Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?
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{"title"=>"Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?", "type"=>"journal", "authors"=>[{"first_name"=>"Giordano", "last_name"=>"Valente", "scopus_author_id"=>"53364340400"}, {"first_name"=>"Lorenzo", "last_name"=>"Pitto", "scopus_author_id"=>"54389719700"}, {"first_name"=>"Debora", "last_name"=>"Testi", "scopus_author_id"=>"6701897246"}, {"first_name"=>"Ajay", "last_name"=>"Seth", "scopus_author_id"=>"35957454200"}, {"first_name"=>"Scott L.", "last_name"=>"Delp", "scopus_author_id"=>"7006426955"}, {"first_name"=>"Rita", "last_name"=>"Stagni", "scopus_author_id"=>"55987943600"}, {"first_name"=>"Marco", "last_name"=>"Viceconti", "scopus_author_id"=>"7007048687"}, {"first_name"=>"Fulvia", "last_name"=>"Taddei", "scopus_author_id"=>"7006403985"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84911475324", "sgr"=>"84911475324", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0112625", "pmid"=>"25390896", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"600489321"}, "id"=>"c580b04f-2742-3966-a67f-32b2f9013478", "abstract"=>"Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.", "link"=>"http://www.mendeley.com/research/subjectspecific-musculoskeletal-models-robust-uncertainties-parameter-identification", "reader_count"=>154, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>39, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>46, "Student > Postgraduate"=>5, "Other"=>11, "Student > Master"=>26, "Student > Bachelor"=>7, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>39, "Student > Doctoral Student"=>3, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1790369"], "description"=>"<p>Correlations between hip, knee and ankle joint contact forces and input variables: only statistically significant (p<0.001) R<sup>2</sup> exceeding 0.2 at least in one frame during the stance phase of gait are plotted.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238369, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g007", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_R_2_between_joint_contact_forces_and_input_variables_during_the_stance_phase_of_gait_/1238369", "title"=>"Significant R<sup>2</sup> between joint contact forces and input variables during the stance phase of gait.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790370"], "description"=>"<p>Values were measured through virtual palpation using NMSBuilder by 5 operators in 3 trials each. X, Y and Z indicate antero-posterior, cranio-caudal and medio-lateral directions of the body reference frames, respectively. Body landmark acronyms indicate: sacrum (SACRUM), right anterior superior iliac spine (RASIS), right posterior superior iliac spine (RPSIS), left anterior superior iliac spine (LASIS), left posterior superior iliac spine (LPSIS), right great trochanter (RGT), right medial epicondyle (RME), right lateral epicondyle (RLE), right hip center (RHC), right head of fibula (RHF), right tibial tuberosity (RTT), right lateral tibial condyle (RLC), right medial tibial condyle (RMC), right medial malleolus (RMM), right lateral malleolus (RLM), right calcaneus (RCA), right first metatarsus (RFM), right second metatarsus (RSM), right fifth metatarsus (RVM), right superior plantar aspect of calcaneus (RPAI), right inferior plantar aspect of calcaneus (RPAII).</p><p>Standard deviations of the body landmark positions measured experimentally.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238370, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.t001", "stats"=>{"downloads"=>5, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Standard_deviations_of_the_body_landmark_positions_measured_experimentally_/1238370", "title"=>"Standard deviations of the body landmark positions measured experimentally.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790371"], "description"=>"<p>Standard deviations and ranges of the magnitudes of joint contact forces and the major muscle forces are reported as mean and maximum values across the stance phase of gait.</p><p>Variability in joint contact and muscle forces.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238371, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.t002", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_joint_contact_and_muscle_forces_/1238371", "title"=>"Variability in joint contact and muscle forces.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790372"], "description"=>"<div><p>Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.</p></div>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238372, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625", "stats"=>{"downloads"=>20, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Are_Subject_Specific_Musculoskeletal_Models_Robust_to_the_Uncertainties_in_Parameter_Identification_/1238372", "title"=>"Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790356"], "description"=>"<p>The modeling software systems were applied to study the sensitivity of model predictions to the uncertainties in parameter identification. Lower-body MRI and gait analysis data were acquired for a healthy subject. NMSBuilder was used to create the baseline subject-specific model leveraging OpenSim. The Probabilistic Musculoskeletal Modeling module (PMM) was used to create probabilistic simulations of gait through a Monte-Carlo analysis, by interfacing Matlab and OpenSim. The input variables were perturbed according to their uncertainties, and the corresponding OpenSim models were created that included the different sets of perturbed variables. Using each model and the recorded gait analysis data, simulations of gait were run to calculate the stochastic output variables.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238356, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g001", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_of_subject_specific_musculoskeletal_modeling_/1238356", "title"=>"Workflow of subject-specific musculoskeletal modeling.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790358"], "description"=>"<p>To analyze the sensitivity of model predictions to the uncertainties in parameter values, three categories of stochastic input variables were identified (for a total of 312 input variables): body landmark positions (affecting position and orientation of body reference frames and joints, inertial tensors and joint kinematics), musculotendon geometry (position of origin/insertion and via points defining musculotendon paths and affecting muscle moment arms) and maximum muscle tension (affecting maximum force-generating capacity of the muscles). Each variable was assumed as normally or uniformly distributed, and a Latin Hypercube Sampling strategy was applied to efficiently sample the variables from their distribution.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238358, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_statistical_perturbation_of_the_input_variables_/1238358", "title"=>"Schematic of statistical perturbation of the input variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790359"], "description"=>"<p>Bands represent mean values ±1 standard deviation (in degrees) during the stance phase of gait.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238359, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_joint_angles_due_to_the_perturbation_of_model_variables_/1238359", "title"=>"Variability in joint angles due to the perturbation of model variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790360"], "description"=>"<p>Bands represent mean values ±1 standard deviation (in Nm) during the stance phase of gait.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238360, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g004", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_net_joint_moments_due_to_the_perturbation_of_model_variables_/1238360", "title"=>"Variability in net joint moments due to the perturbation of model variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790361"], "description"=>"<p>Bands represent mean values ±1 standard deviation (in BW) during the stance phase of gait.</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238361, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g005", "stats"=>{"downloads"=>2, "page_views"=>49, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_joint_contact_forces_due_to_the_perturbation_of_model_variables_/1238361", "title"=>"Variability in joint contact forces due to the perturbation of model variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1790366"], "description"=>"<p>Bands represent mean values ±1 standard deviation (in BW) during the stance phase of gait. Muscles shown are: medial (<i>Med Gas</i>) and lateral (<i>Lat Gas</i>) gastrocnemius, soleus, tibialis anterior (<i>Tib Ant</i>), gluteus medius anterior (<i>GMedA</i>), middle (<i>GMedM</i>) and posterior (<i>GMedP</i>), gluteus maximus anterior (<i>GMaxA</i>), tensor fascia latae (<i>TFL</i>), psoas, iliacus, semimembranosus (<i>Semimem</i>), rectus femoris (<i>Rec Fem</i>), vastus medialis (<i>Vas Med</i>), lateralis (Vas Lat) and intermedius (<i>Vas Int</i>).</p>", "links"=>[], "tags"=>["analysis perturbing model parameters", "study musculoskeletal disorders", "muscle tension", "uncertainty", "input variables", "body landmark positions", "parameter identification", "contact forces", "84 musculotendon units", "model creation", "model predictions"], "article_id"=>1238366, "categories"=>["Biological Sciences"], "users"=>["Giordano Valente", "Lorenzo Pitto", "Debora Testi", "Ajay Seth", "Scott L. Delp", "Rita Stagni", "Marco Viceconti", "Fulvia Taddei"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112625.g006", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_the_major_muscle_forces_due_to_the_perturbation_of_model_variables_/1238366", "title"=>"Variability in the major muscle forces due to the perturbation of model variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-12 03:29:00"}

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Relative Metric

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