Introduction

The quantification and analysis of human movement have always challenged researchers and clinicians over the past decades. While it was a time-consuming and cumbersome task in the early days of motion capturing, technical advancements such as increasing processing power, (wearable) sensor technologies, the ability to collect and process large amount of data, and machine learning approaches are revolutionizing clinical gait analysis (CGA) for both clinical and research settings. This talk aims to offer a brief overview of state-of-the-art methods to quantify human locomotion three-dimensionally with special emphasis on estimating internal (knee) joint loading. The methods covered will encompass standard marker-based CGA which is typically used in clinical settings, to more complex physics-based musculoskeletal simulations. Finally, this talk provides a brief perspective on how the rapid technological development will shape the future of estimating dynamic (knee) joint loading not only in laboratory settings but also in the field.

Content

To quantify human movement three-dimensionally the current technology used in CGA are infrared-based motion capture systems. A motion capture system comprises several synchronized infrared cameras which record the three-dimensional coordinates of small reflective spherical markers that are attached with adhesive tape to the patient's skin above anatomical landmarks. These coordinates, together with a kinematic biomechanical model, then allow the calculation of joint kinematics such as knee joint angles [1]. During laboratory gait analysis, force plates are used to measure the occurring ground reaction forces. When these forces are combined with the kinematic information, an inverse-dynamic approach can be used to estimate joint reaction forces and joint moments (kinetic variables), e.g., the internal knee flexion moment. The medical history and a rigorous clinical examination are then used together with the CGA data to inform the medical diagnosis, assessment, monitoring, or outcome prediction for both orthopedic and neuro-orthopedic patients [2].

Although in-vivo joint loading cannot be measured directly with motion capturing, a commonly used surrogate to estimate internal medial knee joint loading is the frontal knee adduction moment (KAM). The rationale for this surrogate is the association between higher KAM and observed radiographic degeneration of the knee structure and cartilage [3,4]. However, while research has demonstrated that biofeedback-assisted gait retraining can reduce KAM and pain as well as improve function in knee osteoarthritis patients [5,6], it also provides evidence that KAM is only moderately associated with the actual medial knee joint contact force [7]. Thus, while KAM is simple to assess, it only provides a moderately reasonable surrogate for internal knee joint loading.

Physics-based simulations expand upon common CGA methodology by utilizing more complex biomechanical models [8]. Basically, they provide a mathematical description of the human (neuro-) musculoskeletal system. Their main advantage is that they can be used to estimate internal structure loading, such as of the ligaments, or muscle forces, as well as joint contact loading. Thereby allowing a more in-depth understanding of the internal loading during human movement. Further, these models offer the potential of personalization by adjusting, for instance, the muscle geometry or bone-cartilage (knee) morphology of the model based on medical images of a patient [9]. While these physics-based (and personalized) simulations typically need substantial processing power, recent research has demonstrated that they can be run in real-time to offer biofeedback about the medial tibiofemoral contact force or Achilles tendon strain during gait retraining [10,11]. Physics-based simulations are becoming increasingly popular in both research and clinical settings.

Although musculoskeletal simulations can support clinical decision making [8] the need for highly trained personnel and associated hardware and time requirements lead to a reduced accessibility especially in clinical settings [12]. However, the ongoing rapid technological development might offer great potential to make these methods more accessible in the future. One prominent example is Machine and Deep Learning (ML). ML allows the bypassing of several bottleneck calculations in physics-based musculoskeletal modeling workflows. To this end, artificial neuronal networks (ANN) are trained with a large amount of motion capture data and musculoskeletal simulation results. After successful training, they can estimate internal joint or muscle forces in a fraction of the time necessary to run traditional musculoskeletal simulations. Current approaches seem promising and demonstrate high correlations between ML-based predicted internal forces and actual simulation results [13–15]. Recently, it was also shown that these approaches can be based on data from wearable inertial measurements units (IMUs) instead of motion capture data [16,17] thereby offering the potential to bring musculoskeletal simulations into the field. Another very recent approach which utilizes ML is marker-less three-dimensional human pose estimation from either monocular or multi-view settings [18]. This technology allows the estimation of human movement two- and three-dimensionally based simply on video images without the need for markers being attached to the skin. The feasibility of such an approach to measure for example the peak KAM based only on positions obtainable from two-dimensional video analysis was recently demonstrated [19]. Marker-less motion capture systems, to date, are already partly commercially available and allow to estimate three-dimensional gait kinematics with errors of less than 5.5 degrees with the exception of segmental rotations about the longitudinal axis [20]. While error rates of such approaches presently only seem partly satisfying, marker-less motion capturing techniques are currently under heavy development and presumably will become increasingly more powerful over time.

In conclusion, CGA offers various approaches to investigate dynamic (knee) joint loading, and recent research demonstrates that these predominantly laboratory-based measurements and simulations might soon be even available outside of the laboratory with the support of wearable sensors and artificial intelligence. The next step will be to combine these technologies into well-designed and easy to use products that can support clinicians and patients during medical daily routine [21].

References

[1] R. Baker, Measuring walking: a handbook of clinical gait analysis, Mac Keith Press, London, 2013.

[2] R. Baker, Gait analysis methods in rehabilitation, J. NeuroEngineering Rehabil. 3 (2006) 4. https://doi.org/10.1186/1743-0003-3-4.

[3] K.L. Bennell, K.-A. Bowles, Y. Wang, F. Cicuttini, M. Davies-Tuck, R.S. Hinman, Higher dynamic medial knee load predicts greater cartilage loss over 12 months in medial knee osteoarthritis, Ann. Rheum. Dis. 70 (2011) 1770–1774. https://doi.org/10.1136/ard.2010.147082.

[4] E.F. Chehab, J. Favre, J.C. Erhart-Hledik, T.P. Andriacchi, Baseline knee adduction and flexion moments during walking are both associated with 5 year cartilage changes in patients with medial knee osteoarthritis, Osteoarthritis Cartilage. 22 (2014) 1833–1839. https://doi.org/10.1016/j.joca.2014.08.009.

[5] J. Bowd, P. Biggs, C. Holt, G. Whatling, Does Gait Retraining Have the Potential to Reduce Medial Compartmental Loading in Individuals With Knee Osteoarthritis While Not Adversely Affecting the Other Lower Limb Joints? A Systematic Review, Arch. Rehabil. Res. Clin. Transl. 1 (2019) 100022. https://doi.org/10/gnt2s6.

[6] L.C. Pereira, J. Runhaar, J. Favre, B.M. Jolles, S. Bierma-Zeinstra, Association between changes in the knee adduction moment and changes in knee pain and function in response to non-surgical biomechanical interventions for medial knee osteoarthritis: a systematic review, Eur. J. Phys. Rehabil. Med. (2021). https://doi.org/10/gnt2s7.

[7] R.E. Richards, M.S. Andersen, J. Harlaar, J.C. van den Noort, Relationship between knee joint contact forces and external knee joint moments in patients with medial knee osteoarthritis: effects of gait modifications, Osteoarthritis Cartilage. 26 (2018) 1203–1214. https://doi.org/10/gd4jhh.

[8] B.A. Killen, A. Falisse, F. De Groote, I. Jonkers, In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?, Appl. Sci. 10 (2020) 7255. https://doi.org/10/gnkdhk.

[9] C.R. Smith, R.L. Lenhart, J. Kaiser, M.F. Vignos, D.G. Thelen, Influence of Ligament Properties on Tibiofemoral Mechanics in Walking, J. Knee Surg. 29 (2016) 99–106. https://doi.org/10.1055/s-0035-1558858.

[10] C. Pizzolato, M. Reggiani, D.J. Saxby, E. Ceseracciu, L. Modenese, D.G. Lloyd, Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (2017) 1612–1621. https://doi.org/10.1109/TNSRE.2017.2683488.

[11] C. Pizzolato, V.B. Shim, D.G. Lloyd, D. Devaprakash, S.J. Obst, R. Newsham-West, D.F. Graham, T.F. Besier, M.H. Zheng, R.S. Barrett, Targeted Achilles Tendon Training and Rehabilitation Using Personalized and Real-Time Multiscale Models of the Neuromusculoskeletal System, Front. Bioeng. Biotechnol. 8 (2020). https://doi.org/10.3389/fbioe.2020.00878.

[12] W.S. Burton, C.A. Myers, P.J. Rullkoetter, Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living, J. Biomech. 123 (2021) 110439. https://doi.org/10/gkgk44.

[13] L. Rane, Z. Ding, A.H. McGregor, A.M.J. Bull, Deep Learning for Musculoskeletal Force Prediction, Ann. Biomed. Eng. 47 (2019) 778–789. https://doi.org/10/gf3f68.

[14] M.M. Ardestani, Z. Chen, L. Wang, Q. Lian, Y. Liu, J. He, D. Li, Z. Jin, Feed forward artificial neural network to predict contact force at medial knee joint: Application to gait modification, Neurocomputing. 139 (2014) 114–129. https://doi.org/10/gh3884.

[15] T.T. Dao, From deep learning to transfer learning for the prediction of skeletal muscle forces, Med. Biol. Eng. Comput. 57 (2019) 1049–1058. https://doi.org/10/ghhvsp.

[16] M. Mundt, W.R. Johnson, W. Potthast, B. Markert, A. Mian, J. Alderson, A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units, Sensors. 21 (2021) 4535. https://doi.org/10/gmn54p.

[17] A. De Brabandere, J. Emmerzaal, A. Timmermans, I. Jonkers, B. Vanwanseele, J. Davis, A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU, Front. Bioeng. Biotechnol. 8 (2020). https://doi.org/10/ggsk9g.

[18] Y. Desmarais, D. Mottet, P. Slangen, P. Montesinos, A review of 3D human pose estimation algorithms for markerless motion capture, Comput. Vis. Image Underst. 212 (2021) 103275. https://doi.org/10.1016/j.cviu.2021.103275.

[19] M.A. Boswell, S.D. Uhlrich, Ł. Kidziński, K. Thomas, J.A. Kolesar, G.E. Gold, G.S. Beaupre, S.L. Delp, A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis, Osteoarthritis Cartilage. 29 (2021) 346–356. https://doi.org/10/gnt2s8.

[20] R.M. Kanko, E.K. Laende, E.M. Davis, W.S. Selbie, K.J. Deluzio, Concurrent assessment of gait kinematics using marker-based and markerless motion capture, J. Biomech. 127 (2021) 110665. https://doi.org/10.1016/j.jbiomech.2021.110665.

[21 D. Lloyd, The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries, Sports Biomech. 0 (2021) 1–29. https://doi.org/10/gm585p.

Acknowledgments

This work received funding from the Gesellschaft für Forschungsförderung NÖ (Research Promotion Agency of Lower Austria) within the Endowed Professorship for Applied Biomechanics and Rehabilitation Research (SP19-004).

Introduction

For this invited talk, we will focus on two different studies in our research group that have elucidated 1) the role of the healthy meniscus in cartilage load distribution and 2) changes in tibiofemoral cartilage strain in patients with meniscus tears and the relationship of cartilage strain to catabolic biomarkers in the synovial fluid of these joints.

Content

There is currently limited in vivo data in human subjects characterizing the role of the meniscus in load distribution. Therefore, the purpose of this study was to compare the strains experienced in regions of articular cartilage covered by the meniscus to regions of cartilage not covered by the meniscus¹. We hypothesized that in response to walking, tibial cartilage covered by the meniscus would experience lower strains than uncovered tibial cartilage. Magnetic resonance imaging (MRI) of the knee joints of 8 healthy subjects was performed before and after walking on a treadmill. Using MRI-generated 3-dimensional models, cartilage thickness was measured in 4 different regions based on meniscal coverage and compartment: uncovered medial, covered medial, uncovered lateral, and covered lateral. In each compartment, covered cartilage was significantly thinner than uncovered cartilage. After 20 minutes of walking, all regions experienced significant decreases in cartilage thickness. The uncovered medial region experienced significantly more strain than the covered medial region. There was no detectable difference in strain between the covered and uncovered regions in the lateral compartment. In the medial compartment in response to walking, cartilage that is covered by the meniscus experiences lower strains than uncovered cartilage. These findings provide important information on the relationship between in vivo tibial compressive strain responses and meniscal coverage that is critical to understanding normal meniscal function.

Meniscal tears are common injuries and meniscus loss is a risk factor for the development of osteoarthritis. Therefore, the goal of this study was to measure the in vivo tibiofemoral cartilage strain in patients with meniscus tears in relation to catabolic biomarkers in the synovial fluid of these joints². MRI and biplanar fluoroscopy were used to determine the in vivo motion and cartilage contact mechanics of the knee. While performing a quasi-static lunge, subjects with isolated medial meniscus tears were analyzed. The contralateral uninjured knee was used as a control. At the time of surgery, synovial fluid was collected from the injured knee and matrix metalloproteinase (MMP) activity, sulfated glycosaminoglycan, cartilage oligomeric matrix protein, prostaglandin E2, and the collagen type II cleavage biomarker C2C were measured. In the medial compartment, contact strain increased significantly in the injured knees compared to contralateral control knees. In the lateral compartment, the contact strain in the injured knee was significantly increased at 105 degrees of flexion. The average cartilage strain at maximum flexion positively correlated with total MMP activity in the synovial fluid. Our findings reveal that meniscal injury leads to loss of normal joint function and increased strain of the articular cartilage, which correlated to elevated total MMP activity in the synovial fluid. The increased strain and total MMP activity may reflect, or potentially contribute to, the early development of post-traumatic osteoarthritis following meniscal injury.

References

1. Liu, B., Lad, N.K., Collins, A.T., Ganapathy, P.K., Utturkar, G.M., McNulty, A.L., Spritzer, C.E., Moorman, C.T. 3rd, Sutter, E.G., Garrett, W.E., DeFrate, L.E. In Vivo Tibial Cartilage Strains in Regions of Cartilage-to-Cartilage Contact and Cartilage-to-Meniscus Contact in Response to Walking. American Journal of Sports Medicine, 45(12): 2817-2823, 2017.

2. Carter, T.E., Taylor, K.A., Spritzer, C.E., Utturkar, G.M., Taylor, D.C., Moorman III, C.T., Garrett, W.E., Guilak, F., McNulty, A.L., DeFrate, L.E. In vivo Cartilage Strain Increases Following Medial Meniscal Tear and Correlates with Synovial Fluid Matrix Metalloproteinase Activity. Journal of Biomechanics, 48(8):1461-1468, 2015.

Acknowledgments

This work was supported in part by funding from the National Institutes of Health.

Introduction

A systematic review by D'Souza [1] shows that certain parameters are associated with an increased risk for the occurrence and progression of knee osteoarthritis (KOA). This can be shown in the gait pattern by means of three-dimensional gait analysis. Parameters such as peal KAM (maximum knee adduction moment), KAM impulse or varus thrust are values that are analysed in the gait pattern and can be associated with KOA. Furthermore, the forces in the medial-lateral compartment can be calculated from the data of the three gait analyses using musculoskeletal modelling. Changes in the gait pattern can influence these parameters (KAM). Clinical movement analysis can provide feedback on the altered gait pattern and indicate whether the load is actually changed as a result. Changes such as reduced gait speed, reduced cadence, reduced flexion in the stance phase as well as the reduction of hip adduction moment contribute to the lowering of the increased adduction moment in medial knee joint arthrosis [2][3][4][5][6]. Lateral raising of the edge of the shoe or orthoses can also have a positive influence on the load situation in the knee joint. In particular, the first peak in the KAM can be reduced by raising the lateral edge of the shoe in patients with KOA [7].

Content

Using the inverse approach, the load in the medial and lateral compartments can be calculated by means of musculoskeletal modelling. Zhao et al. [8] demonstrated that KAM correlates highly with the contact force in the medial compartment and the ratio of medial to total force during walking. Van Rossom et.al. [9] used musculoskeletal modelling to show that tibial slop, frontal leg alignment and rotational malalignment at the tibia contribute to changes in knee joint forces. A combination of a varus malalignment and an internal rotation malalignment further increases the KAM. However, if the varus malalignment is combined with external rotation, the KAM is normalised. Vice versa for the valgus malalignment.

These observations were also made by the research group around Farr et.al. [10 ] in adolescents who had a valgus deformity in the knee. In patients with a valgus deformity, the tibial rotation has a major influence on the KAM in addition to the frontal deformity. Although the frontal leg axis could be corrected by correcting the axis with tension band plating, the postoperative result of the 3-dimensional gait analysis showed a much higher adduction moment in those patients with a reduced tibial external rotation than in the normal group [11]. Preoperatively, the group with valgus malalignment and rotational malalignment showed an almost normal KAM. In contrast, the valgus-only group showed a greatly reduced KAM.

Musculoskeletal modelling can also be used to investigate which exercises in the rehabilitation phase place more strain on the medial or lateral compartment and thus create targeted planning of the therapy programme.

The three-dimensional clinical gait analysis makes it possible to gain an insight into the prevailing forces on the knee joint and shows an objective picture of the load situation. With the additional use of musculoskeletal modelling, it is also possible to analyse the load distribution in the knee joint more precisely. However, the creation of a patient-specific biomechanical model is currently still quite time-consuming.

References

[1] D’Souza u. a., „Are Biomechanics during Gait Associated with the Structural Disease Onset and Progression of Lower Limb Osteoarthritis?“

[2] Guo, Axe, und Manal, „The Influence of Foot Progression Angle on the Knee Adduction Moment during Walking and Stair Climbing in Pain Free Individuals with Knee Osteoarthritis“.

[3] Mills, Hunt, und Ferber, „Biomechanical Deviations during Level Walking Associated with Knee Osteoarthritis“.

[4] Mündermann u. a., „Potential Strategies to Reduce Medial Compartment Loading in Patients with Knee Osteoarthritis of Varying Severity“.

[5] Chang u. a., „The Relationship between Toe-out Angle during Gait and Progression of Medial Tibiofemoral Osteoarthritis“.

[6] Andrews u. a., „Lower Limb Alignment and Foot Angle Are Related to Stance Phase Knee Adduction in Normal Subjects“.

[7] Fantini Pagani, Hinrichs, und Brüggemann, „Kinetic and Kinematic Changes with the Use of Valgus Knee Brace and Lateral Wedge Insoles in Patients with Medial Knee Osteoarthritis“.

[8] Zhao u. a., „Correlation between the Knee Adduction Torque and Medial Contact Force for a Variety of Gait Patterns“.

[9] Van Rossom u. a., „The Influence of Knee Joint Geometry and Alignment on the Tibiofemoral Load Distribution“.

[10] Farr u. a., „Functional and radiographic consideration of lower limb malalignment in children and adolescents with idiopathic genu valgum“.

[11] Farr u. a., „Rotational gait patterns in children and adolescents following tension band plating of idiopathic genua valga“.