Introduction. A deep squat (DS) is a challenging motion at the level of the hip joint generating substantial reaction forces (HJRF). As a closed chain exercise, it has great value in rehabilitation and muscle strengthening of hip and knee. During DS, the hip flexion angle approximates the functional range of hip motion risking femoroacetabular impingement in some morphologies. In-vivo HJRF measurements have been limited to instrumented implants in a limited number of older patients performing incomplete squats (< 50° hip flexion and < 80° knee flexion). On the other hand, total hip arthroplasty is being increasingly performed in a younger and higher demanding patient population. These patients clearly have a different kinetical profile with hip and knee flexion ranges going well over 100 degrees. Since measurements of HJRF with instrumented prostheses in healthy subjects would be ethically unfeasible, this study aims to report a personalised numerical solution based on inverse dynamics to calculate realistic in-silico HJRF values during DS. Material and methods. Thirty-five healthy males (18–25 years old) were prospectively recruited for motion and morphological analysis. DS motion capture (MoCap) acquisitions and MRI scans with gait lab marker positions were obtained. The AnyBody Modelling System (v6.1.1) was used to implement a novel personalisation workflow of the AnyMoCap template model. Bone geometries, semi-automatically segmented from MRI, and corresponding markers were incorporated into the template human model by an automated procedure. A state of-the-art TLEM 2.0 dataset, included in the Anybody Managed Model Repository (v2.0), was used in the template model. The subject-specific MoCap trials were processed to compute kinematics of DS, muscle and joint reaction forces in the entire body. Resulting hip joint loads were compared with in-vivo data from OrthoLoad dataset. Additionally, hip and knee joint angles were computed. Results. An average HJRF of 274%BW (251.5 – 297.9%BW; 95% confidence interval) was calculated at the peak of DS. The HJRF on the pelvis was directed superior, medial and posterior throughout the DS. Peak knee and hip flexion angles were 112° (108.1° – 116.5°) and 107° (104.6° – 109.4°) on average. Discussion and conclusions. A comprehensive approach to construct an accurate personalised musculoskeletal model from subject-specific MoCap data, bone geometries, and palpatory landmarks was presented. Consistently higher HJR forces during DS in young adults were demonstrated as opposed to the Orthoload dataset. Similarly, knee and hip flexion angles were much higher, which could cause the increase in HJRF. It can be concluded that DS kinetics in young adults differ from the typical total hip arthroplasty population. These models will enable further in-silico joint biomechanics studies, and could serve the purpose of a virtual test bed for implant design

For a proper rehabilitation of the knee following knee arthroplasty, a comprehensive understanding of bony and soft tissue structures and their effects on biomechanics of the individual patient is essential. Musculoskeletal models have the potential, however, to predict dynamic interactions of the knee joint and provide knowledge to the understanding of knee biomechanics. Our goal was to develop a generic musculoskeletal knee model which is adaptable to subject-specific situations and to use in-vivo kinematic measurements obtained under full-weight bearing condition in a previous Upright-MRI study of our group for a proper validation of the simulation results. The simulation model has been developed and adapted to subject-specific cases in the multi-body simulation software AnyBody. For the implementation of the knee model a reference model from the AnyBody Repository was adapted for the present issue. The standard hinge joint was replaced with a new complex knee joint with 6DoF. The 3D bone geometries were obtained from an optimized MRI scan and then post-processed in the mesh processing software MeshLab. A homogenous dilation of 3 mm was generated for each bone and used as articulating surfaces. The anatomical locations of viscoelastic ligaments and muscle attachments were determined based on literature data. Ligament parameters, such as elongation and slack length, were adjusted in a calibration study in two leg stance as reference position. For the subject-specific adaptation a general scaling law, taking segment length, mass and fat into account, was used for a global scaling. The scaling law was further modified to allow a detailed adaption of the knee region, e.g. align the subject-specific knee morphology (including ligament and muscle attachments) in the reference model. The boundary conditions were solely described by analytical methods since body motion (apart from the knee region) or force data were not recorded in the Upright-MRI study. Ground reaction forces have been predicted and a single leg deep knee bend was simulated by kinematic constraints, such as that the centre of mass is positioned above the ankle joint. The contact forces in the knee joint were computed using the force dependent kinematic algorithm. Finally, the simulation model was adapted to three subjects, a single leg deep knee bend was simulated, subject-specific kinematics were recorded and then compared to their corresponding in-vivo kinematic measurements data. We were able to simulate the whole group of subjects over the complete range of motion. The tibiofemoral kinematics of three subjects could be simulated showing the overall trend correctly, whereas absolute values partially differ. In conclusion, the presented simulation model is highly adaptable to an individual situation and seems to be suitable to approximate subject-specific knee kinematics without consideration of cartilage and menisci. The model enables sensitivity analyses regarding changes in patient specific knee kinematics following e.g. surgical interventions on bone or soft tissue as well as related to the design and placement of partial or total knee joint replacement. However, model optimisation, a higher case number, sensitivity analyses of selected parameters and a semi-automation of the workflow are parts of our ongoing work

Introduction. Gait laboratory measurement of whole-body kinematics and ground reaction forces during a wide range of activities is frequently performed in joint replacement patient diagnosis, monitoring, and rehabilitation programs. These data are commonly processed in musculoskeletal modeling platforms such as OpenSim and Anybody to estimate muscle and joint reaction forces during activity. However, the processing required to obtain musculoskeletal estimates can be time consuming, requires significant expertise, and thus seriously limits the patient populations studied. Accordingly, the purpose of this study was to evaluate the potential of deep learning methods for estimating muscle and joint reaction forces over time given kinematic data, height, weight, and ground reaction forces for total knee replacement (TKR) patients performing activities of daily living (ADLs). Methods. 70 TKR patients were fitted with 32 reflective markers used to define anatomical landmarks for 3D motion capture. Patients were instructed to perform a range of tasks including gait, step-down and sit-to-stand. Gait was performed at a self-selected pace, step down from an 8” step height, and sit-to-stand using a chair height of 17”. Tasks were performed over a force platform while force data was collected at 2000 Hz and a 14 camera motion capture system collected at 100 Hz. The resulting data was processed in OpenSim to estimate joint reaction and muscle forces in the hip and knee using static optimization. The full set of data consisted of 135 instances from 70 patients with 63 sit-to-stands, 15 right-sided step downs, 14 left-sided step downs, and 43 gait sequences. Two classes of neural networks (NNs), a recurrent neural network (RNN) and temporal convolutional neural network (TCN), were trained to predict activity classification from joint angle, ground reaction force, and anthropometrics. The NNs were trained to predict muscle and joint reaction forces over time from the same input metrics. The 135 instances were split into 100 instances for training, 15 for validation, and 20 for testing. Results. The RNN and TCN yielded classification accuracies of 90% and 100% on the test set. Correlation coefficients between ground truth and predictions from the test set ranged from 0.81–0.95 for the RNN, depending on the activity. Predictions from both NNs were qualitatively assessed. Both NNs were able to effectively learn relationships between the input and output variables. Discussion. The objective of the study was to develop and evaluate deep learning methods for predicting patient mechanics from standard gait lab data. The resulting models classified activities with excellent performance, and showed promise for predicting exact values for loading metrics for a range of different activities. These results indicate potential for real-time prediction of musculoskeletal metrics with application in patient diagnostics and rehabilitation. For any figures or tables, please contact authors directly

Introduction. Fretting corrosion at the taper interface of modular connections can be studied using Finite Element (FE) analyses. However, the loading conditions in FE studies are often simplified, or based on generic activity patterns. Using musculoskeletal modeling, subject-specific muscle and joint forces can be calculated, which can then be applied to a FE model for wear predictions. The objective of the current study was to investigate the effect of incorporating more detailed activity patterns on fretting simulations of modular connections. Methods. Using a six-camera motion capture system, synchronized force plates, and 45 optical markers placed on 6 different subjects, data was recorded for three different activities: walking at a comfortable speed, chair rise, and stair climbing. Musculoskeletal models, using the Twente Lower Extremity Model 2.0 implemented in the AnyBody modeling System™ (AnyBody Technology A/S, Aalborg, Denmark; figure1), were used to determine the hip joint forces. Hip forces for the subject with the lowest and highest peak force, as well as averaged hip forces were then applied to an FE model of a modular taper connection (Biomet Type-1 taper with a Ti6Al4V Magnum +9 mm adaptor; Figure 2). During the FE simulations, the taper geometry was updated iteratively to account for material removal due to wear. The wear depth was calculated based on Archard's Law, using contact pressures, micromotions, and a wear factor, which was determined from accelerated fretting experiments. Results. The forces for the comfortable walking speed had the highest peak forces for the maximum peak subject, with a maximum peak force of 3644 N, followed by walking up stairs, with a similar maximum peak force of 3626 N. The chair rise had a lower maximum peak force of 2240 N (−38.5%). The simulated volumetric wear followed the trends seen in the peaks of the predicted hip joint forces, with the largest wear volumes predicted for a comfortable walking speed, followed by the stairs up activity and the chair rise (Figure 3). The subjects with the highest peak forces produced the most volumetric wear in all cases. However, the lowest peak subject had a higher volumetric wear for the stairs up case than the average subject. Discussion. This study explored the effect of subject-specific variations in hip joint loads on taper fretting. The results indicate that taper wear was predominantly affected by the magnitudes of the peak forces, rather than by the orientation of the force. A more comprehensive study, capturing the full spectrum of patient variability, can help identifying parameters that accelerate fretting corrosion. Such a study should also incorporate other sources of variability, including surgical factors such as implant orientation, sizing, and offset. These factors also affect hip joint forces, and can be evaluated in musculoskeletal models such as presented here

Introduction. One of the known mechanisms which could contribute to the failure of total hip replacements (THR) is edge contact. Failures associated with edge contact include rim damage and lysis due to altered loading and torques. Recent study on four THR patients showed that the inclusion of pelvic motions in a contact model increased the risk of edge contact in some patients. The aim of current study was to determine whether pelvic motions have the same effect on contact location for a larger patient cohort and determine the contribution of each of the pelvic rotations to this effect. Methods. Gait data was acquired from five male and five female unilateral THR patients using a ten camera Vicon system (Oxford Metrics, UK) interfaced with twin force plates (AMTI) and using a CAST marker set. All patients had good surgical outcomes, confirmed by patient-reported outcomes and were considered well-functioning, based on elective walking speed. Joint contact forces and pelvic motions were obtained from the AnyBody modelling system (AnyBody Technologies, DK). Only gait cycle regions with available force plate data were considered. A finite element model of a 32mm head on a featureless hemispherical polyethylene cup, 0.5mm radial clearance, was used to obtain the contact area from the contact force. A bespoke computational tool was used to analyse patients' gait profiles with and without pelvic motions. The risk of edge contact was measured as a “centre proximity angle” between the cup pole and centre of the contact area, and “edge proximity angle” between the cup pole and the furthest contact area point away from the pole. Pelvic tilt, drop and internal-external rotation were considered one at a time and in combinations. Results. In eight out of 10 patients, the addition of pelvic motions decreased the risk of edge contact during toe-off. There was up to 6° reduction in the proximity angles when pelvic motions were introduced to the gait cycle. In six out of 10 patients, the addition of pelvic motions resulted in an increase in the risk of edge contact during heel-strike with up to 6° increase in the proximity angles. For all patients where these effects were seen, sagittal pelvic tilt was a substantial contributor. Conclusion. The results of this study suggest that pelvic motion play an important role in contact location in THR bearings during loading phase. Both static and dynamic pelvic tilt contribute to the variability in the risk of edge contact. Further tests on larger patient cohorts are required to confirm the trends observed. The outcomes of this study suggest that pre-clinical mechanical and tribological testing of THRs should consider the role of pelvic motion. The outcomes also have implications for establishing surgical positioning safe zones, which are currently based only on risk of dislocation and severe impingement

Introduction. There are over one-half million total knee replacement (TKR) procedures performed each year in the United States and is projected to increase to over 3.48 million by 2030. Concurrent with the increase in TKR procedures is a trend of younger patients receiving knee implants (under the age of 65). These younger patients are known to have a 5% lower implant survival rate at 8 years post-op compared to older patients (65+ years), and they are also known to live more active lifestyles that place higher demands on the durability and functional performance of the TKR device. Conventional TKR designs increase articular conformity to increase stability, but these articular constraints decrease patient range of knee motion, often limiting key measures of femoral rollback, A/P motion, and deep knee flexion. Without this articular constraint however, many patients report TKR “instability” during activities such as walking and stair descent, which can significantly impede confidence of movement. Therefore there is a need for a TKR system that can offer enhanced stability while also maintaining active ranges of motion. Materials and Methods. A novel knee arthroplasty system was designed that uses synthetic ligament systems that can be surgically replaced, to provide ligamentous stability and natural motion to increase the functional performance of the implant. Using an anatomical knee model from the AnyBody software, a computational model that incorporated ligaments into an existing Journey II TKR was developed. Using the software ligaments were modeled and given biomechanical properties developed from equations from literature. Simulated A/P drawer tests and knee flexion test were analyzed for 2,916 possible cruciate ligament location and length combinations to determine the effects on the A/P stability of the TKR. A physical model was constructed, and the design was verified by performing 110 N A/P drawer tests under 710 N of simulated body weight. Results and Discussion. As ACL insertion location moved posteriorly on the femur, it was found to decrease ACL ligament strain, enabling a higher range of flexion. In general, as ACL and PCL length increased, the A/P laxity of the TKR system increased linearly. Range of motion was found to be more dependent on ligament attachment location, and laxity was more dependent on ligament length. In this work, TKR stability was clearly affected by changes in synthetic ligament length and location. When comparing the laxity between a TKR with and without ligaments, the TKR with synthetic ligaments experienced significantly less displacement than a TKR without synthetic ligaments as seen in Figure 1. Conclusions. This study shows that the stability of a TKR can be increased while maintaining range of motion by incorporating synthetic ligaments into this design. The effectiveness of the ligaments was clearly dependent on two factors: length and location, with incorrect lengths and locations significantly impairing ranges of motion. These results verify that a knee replacement can incorporate synthetic ligaments, and that with calibrated location and lengths, they can significantly influence stability and possible kinematic performance of the TKR system, and potentially influencing long-term functional outcomes. For any figures or tables, please contact the authors directly

Introduction. Total knee arthroplasty (TKA) is a well proven surgical procedure. Squat and gait motions are common activities in daily life. However, squat motion is known as most dissatisfying motion in activities in daily life after total knee arthroplasty (TKA). Dissatisfaction after TKA might refer to muscle co-contraction between quadriceps and hamstrings. The purposed of this study was to develop squat and gait simulation model and analyses the contact mechanics and quadriceps and hamstring muscle stability. We hypothesized that squat model shows larger contact forces and lower hamstring to quadriceps force ratio than gait model. Materials and Methods. Squat motion and gait model were simulated in musculoskeletal simulation software (AnyBody Modeling System, AnyBody Technology, Denmark). Subject-specific bone models used in the simulation were reconstructed from CT images by Mimics (Materialize, Belgium). The lower extremity model was constructed with pelvis, femur, tibia, foot segments and total knee replacement components: femoral component, tibial insert, tibial tray, and patella component [Fig.1]. The muscle model was consisted of 160 muscle elements. The TKR components used in this study are PS-type LOSPA Primary Knee System (Corentec Co., Ltd, Republic of Korea). Force-dependent kinematics method was used in the simulation. The model was simulated to squat from 15° to 100° knee flexion, in 100 frames. Gait simulation model was based on motion capture and force-plate system. Motion capture and force-plate data were from grand challenge competition dataset. Results / Discussion. Patellofemoral contact forces ranged from 0.18 to 3.78 percent body weight (%BW) and from 0.00 to 1.36 %BW during squat motion and gait cycle, respectively. Patellofemoral contact forces calculated at 30°, 60°, and 90° flexion during squat motion were 0.53, 1.93, and 3.22 %BW, respectively. Wallace et al. also reported patellofemoral contact forces at 30°, 60°, and 90° flexion, which were 0.31, 1.33, 2.45 %BW during squat motion. Our results showed similar results from other studies, however the squat model overestimated the patellofemoral contact forces. Contact stiffness in the simulation model might affected the overestimated contact forces. Hamstring to quadriceps force ratio ranged from 0.32 to 1.88 for squat model, and from 0.00 to 2.54 for gait model. As our hypothesis, squat motion showed larger patellofemoral contact forces. Also, mean hamstring to quadriceps force ratio of squat model were about half than the mean hamstring to quadriceps force ratio of gait model. From the results, possibility exists that unbalanced force of quadriceps and hamstring can affect dissatisfaction after TKA while squat motion is the most dissatisfying motion after TKA. However, muscle stability is not the only factor that can affect dissatisfaction after TKA. In future study, more biomechanical parameters should be evaluated to find meaningful dissatisfying factor after TKA. Conclusion. In conclusion, TKA musculoskeletal models of squat and gait motion were constructed and patellofemoral contact force / hamstring to quadriceps force ratio were evaluated. Patellofemoral mechanics were validated by comparison of previous study. Additional studies are needed to find dissatisfying factor after TKA

Introduction. Early hip OA may be attributed to smaller coverage of the femoral head leading to higher loads per unit area. We hypothesize that tight hamstrings may contribute to increased loads per unit area on the femoral head during gait. When a patient has tight hamstrings they cannot flex their pelvis in a normal fashion which may result in smaller coverage of the femoral head (Figure 1). This study aimed to determine if subjects with tight hamstrings can improve femoral head coverage during gait after a stretching intervention. Methods. Nine healthy subjects with tight hamstrings (popliteal angle>25°) were recruited and consented for this IRB approved study. Gait analysis with 58 reflective markers were placed by palpation on anatomical landmarks of the torso and lower extremities. Ten optoelectronic cameras (Qualisys, Gothenburg, Sweden) and three force plates (AMTI, Watertown, MA) were used to track marker position and measure foot strike forces. Subjects walked at a self-selected speed across the force plates until ten clean trials were performed and then were scanned with the reflective markers on the spine using an EOS (EOS Imaging, France) bi-planar x-ray system. Following testing participants completed a six week stretching program to increase hamstring length. Pelvic tilt (PT) was measured at heel strike for each trial and averaged. Using EOS scans the femoral head radius was measured using three points that best fit the load bearing surface on the sagittal view from the anterior acetabular rim to a point on the posterior acetabulum 45 degrees from vertical. The radius of femoral head and angle of acetabular coverage were used to calculate the load bearing surface area of femoral head. Load on the femur was calculated using an Anybody lower body model (Anybody Technology, Aalborg, Denmark) and load per unit area change was compared. Results. Nine participants completed the stretching program and post intervention testing. PA increased in all subjects (mean ± SD) 18.8° ± 11° (p<.01). Eight of nine subjects had an increase in anterior PT at heel strike resulting in a mean change of 2.1° ± 2.9° (p<.05). The change in PT resulted in a mean surface area change of 0.63cm. 2. ± 0.77 cm. 2. (p<.05), which resulted in a mean pressure change of −57.9MPa ± 55.7MPa. Removing the one subject who decreased in anterior pelvic tilt resulted in a mean change in PT of 2.9° ± 1.2°, a mean change in surface area of 0.85cm. 2. ± 0.46 cm. 2. , and a mean pressure change of −74.4 MPa ± 27.2 MPa. (Table 1). Discussion/Conclusion. This study verified the hypothesis that functional PT is influenced significantly by tight hamstrings. Using a stretching intervention program small changes in functional PT can be elicited that may significantly decrease the force per unit area on the femoral head and possibly the risk for developing degeneration joint disease. Although our study is limited by the small number of participants it does lend one significant benefit to intervene in patients who have chronically tight hamstrings. For figures/tables, please contact authors directly.

Introduction. For the evaluation of new orthopaedic implants, cadaveric testing remains an attractive solution. However, prior to cadaveric testing, the performance of an implant can be evaluated using numerical simulations. These simulations can provide insight in the kinematics and contact forces associated with a specific implant design and/or positioning. Methods. Both a two and three dimensional simulation model have been created using the AnyBody Modelling System (AMS). In the two dimensional model, the knee joint is represented by a hinge. Similarly, the ankle and hip joint are represented by a hinge joint and a variable amplitude quadriceps force is applied to a rigid bar connected to the tibia (Figure 1a). In line with this simulation model, a hinge model was created that could be mounted in the UGent knee simulator to evaluate the performance of the simulated model. The hinge model thereby performs a cyclic motion under varying quadriceps load while recording the ankle reaction forces. In addition to the two dimensional model, a three dimensional model has been developed (Figure 1b). More specifically, a model is built of a sawbone leg holding a posterior stabilized single radius total knee implant. The physical sawbone model contains simplified medial and lateral collateral ligaments. In line with the boundary conditions of the UGent knee simulator, the simulated hip contains a single rotational degree of freedom and the ankle holds four degrees of freedom (three rotations, single translation). In the simulations, the knee is modelled using the force-dependent kinematics (FDK) method built in the AMS. This leaves the knee with six degrees of freedom that are controlled by the ligament tension in combination with the applied quadriceps load and shape of the implant. The physical sawbone model goes through five cycles in the UGent simulator using while recording the kinematics of the femur and tibia using a set of markers rigidly attached to the femur and tibia bone. The position of the implant with respect to the markers was evaluated by CT-scanning the sawbone model. Results and Discussion. In a first step, the reaction forces at the ankle in the 2D model were evaluated. The difference between the simulated and measured reaction force is limited and can be explained from a slight variation of the attachment point of the quadriceps load. For the 3D model, the kinematic patterns have been evaluated for both the simulation and physical model using Grood & Suntay definitions. The kinematic parameters display realistic trends, however, no exact match has been obtained for all parameters so far. The latter might be attributed to a number of simplifications in the simulation model as well as elastic deformation of the physical sawbone model. Conclusion. A three dimensional model of a knee implant in the UGent Knee Simulator has been developed. The simulated kinematic patterns appear realistic though no exact match with the measured patterns has been obtained. Future research will therefore focus on the development of a more realistic experimental and numerical model

INTRODUCTION. Total hip arthroplasty (THA) is a very successful orthopaedic treatment with 15 years implant survival reaching 95%, but decreasing age and increasing life expectancy of THA patients ask for much longer lasting solutions. Shorter and more flexible cementless stems are of high interest as these allow to maintain maximum bone stock and reduce adverse long-term bone remodeling.1 However, decreasing stem length and reducing implant stiffness might compromise the initial stability by excessively increasing interfacial stresses. In general, a good balance between implant stability and reduced stress shielding must be provided to obtain durable THA reconstruction.2. This finite element (FE) study aimed to evaluate primary stability and bone remodeling of a new design of short hip implant with solid and U-shaped cross-section. MATERIALS AND METHODS. The long tapered Quadra-H stem and the short SMS implants (Medacta International, Castel San Pietro, Switzerland) were compared in this study (Figure 1). A FE model of a femur was based on calibrated CT data of an 81 year-old male (osteopenic bone quality). Both titanium alloy implants were assigned an elastic modulus of 105 GPa and the Poisson's ratios were set to 0.3. Initial stability simulations included the hip joint force and all muscle loads during a full cycle of normal walking as calculated in AnyBody software (Anybody Technology AS, Denmark), whereas the remodeling simulation used the peak loads from normal walking and stair climbing activities. Initial stability results are presented as micromotions on the implant surface with a threshold of 40 µm.3 Bone remodeling outcomes are represented in a form of simulated Dual X-ray Absorptiometry (DEXA) scans and the quantitative bone mineral density (BMD) changes in 7 periprosthetic zones. RESULTS. The U-shaped SMS implant showed slightly higher micromotions (2.7% surface area exceeding 40 µm) than the Quadra-H stem (0.2%), whereas micromotions of solid SMS were considerably higher (8.4%) (Figure 2). The largest micromotions were found on medial side of all implants. The smallest bone loss one year post-operatively was predicted around the U-shaped SMS implant. Proximal zones (1, 6 and 7) showed the largest bone loss with average of 9.9%, 11.8% and 12.8% for the U-shaped SMS, solid SMS and Quadra-H respectively (Figure 3). The bone remodeling prediction for the Quadra-H stem was in good agreement with clinical DEXA measurements (overall bone loss of 5.5% vs. 5.7). CONCLUSION. The U-shaped SMS implant is clearly superior to its solid version and has potential to provide comparable initial stability as the long Quadra-H stem and considerably better long-term bone stock preservation

There is an increasing prevalence of haptic devices in many engineering fields, especially in medicine and specifically in surgery. The stereotactic haptic boundaries used in Computer Aided Orthopaedic Surgery Unicomparmental Knee Arthroplasty (CAOS UKA) systems for assistive milling control can lead to an increase in the force required to manipulate the device; this study presented here has seen a several fold increase in peak forces between haptic and non-haptic conditions of a semi-active preoperative image system. Orthopaedic Arthroplasty surgeons are required to apply forces ranging from large gripping forces to small forces for delicate manipulation of tools and through a large range of postures. There is also a need for surgeons to move around and position themselves to gain line of sight with the object of interest and to operate while wearing additional clothing such as the protective headwear and double gloves. These factors further complicate comparison with other ergonomic studies of other robotics systems. While robotics has been implemented to reduce fatigue in surgery one area of concern in CAOS is localised user muscle fatigue in high volume use. In order to create the conditions necessary for the generation of fatigue in a realistic user experience, but in the time available for the participants, an extended period of controlled and prolonged cutting and manipulation of the robotic arm was needed. This pragmatic test requirement makes the test conditions slightly artificial but does indicate areas of high potential for fatigue when interacting with the system in high volume instances. The surgeon-robotic system interaction was captured using 3 dimensional motion analysis and a force transducer embedded in the end effector of the robotic arm and modelled using an existing upper body model in Anybody software. The kinematic and force information allowed initial calculations of the interaction between the user and the Robotic system. Validation of the model was conducted using Electromyography assessment of activity and fatigue. Optimisation of the model sought to create an efficient cutting regime to reduce cutting time with reduced muscle force in an attempt to reduce users discomfort/fatigue while taking into account anthropometric variations in the users and minimising overall energy requirements, burr path length and maximum muscle force. From the assessment of a small group of three surgeons with experience of the Robotic system there was little to no experience of above normal localised fatigue during small volume use of the system. Observation of these surgeons operating the robot state otherwise with examples of reactions to discomfort. There is also anecdotal evidence that fatigue becomes more problematic in higher volume work loads

Currently, standard total knee arthroplasty (TKA) procedures focus on axial and rotational alignment of the prosthesis components and ligament balancing. Even though TKA has been constantly improved, TKA patients still experience a significantly poorer functional outcome than total hip arthroplasty patients. Among others, complications can occur when knee kinematics (active/passive) after TKA do not correspond with the physiological conditions. We hypothesised that the Q-angle has a substantial impact on active joint kinematics and should be taken into account in TKA. The Q-angle can be influenced by the position of the tibial tuberosity (TT). A pathological position of the TT is commonly related to patellofemoral pain and knee instability. A clinically well accepted surgical treatment is the TT medialisation which causes a change in the orientation of the patella tendon and thus alters the biomechanics of the knee. If active and passive knee kinematics differs, this aspect should be considered for implant design and positioning. Therefore we investigated the sensitivity of active knee kinematics related to the position of the TT by using a complex multi-body model with a dynamic simulation of an entire gait cycle. The validated model has been implemented in the multi-body simulation software AnyBody and was adapted for the present issue. The knee joint is represented by articulating surfaces of a standard prosthesis and contains 6 degrees of freedom. Intra-articular passive structures are implemented and the muscular apparatus consists of 159 muscles per leg. As input parameter for the sensitivity analysis, the TT was translated medially 9 mm and laterally 15 mm from the initial position in equidistant steps of 3 mm. The Q-angle was about 10° in the initial position, which lies in the physiological range. It changed approximately 2.5° with a gradual shift of 3 mm, confirming the impact of the individual TT position on active knee kinematics. The tibiofemoral kinematics, particularly the internal/external rotation of the tibia was significantly affected. Lateralisation of the TT decreased the external rotation of the tibia, whereas a medialisation caused an increase. During contralateral toe off the external rotation was +7.5° for a medial transfer of 9 mm and −1.4° for a lateral transfer of 15 mm, respectively. The differences in external rotation were almost zero for low flexion angles, correlating with the activation pattern of the quadriceps muscle: the higher the activation of the quadriceps, the greater were the changes in kinematics. In conclusion, knee kinematics are strongly affected by the Q-angle which is directly associated with the position of the TT. As active kinematics may show significant differences to passive kinematics, intraoperative ligament balancing may result in a suboptimal ligament situation during active motion. Since the Q-angle varies widely between gender and patients, the individual situation should be considered. The optimisation of the model and further experimental validation is one aspect of our ongoing work

INTRODUCTION. Several clinical studies demonstrated long-term adjacent-level effects after implantation of spinal fusion devices[1]. These effects have been reported as adjacent joint degeneration and the development of new symptoms correlating with adjacent segment degeneration[2] and the trend has therefore gone to motion preservation devices; however, these effects have not been understood very well and have not been investigated thoroughly[3]. The aim of this study is to investigate the effect of varying the stiffness of spinal fusion devices on the adjacent vertebral levels. Disc forces, moments and facet joint forces were analyzed. METHODS. The AnyBody Modeling System was used to compute the in-vivo muscle and joint reaction forces of a musculoskeletal model. The full body model used in this study consists of 188 muscle fascicles in the lumbar spine and more than 1000 individual muscle branches in total. The model has been proposed by de Zee et al.[3], validated by Rasmussen et al.[4] and by Galibarov et al.[5]. The new model[5] determines the individual motions between vertebrae based on the equilibrium between forces acting on the vertebrae from muscles and joints and the passive stiffness in disks and ligaments, figure 1a. An adult of 1.75 m and 75 kg with a spinal implant in L4L5 was modeled. This model was subjected to a flexion-extension motion using different elastic moduli to analyze and compare to a non-implanted scenario. The analyzed variables were vertebral motion, the disc reaction forces and moments, as well as facet joint forces in the treated and the adjacent levels: L2L3, L3L4, L4L5 and L5-Sacrum. RESULTS. When introducing a spinal fusion device in the L4L5 joint the reaction forces and moments decreased in this joint with stiffer devices leading to lower joint loads. However, in the adjacent joints, L3L4 and L5Sacrum, an increase was observed when implanting stiffer devices. Similar trends could be found for the L2L3 joint. The loads in the facet joints showed the same trends. While introducing a spinal fusion device reduced the facet joint forces in the treated joint, the loads in the adjacent facet joints were increased according to the stiffness of the implanted device, figure 1b. DISCUSSION. While the treated disc joint showed reduced motion and loads, the adjacent levels demonstrated a significant increase. In particular, the increased facet joint forces in the adjacent levels can lead to adjacent level facet pain or accelerated facet joint degeneration. Introducing a device resulted in preventing facet contact and therefore facet joint loads, even using the device with the lowest stiffness. CONCLUSION. The presented model shows that clinical complications such as facet joint degeneration in adjacent levels after implantation of spinal fusion device are consistent with the change in the mechanical-stimulus distribution in the system

The alignment of prostheses components has a major impact on the longevity of total knee protheses as it significantly influences the biomechanics and thus also the load distribution in the knee joint. Knee joint loads depend on three factors: (1) geometrical conditions such as bone geometry and implant position/orientation, (2) passive structures such as ligaments and tendons as well as passive mechanical properties of muscles, and (3) active structures that are muscles. The complex correlation between implant position and clinical outcome of TKA and later in vivo joint loading after TKA has been investigated since 1977. These investigations predominantly focused on component alignment relative to the mechanical leg axis (Mikulicz-line) and more recently on rotational alignment perpendicular to the mechanical axis. In general four different approaches can be used to study the relationship between implant position and knee joint loads: In anatomical studies (1), the influence of the geometrical conditions and passive structures can be analyzed under the constraint that the properties of vital tissue are only approximated. This could be overcome with an intraoperative load measurement approach (2). Though, this set up does not consider the influence of active structures. Although post-operative in vivo load measurements (3) provide information about the actual loading condition including the influence of active structures, this method is not applicable to investigate the influence of different implant positions. Using mathematical approaches (4) including finite element analysis and multi-body-modeling, prostheses positions can be varied freely. However, there exists no systematical analysis of the influence of prosthesis alignment on knee loading conditions not only in axial alignment along and rotational alignment perpendicular to the mechanical axis but in all six degrees of freedom (DOF) with a validated mathematical model. Our goal was therefore to investigate the correlation between implant position and joint load in all six DOF using an adaptable biomechanical multi-body model. A model for the simulation of static single leg stance was implemented as an approximation of the phase with the highest load during walking cycle. This model is based on the AnyBody simulation software (AnyBody Technology A/S, Denmark). As an initial approach, with regard to the simulation of purely static loading the knee joint was implemented as hinge joint. The patella was realised as a deflection point, a so called “ViaNode,” for the quadriceps femoris muscle. All muscles were implemented based on Hill's muscle model. The knee model was indirectly validated by comparison of the simulation results for single and also double leg stance with in-vivo measurements from the Orthoload database (www.orthoload.de). For the investigation of the correlation between implant position and knee load, major boundary conditions were chosen as follows:. •. Flexion angle was set to 20° corresponding to the position with the highest muscle activity during gait cycle. •. Muscle lengths and thereby also muscle loads were adapted to the geometrical changes after each simulation step representing the situation after post-operative rehabilitation. As input parameters, the tibial and femoral components' positions were independently translated in a range of ±20mm in 10 equally distant steps for all three spatial directions. For the rotational alignment in adduction/abduction as well as flexion/extension the tibial and femoral components' positions were varied in the range of ±15° and for internal/external rotation within the range of ±20°, also in 10 equally angled steps. Changes in knee joint forces and torques as well as in patellar forces were recorded and compared to results of previous studies. Comparing the simulation results of single and double leg stance with the in-vivo measurements from the Orthoload database, changes in knee joint forces showed similar trends and the slope of changes in torques transmitted by the joint was equal. Against the background of unknown geometrical conditions in the Orthoload measurements and the simplification (hinge joint) of the initial multi-body-model compared to real knee joints, the developed model provides a reasonable basis for further investigations already – and will be refined in future works. As influencing parameters are very complex, a non-ambiguous interpretation of force/torque changes in the knee joint as a function of changes in component positions was in many cases hardly possible. Changes in patella force on the other hand could be traced back to geometrical and force changes in the quadriceps femoris muscle. Positional changes mostly were in good agreement with our hypotheses based on literature data when knee load and patellar forces respectively were primarily influenced by active structures, e.g. with regard to the danger of patella luxation in case of increased internal rotation of the tibial component. Whereas simulations also showed results contradicting our expectations for positional changes mainly affecting passive structures, e.g. cranial/caudal translation of the femoral component. This shows the major drawback of the implemented model: Intra-articular passive structures such as cruciate and collateral ligaments were not represented. Additionally kinematic influences on knee and patella loading were not taken into account as the simulations were made under static conditions. Implementation of relative movements of femoral, tibial and patella components and simulation under dynamic conditions might overcome this limitation. Furthermore, the boundary condition of complete muscle adaptations might be critical, as joint loads might be significantly higher shortly after operation. This could lead to a much longer and possibly ineffective rehabilitation process