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10:30   Knee 10:30 15 mins Adhesives for fixation of polycarbonate urethane implants to the bone Pardis Farjam, Edsko E.G. Hekman, Gijsbertus J Verkerke, Jeroen Rouwkema Abstract: Polyurethanes (PU) have been used in orthopaedic implants due to their superior characteristics including; good biocompatibility and mechanical properties, lubricity, and good wear properties. PU can be involved in a diverse range of orthopaedic applications including but not limited to joint replacement implants. We are currently designing a novel PU based joint replacement prosthesis, for which we are investigating adhesives as a fixation mechanism to the bone. In orthopaedics, bone adhesives are commonly designed to treat simple and comminuted fractures. Bone adhesives could also be employed as the fixation technique of implants to bone. Adhesives as the anchorage tool bring several advantages, such as: preserving the integrity of the tissue and the implant with the possibility to be delivered via minimal-invasive techniques and offering simple and precise applicability. To be employed as an implant fixation technique, sufficient strength of an adhesive is one of the dominant requirements. Cyanoacrylate-based adhesives have shear bond strengths in the range of 1-2 MPa in a bone-bone bond. Purpose In this study, we evaluated a commercial biocompatible cyanoacrylate-based adhesive Glubran2® (Gem, Italy) as a candidate fixation technique for PU-based orthopaedic implants. Materials and Method PU film was obtained from our project partners at the Fraunhofer Institute for Manufacturing Engineering and Automation (Stuttgart, Germany). Square specimens of 25 mm*25 mm with a bonding area of 25 mm*10 mm were used. A lap-shear test has been conducted according to ASTM standards F2255 for strength properties of tissue adhesives in lap-shear by tension loading. Results The apparent shear strength as the maximum load divided by the bond area was revealed to be 0.07± 0.01 (MPa). Failure did predominantly occur at the bonding site between the cyanoacrylate-based adhesive and the PU film. Conclusion The biocompatible cyanoacrylate-based adhesive showed inferior shear strength in bonding PU to bone compared to bonding bone to bone. New fixation candidates will be studied for the fixation of PU-based joint replacement prostheses. Acknowledgements This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 863183 10:45 15 mins Estimating knee joint moment during running with only a limited inertial measurement unit setup Robbert van Middelaar, Peter Veltink, Jasper Reenalda Abstract: INTRODUCTION: Joint moment is a useful parameter for joint evaluation during running[1]. Commonly, musculoskeletal models are used for calculation based on optical systems and force plates. However, inertial measurement units (IMUs) are used more frequently in movement analysis due to their practical application in sports-specific settings[2]. Furthermore, the application in musculoskeletal models has become of interest, but to perform inverse dynamics the exact centre of pressure (CoP) towards the model needs to be known. Synchronising kinetic and kinematic data is therefore challenging as IMUs do not have a clearly defined positioning system. Therefore, this study aimed to estimate knee joint moments top-down from the hip with a limited IMU setup during running, avoiding the need for CoP, musculoskeletal models or machine learning for easy outdoor application. METHODS: 8 recreational runners (3F/5M; running >10 km p/w >1 year; rear-foot strikers) ran 90 seconds on a treadmill at 12 kph. Acceleration of the pelvis IMU (240 Hz) was used to estimate sagittal body forces (F=(m_body-m_leg)∙a_pelvis) near the centre of mass of the body, which was assumed to be input in the free body diagram of the femur during stance. Inertia and mass of the femur were estimated[3], orientation and (angular) acceleration were obtained with the femur IMU to estimate with top-down inverse dynamics reaction force and joint moment of the knee with a limited IMU setup. RESULTS: Mean peak knee moment of 2.11 ± 0.44 Nm/kg for all subjects, based on two IMUs on the pelvis and thigh. The results show similar behaviour compared to literature[5,6], but validation is needed. CONCLUSION: This study shows the potential of estimating knee joint moment with a limited IMU setup. This was achieved without the commonly used inverse dynamics approach and thus avoiding the CoP issue and without the need for musculoskeletal models and machine learning. This technique needs to be validated against a golden standard system and need further improvement in accurate estimation of hip forces and optimisation of anthropometric parameters. This method can potentially be a new approach in estimating biomechanical parameters at the knee especially in an outdoor, sports-specific setting. 11:00 15 mins Virtual reality platform design for patients in chronic pain after total knee arthroplasty Yiling Zhang, Ming Cao, Hans Timmerman, Elisabeth Wilhelm Abstract: Total knee replacement has good clinical outcomes; however, a significant proportion of the patients experience chronic pain, which is due to the changes that occur in the central nervous system (CNS). When the pain persists, reorganization in the brain may have actually contributed to chronic pain. Sensory-retraining might reduce chronic pain. Therefore, we are designing and evaluating a virtual reality (VR) platform for patients who suffer from chronic pain after knee surgery. This platform not only guides patients to exercise movements correctly, but also provides augmented feedback to improve their proprioception for enhancing the training effect. The VR platform consists of a series of rehabilitation training games. According to the different degrees of the chronic pian and the interests of patients, game avatars can be selected. The level of difficulty, e. g. determined by the size of the virtual targets, can be adjusted according to the recovery status and fitness level. During training, users stretch and bend their legs to kick target objects. Simultaneously, these movements are recorded using Kinect to determine whether the actions are close to the intended exercise. The player will get extrinsic feedback based on the spatial overlap between intended and detected motion. In addition, the design of human-machine interaction reinforces the immersion, and the audio and visual stimulations lead players into the game scenario. The goal of this therapy method is to restore limb functionality, decrease pain, and thereby improve quality of life of patients with osteoarthritis. 11:15 15 mins Personalized musculoskeletal modeling of premorbid knees towards an optimally planned total knee arthroplasty Periklis Tzanetis, Kevin de Souza, Seonaid Robertson, René Fluit, Bart Koopman, Nico Verdonschot Abstract: One of the guiding principles in total knee arthroplasty (TKA) is to restore function of the arthritic knee. Precise knowledge of premorbid kinematics and soft-tissue loading is important for pre-planning an optimal prosthesis alignment scheme, thereby reproducing a balanced knee motion comparable to healthy individuals. The aim of this study was to investigate the differences in tibiofemoral kinematics and loading patterns of cruciate ligaments between arthritic knees and their corresponding premorbid state, using a novel approach combining imaging-based statistical modeling and musculoskeletal simulation. A cadaver-specific musculoskeletal knee model was developed in the AnyBody Modeling System (AnyBody Technology A/S, Aalborg, Denmark) based on computed tomography (CT) and magnetic resonance (MR) images of a cadaveric lower extremity specimen. This is a force-dependent kinematics model, following a previously established methodology[1]. To scale the model to a patient-specific morphology, a CT image of a single patient with knee osteoarthritis was segmented with an automated technology (Stryker, Manchester, UK), whereby a statistical model of shape and appearance identified the arthritic and premorbid femoral and tibial bone surfaces; a statistical shape model trained on MR data to estimate cartilage thickness from the bone surface segmented in CT. Differences between arthritic and premorbid model predictions were quantified in terms of root mean square deviation (RMSD) during an unloaded extension simulation. Compared to the arthritic model, the premorbid model predicted tibiofemoral kinematics with an RMSD greater than 0.3 mm and 0.2 degrees. The largest RMSD in kinematics was found in tibial external rotation (1.5 degrees) and lateral tibial translation (0.4 mm). The posterior and anterior cruciate ligaments in the premorbid model exhibited comparable behavior as in the arthritic model throughout the range of motion with an RMSD of 110 N and 14 N, respectively. These preliminary findings indicate that premorbid surface reconstruction may alter the forces of the cruciate ligaments, and subsequently, the kinematic predictions at the tibiofemoral joint due to osteophyte removal. The proposed methodology can assist orthopedic surgeons towards an optimally planned TKA tailored to individual patients. However, further validation of this is necessary before this workflow can be clinically applied in a safe and patient-specific manner. REFERENCES 1. Skipper Andersen, M., et al., Introduction to force-dependent kinematics: theory and application to mandible modeling. J. Biomech. Eng., 2017. 139(9). 11:30 15 mins Estimation of joint moments using IMUS to aid clinical decision making during ACL rehabilitation: A review Sanchana Krishnakumar, Bert-Jan F. van Beijnum, Jaap H. buurke, Peter H. Veltink, Chris Baten Abstract: Rehabilitation after an anterior cruciate ligament (ACL) injury is divided into multiple phases and progress between phases are based on functional assessment of patients. These assessments are currently subjective and are done by visual monitoring of activities such as running, hopping, figure-8 running, jump landing, cutting maneuvers by physiotherapists. Estimation of objective measures like knee joint moments and ground reaction forces (GRF) during assessment can help in gaining new insights on knee loading and open new avenues for rehabilitation. Accurate estimation of kinetics is a complex task and requires expensive motion capture systems along with force platforms. On the other hand, several algorithms have been proposed in literature to estimate kinetics by just using kinematics measured with inertial sensors (IMUs). However, the knowledge about their clinical applicability is limited. This study aims to compare available algorithms for prediction of GRF and/or estimation of joint moments only using IMUs and evaluate their feasibility for adoption in ACL rehabilitation. A literature search was conducted using Scopus (Elsevier) with following search queries “Estimation (OR) Prediction (AND) GRF (AND) IMU, “Joint Moment (AND) IMU (OR) IMMU”, “Joint kinetics (AND) IMU (OR) IMMU”, “Joint Kinetics (AND) wearables”, “Joint moments (AND) wearables”, and “Knee moments (AND) IMU (OR)IMMU”. The identified studies were classified based on the parameters estimated (joint moments/GRF) and the principles used such as machine learning (ML), musculoskeletal modelling, hybrid, direct modelling, or statistical approach. The comparison of the algorithms was done based on the accuracy achieved, assumptions used, tasks validated and their applicability for ACL patients. Most of the studies evaluated have estimated only vertical GRF with good accuracy and reported lateral GRFs as less reliable. ML-based approaches have proved to be more versatile but have a disadvantage of sensitivity to input parameters and require large sets of training data. Tasks such as walking, landing also involve double support phases where further transfer functions are required to distribute forces between the legs. The applicability of assumptions made for distribution is unclear for ACL patients. The assessed algorithms have also not yet been validated for tasks such as figure-8 running, jump landing and hopping. A combination of two methods such as biomechanical modelling and ML or musculoskeletal modelling may be used to further increase accuracy and make them versatile to estimate joint moments for a large range of movements. Further validation and tuning of these algorithms are thus necessary before being implemented for ACL monitoring and phase decision. 11:45 15 mins Characterization of a crushable foam model for human distal femur and application for total knee arthroplasty Navid SoltaniHafshejani, Federica Peroni, Sara Toniutti, Thom Bitter, Nico Verdonschot, Dennis Janssen Abstract: INTRODUCTION: To evaluate the effective factors on a successful Total knee Arthropathy (TKA), it is required to investigate the mechanical interlock between bone and implant. Crushable Foam (CF) material model has proven to be a good predictor of bone’s strength and failure pattern in the Finite Element(FE) simulation. The present study aimed to obtain CF parameters for trabecular bone in the human distal-femur and make a comparison between them and the softening Von-Misses (sVM) model. Consequently, the CF-model was applied on a femoral component of a total-knee implant to perform a cyclic analysis of daily activity . METHODS: Sixty-four cylindrical samples from the distal part of eight human cadaveric-femora were tested in uniaxial and confined compression. The CF parameters were obtained dependent on bone mineral density’s (BMD) and were compared to the previously obtained tibial parameters . The CF plasticity-model was applied to FE simulation of uniaxial compression and then the results were compared to the sVM model . In addition, the validated CF model was applied to an entire implanted distal femur, analyzing the slow-jogging condition. RESULTS: All the parameters of interest showed significant non-linear correlation with the BMD. Comparing the mechanical properties of the femoral specimens to the ones of tibial trabecular bone shows a higher value of Young’s modulus in femur, while the yield stress value is slightly lower than the tibial-properties. Numerical-simulations of the uniaxial compression with CF-model resulted in more accurate prediction of bone-response compare to sVM model . Analyzing the slow-jogging condition indicated that the CF model could capture a growth of permanent deformation at the beginning of a cyclic loading. DISCUSSION: Mechanical properties of the human femoral bone were obtained through experimental examination and a CF model was characterized. The identified CF-model accurately predict the bone-behavior in uniaxial compression . The accurate bone response in the FE-simulation derived from yield surface update and consideration of the hydrostatic stress compartment in CF model. The distinctive yield surface of this model allowed for capturing the growth of permanent deformation in cyclic loading. SIGNIFICANCE: A nonlinear material model has been characterized for the human distal femur which allows for the evaluation of bone-implant interlock.

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