valley, Harlingen, Texas, USA
| | | | - Leland E Dibble
- Department of Physical Therapy & Athletic Training, University of Utah, Salt Lake City, Utah, USA
| | - Laurie A King
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
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8
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Hafer JF, Vitali R, Gurchiek R, Curtze C, Shull P, Cain SM. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. J Biomech 2023; 157:111714. [PMID: 37423120 PMCID: PMC10529245 DOI: 10.1016/j.jbiomech.2023.111714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.
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Affiliation(s)
- Jocelyn F Hafer
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States.
| | - Rachel Vitali
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | - Reed Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, United States
| | - Peter Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
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9
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Presley BM, Sklar JC, Hazelwood SJ, Berg-Johansen B, Klisch SM. Balance Assessment Using a Smartwatch Inertial Measurement Unit with Principal Component Analysis for Anatomical Calibration. SENSORS (BASEL, SWITZERLAND) 2023; 23:4585. [PMID: 37430500 DOI: 10.3390/s23104585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 07/12/2023]
Abstract
Balance assessment, or posturography, tracks and prevents health complications for a variety of groups with balance impairment, including the elderly population and patients with traumatic brain injury. Wearables can revolutionize state-of-the-art posturography methods, which have recently shifted focus to clinical validation of strictly positioned inertial measurement units (IMUs) as replacements for force-plate systems. Yet, modern anatomical calibration (i.e., sensor-to-segment alignment) methods have not been utilized in inertial-based posturography studies. Functional calibration methods can replace the need for strict placement of inertial measurement units, which may be tedious or confusing for certain users. In this study, balance-related metrics from a smartwatch IMU were tested against a strictly placed IMU after using a functional calibration method. The smartwatch and strictly placed IMUs were strongly correlated in clinically relevant posturography scores (r = 0.861-0.970, p < 0.001). Additionally, the smartwatch was able to detect significant variance (p < 0.001) between pose-type scores from the mediolateral (ML) acceleration data and anterior-posterior (AP) rotation data. With this calibration method, a large problem with inertial-based posturography has been addressed, and wearable, "at-home" balance-assessment technology is within possibility.
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Affiliation(s)
- Benjamin M Presley
- Mechanical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Jeffrey C Sklar
- Statistics, College of Science and Mathematics, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Scott J Hazelwood
- Mechanical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Biomedical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Britta Berg-Johansen
- Biomedical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Stephen M Klisch
- Mechanical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Biomedical Engineering, College of Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Ortigas Vásquez A, Maas A, List R, Schütz P, Taylor WR, Grupp TM. A Framework for Analytical Validation of Inertial-Sensor-Based Knee Kinematics Using a Six-Degrees-of-Freedom Joint Simulator. SENSORS (BASEL, SWITZERLAND) 2022; 23:348. [PMID: 36616945 PMCID: PMC9824828 DOI: 10.3390/s23010348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 06/16/2023]
Abstract
The success of kinematic analysis that relies on inertial measurement units (IMUs) heavily depends on the performance of the underlying algorithms. Quantifying the level of uncertainty associated with the models and approximations implemented within these algorithms, without the complication of soft-tissue artefact, is therefore critical. To this end, this study aimed to assess the rotational errors associated with controlled movements. Here, data of six total knee arthroplasty patients from a previously published fluoroscopy study were used to simulate realistic kinematics of daily activities using IMUs mounted to a six-degrees-of-freedom joint simulator. A model-based method involving extended Kalman filtering to derive rotational kinematics from inertial measurements was tested and compared against the ground truth simulator values. The algorithm demonstrated excellent accuracy (root-mean-square error ≤0.9°, maximum absolute error ≤3.2°) in estimating three-dimensional rotational knee kinematics during level walking. Although maximum absolute errors linked to stair descent and sit-to-stand-to-sit rose to 5.2° and 10.8°, respectively, root-mean-square errors peaked at 1.9° and 7.5°. This study hereby describes an accurate framework for evaluating the suitability of the underlying kinematic models and assumptions of an IMU-based motion analysis system, facilitating the future validation of analogous tools.
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Affiliation(s)
- Ariana Ortigas Vásquez
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
| | - Allan Maas
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
| | - Renate List
- Human Performance Lab., Schulthess Clinic, 8008 Zurich, Switzerland
| | - Pascal Schütz
- Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland
| | - William R. Taylor
- Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland
| | - Thomas M. Grupp
- Research and Development, Aesculap AG, 78532 Tuttlingen, Germany
- Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich (MUM), Campus Grosshadern, Ludwig Maximilians University Munich, 81377 Munich, Germany
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11
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Laidig D, Weygers I, Seel T. Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints. SENSORS (BASEL, SWITZERLAND) 2022; 22:9850. [PMID: 36560219 PMCID: PMC9785932 DOI: 10.3390/s22249850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.
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Affiliation(s)
- Daniel Laidig
- Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany
| | - Ive Weygers
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Thomas Seel
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
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Jabri S, Carender W, Wiens J, Sienko KH. Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection. J Neuroeng Rehabil 2022; 19:132. [PMID: 36456966 PMCID: PMC9713134 DOI: 10.1186/s12984-022-01099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Vestibular deficits can impair an individual's ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers. METHODS Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task. RESULTS Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed. CONCLUSIONS These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.
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Affiliation(s)
- Safa Jabri
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
| | - Wendy Carender
- grid.412590.b0000 0000 9081 2336Department of Otolaryngology, Michigan Medicine, Ann Arbor, MI 48109 USA
| | - Jenna Wiens
- grid.214458.e0000000086837370Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - Kathleen H. Sienko
- grid.214458.e0000000086837370Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA
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13
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Jiang C, Yang Y, Mao H, Yang D, Wang W. Effects of Dynamic IMU-to-Segment Misalignment Error on 3-DOF Knee Angle Estimation in Walking and Running. SENSORS (BASEL, SWITZERLAND) 2022; 22:9009. [PMID: 36433608 PMCID: PMC9697725 DOI: 10.3390/s22229009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/13/2022] [Accepted: 11/18/2022] [Indexed: 06/16/2023]
Abstract
The inertial measurement unit (IMU)-to-segment (I2S) alignment is an important part of IMU-based joint angle estimation, and the accurate estimation of the three degree of freedom (3-DOF) knee angle can provide practical support for the evaluation of motions. In this paper, we introduce a dynamic weight particle swarm optimization (DPSO) algorithm with crossover factor based on the joint constraint to obtain the dynamic alignment vectors of I2S, and use them to perform the quaternion-based 3-DOF knee angle estimation algorithm. The optimization algorithm and the joint angle estimation algorithm were evaluated by comparing with the optical motion capture system. The range of 3-DOF knee angle root mean square errors (RMSEs) is 1.6°-5.9° during different motions. Furthermore, we also set up experiments of human walking (3 km/h), jogging (6 km/h) and ordinary running (9 km/h) to investigate the effects of dynamic I2S misalignment errors on 3-DOF knee angle estimation during different motions by artificially adding errors to I2S alignment parameters. The results showed differences in the effects of I2S misalignment errors on the estimation of knee abduction, internal rotation and flexion, which indicate the differences in knee joint kinematics among different motions. The IMU to thigh misalignment error has the greatest effect on the estimation of knee internal rotation. The effect of IMU to thigh misalignment error on the estimation of knee abduction angle becomes smaller and then larger during the two processes of switching from walking to jogging and then speeding up to ordinary running. The effect of IMU to shank misalignment error on the estimation of knee flexion angle is numerically the largest, while the standard deviation (SD) is the smallest. This study can provide support for future research on the accuracy of 3-DOF knee angle estimation during different motions.
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Affiliation(s)
- Chao Jiang
- Biomedical Engineering Research Center, School of Bioinformatics, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing 400065, China
| | - Yan Yang
- School of Automation, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing 400065, China
| | - Huayun Mao
- School of Automation, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing 400065, China
| | - Dewei Yang
- School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing 400065, China
| | - Wei Wang
- Biomedical Engineering Research Center, School of Bioinformatics, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing 400065, China
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14
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. SENSORS (BASEL, SWITZERLAND) 2022; 22:8398. [PMID: 36366096 PMCID: PMC9654083 DOI: 10.3390/s22218398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.
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Affiliation(s)
- Michael V. Potter
- Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USA
| | - Stephen M. Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Reed D. Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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15
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Schall MC, Chen H, Cavuoto L. Wearable inertial sensors for objective kinematic assessments: A brief overview. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2022; 19:501-508. [PMID: 35853137 DOI: 10.1080/15459624.2022.2100407] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Mark C Schall
- Department of Industrial and Systems Engineering, Auburn University, Auburn, Alabama
| | - Howard Chen
- Department of Mechanical Engineering, Auburn University, Auburn, Alabama
| | - Lora Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York
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16
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Carcreff L, Payen G, Grouvel G, Massé F, Armand S. Three-Dimensional Lower-Limb Kinematics from Accelerometers and Gyroscopes with Simple and Minimal Functional Calibration Tasks: Validation on Asymptomatic Participants. SENSORS 2022; 22:s22155657. [PMID: 35957218 PMCID: PMC9370908 DOI: 10.3390/s22155657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/15/2022] [Accepted: 07/26/2022] [Indexed: 02/04/2023]
Abstract
The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use.
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Affiliation(s)
- Lena Carcreff
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Nantes Université, Movement-Interactions-Performance, MIP, UR4334, F-44000 Nantes, France
- Correspondence:
| | - Gabriel Payen
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Gautier Grouvel
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
| | - Fabien Massé
- Gait Up SA, 1020 Renens, Switzerland; (G.P.); (F.M.)
| | - Stéphane Armand
- Kinesiology Laboratory, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland; (G.G.); (S.A.)
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17
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A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System. SENSORS 2022; 22:s22134898. [PMID: 35808393 PMCID: PMC9269534 DOI: 10.3390/s22134898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022]
Abstract
This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.
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18
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Suzurikawa J, Kurokawa S, Sugiyama H, Hase K. Estimation of Steering and Throttle Angles of a Motorized Mobility Scooter with Inertial Measurement Units for Continuous Quantification of Driving Operation. SENSORS (BASEL, SWITZERLAND) 2022; 22:3161. [PMID: 35590851 PMCID: PMC9103857 DOI: 10.3390/s22093161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/15/2022] [Accepted: 04/17/2022] [Indexed: 02/04/2023]
Abstract
With the growing demand from elderly persons for alternative mobility solutions, motorized mobility scooters (MMSs) have been gaining importance as an essential assistive technology to aid independent living in local communities. The increased use of MMSs, however, has raised safety issues during driving and magnified the necessity to evaluate and improve user driving skills. This study is intended to develop a novel quantitative monitoring method for MMS driving operation using inertial measurement units (IMUs). The proposed method used coordinate transformations around the rotational axes of the steering wheel and the throttle lever to estimate the steering and throttle operating angles based on gravitational accelerations measured by IMUs. Consequently, these operating angles can be monitored simply using an IMU attached to the throttle lever. Validation experiments with a test MMS in the stationary state confirmed the consistency of the proposed coordinate transformation with the MMS's geometrical structure. The driving test also demonstrated that the operating angles were estimated correctly on various terrains and that the effects of terrain inclination were compensated using an additional IMU attached to the scooter body. This method will be applicable to the quantitative monitoring of driving behavior and act as a complementary tool for the existing skills' evaluation methods.
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Affiliation(s)
- Jun Suzurikawa
- Department of Assistive Technology, Research Institute, National Rehabilitation Center for Persons with Disabilities, 4-1 Namiki, Tokorozawa-shi 359-8555, Japan; (S.K.); (H.S.)
| | - Shunsuke Kurokawa
- Department of Assistive Technology, Research Institute, National Rehabilitation Center for Persons with Disabilities, 4-1 Namiki, Tokorozawa-shi 359-8555, Japan; (S.K.); (H.S.)
- Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi 192-0397, Japan;
| | - Haruki Sugiyama
- Department of Assistive Technology, Research Institute, National Rehabilitation Center for Persons with Disabilities, 4-1 Namiki, Tokorozawa-shi 359-8555, Japan; (S.K.); (H.S.)
- Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi 192-0397, Japan;
| | - Kazunori Hase
- Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi 192-0397, Japan;
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19
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Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking. SENSORS 2022; 22:s22072544. [PMID: 35408159 PMCID: PMC9003309 DOI: 10.3390/s22072544] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 12/22/2022]
Abstract
Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).
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20
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Lee CJ, Lee JK. Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review. SENSORS 2022; 22:s22072507. [PMID: 35408121 PMCID: PMC9002742 DOI: 10.3390/s22072507] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.
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Affiliation(s)
- Chang June Lee
- Department of Mechanical Engineering, Hankyong National University, Anseong 17579, Korea;
| | - Jung Keun Lee
- School of ICT, Robotics & Mechanical Engineering, Hankyong National University, Anseong 17579, Korea
- Correspondence: ; Tel.: +82-31-670-5112
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21
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Simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity for gait analysis using IMUs on the feet of individuals with heterogeneous motor disabilities. J Biomech 2022; 135:111055. [DOI: 10.1016/j.jbiomech.2022.111055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/28/2022] [Accepted: 03/15/2022] [Indexed: 11/18/2022]
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22
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Robbins K, Truong D, Appelhoff S, Delorme A, Makeig S. Capturing the nature of events and event context using hierarchical event descriptors (HED). Neuroimage 2021; 245:118766. [PMID: 34848298 PMCID: PMC8925904 DOI: 10.1016/j.neuroimage.2021.118766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/27/2021] [Accepted: 11/26/2021] [Indexed: 10/25/2022] Open
Abstract
Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645).
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Affiliation(s)
- Kay Robbins
- Department of Computer Science, University of Texas San Antonio San Antonio, TX, United States.
| | - Dung Truong
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, 92903-0559, United States
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, 92903-0559, United States; Paul Sabatier University in Toulouse, Toulouse, France
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, 92903-0559, United States
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23
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In-vitro validation of inertial-sensor-to-bone alignment. J Biomech 2021; 128:110781. [PMID: 34628197 DOI: 10.1016/j.jbiomech.2021.110781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 11/24/2022]
Abstract
A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint.
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24
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Parrington L, King LA, Weightman MM, Hoppes CW, Lester ME, Dibble LE, Fino PC. Between-site equivalence of turning speed assessments using inertial measurement units. Gait Posture 2021; 90:245-251. [PMID: 34530311 DOI: 10.1016/j.gaitpost.2021.09.164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/31/2021] [Accepted: 09/03/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Turning is a component of gait that requires planning for movement of multiple body segments and the sophisticated integration of sensory information from the vestibular, visual, and somatosensory systems. These aspects of turning have led to growing interest to quantify turning in clinical populations to characterize deficits or identify disease progression. However, turning may be affected by environmental differences, and the degree to which turning assessments are comparable across research or clinical sites has not yet been evaluated. RESEARCH QUESTION The aim of this study was to determine the extent to which peak turning speeds are equivalent between two sites for a variety of mobility tasks. METHODS Data were collected at two different sites using separate healthy young adult participants (n = 47 participants total), but recruited using identical inclusion and exclusion criteria. Participants at each site completed three turning tasks: a one-minute walk (1 MW) along a six-meter walkway, a modified Illinois Agility Test (mIAT), and a custom clinical turning course (CCTC). Peak yaw turning speeds were extracted from wearable inertial sensors on the head, trunk, and pelvis. Between-site differences and two one-sided tests (TOST) were used to determine equivalence between sites, based on a minimum effect size reported between individuals with mild traumatic brain injury and healthy control subjects. RESULTS No outcomes were different between sites, and equivalence was determined for 6/21 of the outcomes. These findings suggest that some turning tasks and outcome measures may be better suited for multi-site studies. The equivalence results are also dependent on the minimum effect size of interest; nearly all outcomes were equivalent across sites when larger minimum effect sizes of interest were used. SIGNIFICANCE Together, these results suggest some tasks and outcome measures may be better suited for multi-site studies and literature-based comparisons.
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Affiliation(s)
- Lucy Parrington
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Laurie A King
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | | | - Carrie W Hoppes
- Army-Baylor University Doctoral Program in Physical Therapy, Fort Sam Houston, TX, United States
| | - Mark E Lester
- Army-Baylor University Doctoral Program in Physical Therapy, Fort Sam Houston, TX, United States; Department of Physical Therapy, Texas State University, Round Rock, TX, United States
| | - Leland E Dibble
- Department of Physical Therapy & Athletic Training, University of Utah, Salt Lake City, UT, United States
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT, United States.
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25
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Weygers I, Kok M, Seel T, Shah D, Taylan O, Scheys L, Hallez H, Claeys K. Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint. Sci Data 2021; 8:208. [PMID: 34354084 PMCID: PMC8342472 DOI: 10.1038/s41597-021-00995-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 07/09/2021] [Indexed: 11/12/2022] Open
Abstract
Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
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Affiliation(s)
- Ive Weygers
- KU Leuven campus Bruges, Department of Rehabilitation Sciences, Bruges, 8200, Belgium.
| | - Manon Kok
- TU Delft, Department of Mechanical, Maritime and Materials Engineering, Delft, 2628 CD, the Netherlands
| | - Thomas Seel
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Artificial Intelligence in Biomedical Engineering, Erlangen, 91054, Germany
| | - Darshan Shah
- KU Leuven, Department of Development and Regeneration, Institute for Orthopaedic Research and Training (IORT), Leuven, 3000, Belgium
| | - Orçun Taylan
- KU Leuven, Department of Development and Regeneration, Institute for Orthopaedic Research and Training (IORT), Leuven, 3000, Belgium
| | - Lennart Scheys
- KU Leuven, Department of Development and Regeneration, Institute for Orthopaedic Research and Training (IORT), Leuven, 3000, Belgium
- University Hospitals Leuven, Division of Orthopaedics, Leuven, 3000, Belgium
| | - Hans Hallez
- KU Leuven campus Bruges, Department of Computer Sciences, Bruges, 8200, Belgium
| | - Kurt Claeys
- KU Leuven campus Bruges, Department of Rehabilitation Sciences, Bruges, 8200, Belgium
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26
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Mundt M, Johnson WR, Potthast W, Markert B, Mian A, Alderson J. A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units. SENSORS 2021; 21:s21134535. [PMID: 34283080 PMCID: PMC8271391 DOI: 10.3390/s21134535] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 11/23/2022]
Abstract
The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.
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Affiliation(s)
- Marion Mundt
- Minderoo Tech and Policy Lab, UWA Law School, The University of Western Australia, Crawley 6009, Australia;
- Correspondence:
| | | | - Wolfgang Potthast
- Institute of Biomechanics and Orthopeadics, German Sport University Cologne, 50933 Cologne, Germany;
| | - Bernd Markert
- Institute of General Mechanics, RWTH Aachen University, 52062 Aachen, Germany;
| | - Ajmal Mian
- School of Computer Science and Software Engineering, The University of Western Australia, Crawley 6009, Australia;
| | - Jacqueline Alderson
- Minderoo Tech and Policy Lab, UWA Law School, The University of Western Australia, Crawley 6009, Australia;
- Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland 1010, New Zealand
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27
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Asghari M, Ehsani H, Cohen A, Tax T, Mohler J, Toosizadeh N. Nonlinear analysis of the movement variability structure can detect aging-related differences among cognitively healthy individuals. Hum Mov Sci 2021; 78:102807. [PMID: 34023753 DOI: 10.1016/j.humov.2021.102807] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 11/20/2022]
Abstract
Studying the dynamics of nonlinear systems can provide additional information about the variability structure of the system. Within the current study, we examined the application of regularity and local stability measures to capture motor function alterations due to dual-tasking using a previously validated upper-extremity function (UEF). We targeted young (ages 18 and 30 years) and older adults (65 years or older) with normal cognition based on clinical screening. UEF involved repetitive elbow flexion without counting (ST) and while counting backward by one (DT1) or three (DT3). We measured the regularity (measured by sample entropy (SE)), local stability (measured by the largest Lyapunov exponent (LyE)), as well as conventional peak-dependent variability measures (coefficient of variation of kinematics parameters) to capture motor dynamic alterations due to dual-tasking. Within both groups, only SE showed significant differences between all pairs of UEF condition comparisons, even ST vs DT1 (p = 0.007, effect size = 0.507), for which no peak-dependent parameter showed significant difference. Among all measures, the only parameter that showed a significant difference between young and older adults was LyE (p < 0.001, effect size = 0.453). Current findings highlight the potential of nonlinear analysis to detect aging-related alterations among cognitively healthy participants.
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Affiliation(s)
- Mehran Asghari
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.
| | - Hossein Ehsani
- Department of Kinesiology, University of Maryland College Park, Maryland, MD, USA
| | - Audrey Cohen
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Talia Tax
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Jane Mohler
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Nima Toosizadeh
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA; Arizona Center on Aging (ACOA), Department of Medicine, University of Arizona, College of Medicine, Tucson, AZ, USA; Division of Geriatrics, General Internal Medicine and Palliative Medicine, Department of Medicine, University of Arizona, Tucson, AZ, USA
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28
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Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model. PLoS One 2021; 16:e0249577. [PMID: 33878142 PMCID: PMC8057618 DOI: 10.1371/journal.pone.0249577] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/20/2021] [Indexed: 11/19/2022] Open
Abstract
Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.
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Affiliation(s)
- Michael V. Potter
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- * E-mail:
| | - Stephen M. Cain
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Lauro V. Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
| | - Reed D. Gurchiek
- M-Sense Research Group, University of Vermont, Burlington, VT, United States of America
| | - Ryan S. McGinnis
- M-Sense Research Group, University of Vermont, Burlington, VT, United States of America
| | - Noel C. Perkins
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States of America
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29
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McGrath T, Stirling L. Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6887. [PMID: 33276492 PMCID: PMC7729748 DOI: 10.3390/s20236887] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/17/2020] [Accepted: 11/28/2020] [Indexed: 11/17/2022]
Abstract
Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.
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Affiliation(s)
- Timothy McGrath
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Leia Stirling
- Industrial and Operations Engineering, Robotics Institute, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109, USA;
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