Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping-Are Bone Geometry Predictors Helpful?

Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics. Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated. Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°. Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance

Editorial commentary : assistive technologies for hip arthroscopic cam resection will improve diagnostic and surgical accuracy : desperately needed and here to stay

Hip arthroscopy is technically demanding and presents a steep learning curve. Joint access and maneuverability of surgical tools are impeded by a large soft-tissue envelope. Furthermore, cam resection is challenging owing to the small size of the lesion and the difficulty in delineating what is normal and where the cam starts. Thus, the number of incomplete resections is high and represents the bulk of indications for revision hip arthroscopy. The search for assistive technologies to improve on diagnostics and surgical accuracy is consequently substantial and unquestionably needed. Intraoperative feedback will improve our resection accuracy while decreasing the learning efforts of both expert and novice surgeons

Handle with care : the anterior hip capsule plays a key role in daily hip performance

Background: Passive energy storage and return has long been recognized as one of the central mechanisms for minimizing the energy cost needed for terrestrial locomotion. Although the iliofemoral ligament (IFL) is the strongest ligament in the body, its potential role in energy-efficient walking remains unexplored. Purpose: To identify the contribution of the IFL to the amount of work performed by the hip muscles for normal, straight-level walking. Study Design: Controlled laboratory study. Methods: Straight-level walking of 50 healthy and injury-free adults was simulated using the AnyBody Modeling System. For each participant, the bone morphology and soft tissue properties were nonuniformly scaled. The superior and inferior parts of the IFL were represented by 2 springs each, and a linear force-strain relation was defined. A parameter study was conducted to account for the uncertainty surrounding the mechanical properties of the IFL. The work required from the gluteus, quadriceps, iliopsoas, and sartorius with and without inclusion of the IFL was calculated. Analysis of variance with subsequent post hoc paired t test was used to test the significance of IFL presence on the required mechanical work. Results: During walking, the strain in the IFL reached a median of 18.7% (95% CI, 8.0%-26.5%), with the largest values obtained at toe-off. With the IFL undamaged and fully operational, the effort required by the hip flexor muscles was reduced by a median of 54% (99% CI, 45%-62%) for the iliopsoas and by a median of 41% (99% CI, 27%-54%) for the sartorius muscles. The inclusion of the IFL did not significantly alter the work required by the gluteus and the quadriceps. Conclusion: The findings emphasized the key role the IFL plays in hip flexion by working synergistically with the hip musculature

Handle With Care: The Anterior Hip Capsule Plays a Key Role in Daily Hip Performance.

BACKGROUND: Passive energy storage and return has long been recognized as one of the central mechanisms for minimizing the energy cost needed for terrestrial locomotion. Although the iliofemoral ligament (IFL) is the strongest ligament in the body, its potential role in energy-efficient walking remains unexplored. PURPOSE: To identify the contribution of the IFL to the amount of work performed by the hip muscles for normal, straight-level walking. STUDY DESIGN: Controlled laboratory study. METHODS: Straight-level walking of 50 healthy and injury-free adults was simulated using the AnyBody Modeling System. For each participant, the bone morphology and soft tissue properties were nonuniformly scaled. The superior and inferior parts of the IFL were represented by 2 springs each, and a linear force-strain relation was defined. A parameter study was conducted to account for the uncertainty surrounding the mechanical properties of the IFL. The work required from the gluteus, quadriceps, iliopsoas, and sartorius with and without inclusion of the IFL was calculated. Analysis of variance with subsequent post hoc paired t test was used to test the significance of IFL presence on the required mechanical work. RESULTS: During walking, the strain in the IFL reached a median of 18.7% (95% CI, 8.0%-26.5%), with the largest values obtained at toe-off. With the IFL undamaged and fully operational, the effort required by the hip flexor muscles was reduced by a median of 54% (99% CI, 45%-62%) for the iliopsoas and by a median of 41% (99% CI, 27%-54%) for the sartorius muscles. The inclusion of the IFL did not significantly alter the work required by the gluteus and the quadriceps. CONCLUSION: The findings emphasized the key role the IFL plays in hip flexion by working synergistically with the hip musculature. CLINICAL RELEVANCE: The importance of the contribution of the IFL to the hip flexors warrants careful handling and repair of these ligaments in cases of surgery and structural damage

Statistical-shape prediction of lower limb kinematics during cycling, squatting, lunging, and stepping : are bone geometry predictors helpful?

Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics. Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated. Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60 degrees (race cycling) up to 3.10 degrees (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59 degrees (step up) and 6.61 degrees (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42 degrees compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21 degrees. Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance

Statistical kinematic modelling : concepts and model validity

Data reduction techniques are applied to reduce the volume of data while maintaining its integrity. For cyclic motion data, a reliable overview comparing these methods is lacking. Therefore, this study aims to evaluate the features of the different data reduction techniques by applying them to large public data sets. The periodicity of cyclic motion can be exploited by either analysing a single cycle or studying a series of cycles. Analysing single cycles requires a pre-processing step to isolate the amplitude variability. Three different alignment techniques were evaluated, namely Linear length normalisation (LLN), piecewise LLN (PLLN) and continuous registration (CR). CR showed to remove the most phase variation. For the data reduction, three techniques were assessed (i.e., principal component analysis (PCA), principal polynomial analysis (PPA) and multivariate functional PCA (MFPCA)) based on the in- and out-of-sample error, the compactness and the computation time. The differences were found to be minimal. From our results, PPA appeared to be most useful for data compression. Further, we recommend PCA and MFPCA for classification and feature extraction purposes. We suggest the use of PCA when computation time is key and we advise the use of MFPCA when the inclusion of different data sources is desired. In contrast, the analysis of a series of cycles requires a pre-processing step to decompose the series. Further, a regression model was used to compensate for the difference in fundamental frequency. PCA on FC and MFPCA with splines were applied on the frequency compensated curves. Both methods performed as good

Statistical kinematic modelling : concepts and model validity

Data reduction techniques are applied to reduce the volume of data while maintaining its integrity. For cyclic motion data, a reliable overview comparing these methods is lacking. Therefore, this study aims to evaluate the features of the different data reduction techniques by applying them to large public data sets. The periodicity of cyclic motion can be exploited by either analysing a single cycle or studying a series of cycles. Analysing single cycles requires a pre-processing step to isolate the amplitude variability. Three different alignment techniques were evaluated, namely Linear length normalisation (LLN), piecewise LLN (PLLN) and continuous registration (CR). CR showed to remove the most phase variation. For the data reduction, three techniques were assessed (i.e., principal component analysis (PCA), principal polynomial analysis (PPA) and multivariate functional PCA (MFPCA)) based on the in- and out-of-sample error, the compactness and the computation time. The differences were found to be minimal. From our results, PPA appeared to be most useful for data compression. Further, we recommend PCA and MFPCA for classification and feature extraction purposes. We suggest the use of PCA when computation time is key and we advise the use of MFPCA when the inclusion of different data sources is desired. In contrast, the analysis of a series of cycles requires a pre-processing step to decompose the series. Further, a regression model was used to compensate for the difference in fundamental frequency. PCA on FC and MFPCA with splines were applied on the frequency compensated curves. Both methods performed as good

Alignment and parameterization of single cycle motion data

Motion capturing systems produce a large amount of information on the motion of individuals. A growing number of data reduction techniques have been developed to reduce the amount of data while keeping relevant information. An overview that compares and identifies the advantages and disadvantages of these methods on cyclic motion data is, however, lacking. Therefore, this study aims to assess the features of different data reduction techniques by applying them to a large public gait data set. Due to the periodicity of cyclic data, an individual cycle can be isolated and analyzed. The analysis of single cycles requires pre-processing steps to segment and align the individual cycles. The latter is needed to isolate the amplitude variability. Three alignment procedures with different complexity, namely Linear Length Normalization (LLN), Piecewise LNN (PLLN) and Continuous Registration (CR), are assessed based on the amount of resulting variation. Subsequently three data reduction techniques (i.e. Principal Component Analysis (PCA), Principal Polynomial Analysis (PPA) and Multivariate Functional PCA (MFPCA)) are applied to the aligned single gait cycles. The data reduction techniques are evaluated based on the in-sample error, the out-of-sample error, the compactness and the computation time to produce a model. The curves aligned with CR have the lowest remaining variation and thus the lowest amount of remaining phase variation. The differences between the different data reduction techniques appear to be minimal PPA shows to be the most compact and is therefore recommended when compactness is crucial and out-of-sample performance is less essential. The use of MFPCA is advised when one wants to include data from different sources. PCA is suggested when computation time is key. Copyright (C) 2021 The Authors