The effects of anatomical errors on shoulder kinematics computed using multi-body models

Lavaill, Maxence; Martelli, Saulo; Gilliland, Luke; Gupta, Ashish; Kerr, Graham; Pivonka, Peter

doi:10.1007/s10237-022-01606-0

Cited by 8 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More generally, a lack of model personalisation (i.e. bone geometry and muscle paths) [33] may be responsible for discrepancies in joint forces [44].…”

Section: Discussionmentioning

confidence: 99%

“…The clavicle, ulna, radius and hand segments of the generic OpenSim model were longitudinally scaled to match the participant's anthropometry and landmark positions during the first 0.5 seconds of the abduction task when the participant was standing still. The thorax, scapula and humerus segments were scaled in three orthogonal directions to account for their more complex geometry [33]. For the abduction task with weight, the position of the hand's centre of mass was shifted anteriorly by 3 cm and the hand's weight was increased by 2.4 kg to mimic the changed dynamic properties of the hand-weight system.…”

Section: Shoulder Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Lavaill,

Pizzolato,

Bolsterlee

et al. 2024

Multibody Syst Dyn

Self Cite

View full text Add to dashboard Cite

Shoulder muscle forces estimated via modelling are typically indirectly validated against measurements of glenohumeral joint reaction forces (GHJ-RF). This validation study benchmarks the outcomes of several muscle recruitment strategies against public GHJ-RF measurements. Public kinematics, electromyography, and GHJ-RF data from a selected male participant executing a 2.4 kg weight shoulder abduction task up to 92° GHJ elevation were obtained. The Delft Shoulder and Elbow Model was scaled to the participant. Muscle recruitment was solved by 1) minimising muscle activations squared (SO), 2) accounting for dynamic muscle properties (CMC) and 3) constraining muscle excitations to corresponding surface electromyography measurements (CEINMS). Moreover, the spectrum of admissible GHJ-RF in the model was determined via Markov-chain Monte Carlo stochastic sampling. The experimental GHJ-RF was compared to the resultant GHJ-RF of the different muscle recruitment strategies as well as the admissible stochastic range. From 21 to 40 degrees of humeral elevation, the experimental measurement of the GHJ-RF was outside the admissible range of the model (21 to 659% of body weight (%BW)). Joint force RMSE was between 21 (SO) and 24%BW (CEINMS). At high elevation angles, CMC (11%BW) and CEINMS (14%BW) performed better than SO (25%BW). A guide has been proposed to best select muscle recruitment strategies. At high elevation angles, CMC and CEINMS were the two most accurate methods in terms of predicted GHJ-RF. SO performed best at low elevation angles. In addition, stochastic muscle sampling highlighted the lack of consistency between the model and experimental data at low elevation angles.

show abstract

“…More generally, a lack of model personalisation (i.e. bone geometry and muscle paths) [33] may be responsible for discrepancies in joint forces [44].…”

Section: Discussionmentioning

confidence: 99%

Section: Shoulder Modellingmentioning

confidence: 99%

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Lavaill,

Pizzolato,

Bolsterlee

et al. 2024

Multibody Syst Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…The clavicle, ulna, radius and hand segments of the generic OpenSim model were longitudinally scaled to match the participant's anthropometry and landmark positions during the rst 0.5 seconds of the abduction task when the participant was standing still. The thorax, scapula and humerus segments were scaled in three orthogonal directions to account for their more complex geometry [33]. For the abduction task with weight, the position of the hand's centre of mass was shifted anteriorly by 3 cm and the hand's weight was increased by 2.4 kg to mimic the changed dynamic properties of the hand-weight system.…”

Section: Shoulder Modellingmentioning

confidence: 99%

Benchmark and Validation of State-of-the-art Muscle Recruitment Strategies in Shoulder Modelling

Lavaill,

Pizzolato,

Bolsterlee

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Shoulder muscle forces estimated via modelling are typically indirectly validated against measurements of glenohumeral joint reaction forces (GHJ-RF). This validation study benchmarks the outcomes of several muscle recruitment strategies against public GHJ-RF measurements. Public kinematics, electromyography, and GHJ-RF data from a selected male participant executing a 2.4 kg weight shoulder abduction task up to 92° GHJ elevation were obtained. The Delft Shoulder and Elbow Model was scaled to the participant. Muscle recruitment was solved by 1) minimizing muscle activations squared (SO), 2) accounting for dynamic muscle properties (CMC) and 3) constraining muscle excitations to corresponding surface electromyography measurements (CEINMS). Moreover, the spectrum of admissible GHJ-RF in the model was determined via Markov Chain Monte-Carlo stochastic sampling. The experimental GHJ-RF was compared to the resultant GHJ-RF of the different muscle recruitment strategies as well as the admissible stochastic range. Admissible GHJ-RF spanned 21 to 659% of body weight (%BW), excluding the experimental GHJ-RF up to 40 degrees of humeral elevation. Joint force RMSE were between 23 (CMC) and 27%BW (CEINMS). At high elevation angles, CMC (11%BW) and CEINMS (14%BW) performed better than SO (25%BW). A guide has been proposed to best select muscle recruitment strategies. Overall, CMC and CEINMS were the two most accurate methods in terms of predicted GHJ-RF, especially at high elevation angles. SO performed best at low elevation angles. In addition, stochastic muscle sampling provided critical information on the shoulder model capabilities and the consistency between model and experimental data.

show abstract

“…Scaling MKO models as close as possible to functional anatomy based on high-resolution imaging data in clinical applications has been sought as they can be adapted to pathological cases, and a better STA reduction can be ex-pected [21]. Further, a recent study quantified the effects of anatomical errors on shoulder kinematics and showed that participants' anthropometry and sex could indirectly affect kinematic outcomes [76]. For UE models, the joint centers and axes of the AnyBody and Plug-in Gait models must be defined per the International Society of Biomechanics recommendations for kinematics [77] to facilitate standardization and comparison with other published literature.…”

Section: Study Limitationsmentioning

confidence: 99%

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model

Nagaraja

Bergmann

Andersen

et al. 2022

Journal of Biomechanical Engineering

View full text Add to dashboard Cite

Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for non-invasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established models. This study rigorously compares the upper-extremity joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (Multibody Kinematics Optimization) with those estimated by the Vicon Plug-in Gait model (Single-body Kinematics Optimization). Ten subjects performed three trials of Reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all RMSDs <10deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation co-efficients were found between the model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree, albeit with few key differences.

show abstract

The effects of anatomical errors on shoulder kinematics computed using multi-body models

Cited by 8 publications

References 45 publications

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Benchmark and Validation of State-of-the-art Muscle Recruitment Strategies in Shoulder Modelling

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model

Contact Info

Product

Resources

About