CoChat
Sign Up
Cited by 392Muscle activation and electromyography studiesLower Extremity Biomechanics and PathologiesHuman Motion and Animation

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

A real-time system for biomechanical analysis of human movement and muscle function

Open access
van den Bogert, Antonie J., Geijtenbeek, Thomas, Even-Zohar, Oshri, Steenbrink, Frans, Hardin, Elizabeth C.·2013·Medical & Biological Engineering & Computing
DOI 10.1007/s11517-013-1076-zPMC PMC3751375
Open this paper in CoChat — free accountOpen PDF

Abstract

Mechanical analysis of movement plays an important role in clinical management of neurological and orthopedic conditions. There has been increasing interest in performing movement analysis in real-time, to provide immediate feedback to both therapist and patient. However, such work to date has been limited to single-joint kinematics and kinetics. Here we present a software system, named human body model (HBM), to compute joint kinematics and kinetics for a full body model with 44 degrees of freedom, in real-time, and to estimate length changes and forces in 300 muscle elements. HBM was used to analyze lower extremity function during gait in 12 able-bodied subjects. Processing speed exceeded 120 samples per second on standard PC hardware. Joint angles and moments were consistent within the group, and consistent with other studies in the literature. Estimated muscle force patterns were consistent among subjects and agreed qualitatively with electromyography, to the extent that can be expected from a biomechanical model. The real-time analysis was integrated into the D-Flow system for development of custom real-time feedback applications and into the gait real-time analysis interactive lab system for gait analysis and gait retraining.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s11517-013-1076-z

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Data flow within the human body model (HBM)
Fig. 1 Data flow within the human body model (HBM)CC BY · PubMed Central
Fig. 2 Screen image from the D-Flow system. The distributed rendering system (DRS) window is normally displayed on a large projection screen for interaction with patient and therapist. Muscle activation is visualized as a change in muscle color. The window on the bottom right is the console for application development, showing the data flow editor and the connection editor. A simple application is shown, in which estimated quadriceps forces are used to control a virtual ball, such that upward motion responds to total force, and horizontal motion responds to asymmetry. This simple application would help a patient train to increase their quadriceps activation while maintaining left–right symmetry. The window on the left is the user interface for the HBM
Fig. 2 Screen image from the D-Flow system. The distributed rendering system (DRS) window is normally displayed on a large projection screen for interaction with patient and therapist. Muscle activation is visualized as a change in muscle color. The window on the bottom right is the console for application development, showing the data flow editor and the connection editor. A simple application is shown, in which estimated quadriceps forces are used to control a virtual ball, such that upward motion responds to total force, and horizontal motion responds to asymmetry. This simple application would help a patient train to increase their quadriceps activation while maintaining left–right symmetry. The window on the left is the user interface for the HBMCC BY · PubMed Central
Fig. 3 Errors in joint angles and muscle forces as a function of the allowed computation time in, respectively, the kinematic solver (1) and the static optimization (6). Results are presented for one representative subject. Arrows indicate the settings that are normally used for real-time analysis
Fig. 3 Errors in joint angles and muscle forces as a function of the allowed computation time in, respectively, the kinematic solver (1) and the static optimization (6). Results are presented for one representative subject. Arrows indicate the settings that are normally used for real-time analysisCC BY · PubMed Central
Fig. 4 The top two rows show lower extremity joint angles and moments obtained with the human body model (HBM) from the 12 able-bodied subjects walking at preferred speed. Each curve represents one subject’s mean gait cycle. The shaded area represents mean and standard deviation from a study on children [24], for those variables that were available. Other joint-related variables are available in HBM, but not shown: joint angular velocity, and joint power generation. The bottom two rows show the inter-segmental loads at the knee, acting on the shank segment, and expressed using the axes of the shank reference frame: X (anterior), Y (lateral), and Z (superior)
Fig. 4 The top two rows show lower extremity joint angles and moments obtained with the human body model (HBM) from the 12 able-bodied subjects walking at preferred speed. Each curve represents one subject’s mean gait cycle. The shaded area represents mean and standard deviation from a study on children [24], for those variables that were available. Other joint-related variables are available in HBM, but not shown: joint angular velocity, and joint power generation. The bottom two rows show the inter-segmental loads at the knee, acting on the shank segment, and expressed using the axes of the shank reference frame: X (anterior), Y (lateral), and Z (superior)CC BY · PubMed Central
Fig. 5 Forces and length changes for 16 muscle groups. EMG patterns from the literature [31] are shown for comparison, with the area under the EMG-time curve shaded. Amplitudes of the EMG patterns were scaled to coincide with the amplitude of estimated muscle force. Other muscle-related variables are available in HBM, but not shown: velocity of length change, power generation, and muscle activation (F/F max)
Fig. 5 Forces and length changes for 16 muscle groups. EMG patterns from the literature [31] are shown for comparison, with the area under the EMG-time curve shaded. Amplitudes of the EMG patterns were scaled to coincide with the amplitude of estimated muscle force. Other muscle-related variables are available in HBM, but not shown: velocity of length change, power generation, and muscle activation (F/F max)CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 32 references

Research works printed but not linked: 2

  1. Rasmussen J, de Zee M, Dahl J, Damsgaard M (2009) Salient properties of a combined minimum-fatigue and and quadratic muscle recruitment criterion. In: Proceedings of the 12th international symposium on computer simulation in biomechanics, Cape Town, South Africa, 2–4 July 2009
  2. van den Bogert AJ, Geijtenbeek T A state space filter for smoothing and differentiation of real-time data with variable sampling rate. Comput Methods Biomech Biomed Eng (in review)

Cited by

Sorted by citations

Items 301–391 of 391

Related

See all

Metadata

Authors
van den Bogert, A. J., Geijtenbeek, T., Even-Zohar, O., Steenbrink, F., & Hardin, E. C.
Affiliations
Louis Stokes Cleveland VA Medical Center (US)
ORCID
Venue
Medical & Biological Engineering & Computing
Publisher
Springer Science and Business Media LLC
Published
2013-07-25
Volume
51
Issue
10
Pages
1069-1077
ISSN
  • 0140-0118 (ISSN-L)
  • 1741-0444
DOI
10.1007/s11517-013-1076-z
PMC
PMC3751375
OpenAlex
W2050578981
Cited by
392
References
32
Citations by year
2026: 20 · 2025: 31 · 2024: 30 · 2023: 49 · 2022: 38 · 2021: 42 · 2020: 34 · 2019: 35 · 2018: 32 · 2017: 35 · 2016: 17 · 2015: 17 · 2014: 10 · 2013: 2
FWCI
8.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical & Biological Engineering & Computing (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EngagedScholarship @ Cleveland State University (Cleveland State University) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
Keywords
human body model, joint kinematics, joint kinetics, muscle force estimation, gait analysis, lower extremity, real-time feedback, gait retraining, electromyography validation
MeSH terms
Adult, Biomechanical Phenomena, Computer Simulation, Computer Systems, Female, Gait, Humans, Male, Models, Biological, Movement, Software, User-Computer Interface, Range of Motion, Articular, Muscle, Skeletal

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review