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Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time. [ABSTRACT FROM AUTHOR]

Introduction: We compared the 12-months effects of arthroscopic surgery and physiotherapist-led care for femoroacetabular impingement (FAI) syndrome on the time-varying magnitude of hip contact force and muscle contributions to hip contact force during walking., Methods: Secondary analysis was performed on thirty-seven individuals with FAI syndrome who received biomechanical assessment before and 12-months following either arthroscopic surgery (n = 17) or physiotherapist-led care (Personalised Hip Therapy, PHT) (n = 20). At both time points, three-dimensional whole-body motions, ground reaction forces, and surface electromyograms (n = 14) were acquired during overground walking. A neuromusculoskeletal model was used to determine hip contact force and muscle contributions to hip contact force. Two-way repeated measures analyses of variance, implemented through statistical parametric mapping, were used to assess interactions between, and main effects of, treatment (arthroscopy vs. PHT) and time (baseline vs. follow-up) on time-varying magnitude of hip contact force and muscle contributions to hip contact force. Effects were reported as mean differences (normalized to bodyweight, BW) with 95% confidence intervals [95% CI, lower, upper bound]., Results: For both treatment groups, hip contact force was larger at 12-months compared to their respective baseline (mean increase across stride, arthroscopy: 0.97 BW [95% CI 0.49, 1.46] p < .001; PHT: 1.05 BW [95% CI 0.68, 1.43] p < .001), however, no interaction effects were found. For both treatment groups, hip flexor, adductor, and abductor muscle groups made greater contributions to hip contact force after 12-months compared to baseline, while hip extensors made smaller contributions., Conclusions: Compared to baseline, both treatments resulted in 12-months increases in hip contact force during walking caused by larger flexor, adductor, and abductor muscle forces at follow-up. At 12-months, hip contact force magnitude remained different normative values reported for healthy individuals, indicating neither treatment fully restored hip biomechanics., Competing Interests: Conflict of Interest and Funding Source: DJH is a consultant to Pfizer, Lilly, TLCBio, and Merck Serono and he and KLB are supported by Australian National Health and Medical Research Council Investigator Grants. For the remaining authors, no conflicts of interest were declared. This work was supported by the Australian National Health and Medical Research Council [grant APP1069278, 2014]. The funding source was not involved in study design, data collection, data analysis or interpretation of results, writing of the manuscript or the decision to submit the article for publication. The authors have no conflicts of interest to disclose., (Copyright © 2024 by the American College of Sports Medicine.)

Aggression-and its role in human societal development-continues to be hotly debated within both the sciences and the humanities. Whatever the evolutionary origins and repercussions of interpersonal and intergroup conflict for the human story, cultures around the globe have invested significant time and effort into designing deadly hand-held weaponry. Here, we describe for the first time, how humans deliver a deadly strike using two iconic and widespread Aboriginal Australian weapons: the kodj and the leangle with parrying shield. We present the world's first evaluation of striking biomechanics and human and weapon efficiency regarding this class of implement. Results demonstrate the leangle is far more effective at delivering devastating blows to the human body, while the kodj-a multi-functional tool-is more efficient for a human to manoeuvre and still capable of delivering severe blows that can cause death. Together, these data provide the beginnings of an in-depth understanding of how hand-held weaponry has impacted the human body throughout the deep past., (© 2024. The Author(s).)

Background: Femoroacetabular impingement syndrome (FAIS) can cause hip pain and chondrolabral damage that may be managed non-operatively or surgically. Squatting motions require large degrees of hip flexion and underpin many daily and sporting tasks but may cause hip impingement and provoke pain. Differential effects of physiotherapist-led care and arthroscopy on biomechanics during squatting have not been examined previously. This study explored differences in 12-month changes in kinematics and moments during squatting between patients with FAIS treated with a physiotherapist-led intervention (Personalised Hip Therapy, PHT) and arthroscopy., Methods: A subsample ( n = 36) of participants with FAIS enrolled in a multi-centre, pragmatic, two-arm superiority randomised controlled trial underwent three-dimensional motion analysis during squatting at baseline and 12-months following random allocation to PHT ( n = 17) or arthroscopy ( n = 19). Changes in time-series and peak trunk, pelvis, and hip biomechanics, and squat velocity and maximum depth were explored between treatment groups., Results: No significant differences in 12-month changes were detected between PHT and arthroscopy groups. Compared to baseline, the arthroscopy group squatted slower at follow-up (descent: mean difference -0.04 m∙s -1 (95%CI [-0.09 to 0.01]); ascent: -0.05 m∙s -1 [-0.11 to 0.01]%). No differences in squat depth were detected between or within groups. After adjusting for speed, trunk flexion was greater in both treatment groups at follow-up compared to baseline (descent: PHT 7.50° [-14.02 to -0.98]%; ascent: PHT 7.29° [-14.69 to 0.12]%, arthroscopy 16.32° [-32.95 to 0.30]%). Compared to baseline, both treatment groups exhibited reduced anterior pelvic tilt (descent: PHT 8.30° [0.21-16.39]%, arthroscopy -10.95° [-5.54 to 16.34]%; ascent: PHT -7.98° [-0.38 to 16.35]%, arthroscopy -10.82° [3.82-17.81]%), hip flexion (descent: PHT -11.86° [1.67-22.05]%, arthroscopy -16.78° [8.55-22.01]%; ascent: PHT -12.86° [1.30-24.42]%, arthroscopy -16.53° [6.72-26.35]%), and knee flexion (descent: PHT -6.62° [0.56- 12.67]%; ascent: PHT -8.24° [2.38-14.10]%, arthroscopy -8.00° [-0.02 to 16.03]%). Compared to baseline, the PHT group exhibited more plantarflexion during squat ascent at follow-up (-3.58° [-0.12 to 7.29]%). Compared to baseline, both groups exhibited lower external hip flexion moments at follow-up (descent: PHT -0.55 N∙m/BW∙HT[%] [0.05-1.05]%, arthroscopy -0.84 N∙m/BW∙HT[%] [0.06-1.61]%; ascent: PHT -0.464 N∙m/BW∙HT[%] [-0.002 to 0.93]%, arthroscopy -0.90 N∙m/BW∙HT[%] [0.13-1.67]%)., Conclusion: Exploratory data suggest at 12-months follow-up, neither PHT or hip arthroscopy are superior at eliciting changes in trunk, pelvis, or lower-limb biomechanics. Both treatments may induce changes in kinematics and moments, however the implications of these changes are unknown., Trial Registration Details: Australia New Zealand Clinical Trials Registry reference: ACTRN12615001177549. Trial registered 2/11/2015., Competing Interests: David Lloyd has received research support from Arthrex and Orthopediatrics on an Australian Research Council Industrial Training and Transformation Centre grant, and from Orthocell on MTPConnect BioMedTech Horizons grant and Australian Research Council Industry Linkage grant. David J. Hunter has received consulting fees for scientific advisory roles from Pfizer, Lilly, Merck Serono, TLCBio, Kolon Tissuegene and Novartis. Nadine Foster is funded through an Australian National Health and Medical Research Council (NHMRC) Investigator Grant (ID: 2018182). No other potential Conflicts of Interest have been declared by any other authors., (© 2024 Grant et al.)

A single depth camera provides a fast and easy approach to performing biomechanical assessments in a clinical setting; however, there are currently no established methods to reliably determine joint angles from these devices. The primary aim of this study was to compare joint angles as well as the between-day reliability of direct kinematics to model-constrained inverse kinematics recorded using a single markerless depth camera during a range of clinical and athletic movement assessments.A secondary aim was to determine the minimum number of trials required to maximize reliability. Eighteen healthy participants attended two testing sessions one week apart. Tasks included treadmill walking, treadmill running, single-leg squats, single-leg countermovement jumps, bilateral countermovement jumps, and drop vertical jumps. Keypoint data were processed using direct kinematics as well as in OpenSim using a full-body musculoskeletal model and inverse kinematics. Kinematic methods were compared using statistical parametric mapping and between-day reliability was calculated using intraclass correlation coefficients, mean absolute error, and minimal detectable change. Keypoint-derived inverse kinematics resulted in significantly smaller hip flexion (range = -9 to -2°), hip abduction (range = -3 to -2°), knee flexion (range = -5° to -2°), and greater dorsiflexion angles (range = 6-15°) than direct kinematics. Both markerless kinematic methods had high between-day reliability (inverse kinematics ICC 95 %CI = 0.83-0.90; direct kinematics ICC 95 %CI = 0.80-0.93). For certain tasks and joints, keypoint-derived inverse kinematics resulted in greater reliability (up to 0.47 ICC) and smaller minimal detectable changes (up to 13°) than direct kinematics. Performing 2-4 trials was sufficient to maximize reliability for most tasks. A single markerless depth camera can reliably measure lower limb joint angles, and skeletal model-constrained inverse kinematics improves lower limb joint angle reliability for certain tasks and joints., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by an Australian Government Innovation Connections Grant in collaboration with VALD (Brisbane, Australia). DD, GL, and LT are employees of VALD Performance, who produce a commercialized motion capture system that uses a Microsoft Azure Kinect depth camera (“HumanTrak”). MNB is supported by an Advance Queensland Industry Research Fellowship in partnership with VALD, which was awarded after the completion of the present study. The current study does not directly use the HumanTrak system. TC, CP, DL, RB, and MB have been awarded research funding from VALD Performance for additional projects unrelated to this one., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. To assess the capability of the neural network to detect directional changes in HCF resulting from prescribed gait modifications., Design: A calibrated electromyography-informed neuromusculoskeletal model was used to compute lower body joint angles, moments, and HCF for 17 participants with mild-to-moderate hip OA. Anatomical key points (e.g., joint centres) were synthesised from marker trajectories and augmented with bias and noise expected from computer vision-based pose estimation systems. Temporal convolutional and long short-term memory neural networks (NN) were trained using leave-one-subject-out validation to predict neuromusculoskeletal modelling outputs from the synthesised key points and measured electromyography data from 5 hip-spanning muscles., Results: HCF was predicted with an average error of 13.4 ± 7.1% of peak force. Joint angles and moments were predicted with an average root-mean-square-error of 5.3 degrees and 0.10 Nm/kg, respectively. The NN could detect changes in peak HCF that occur due to gait modifications with good agreement with neuromusculoskeletal modelling (r 2 = 0.72) and a minimum detectable change of 9.5%., Conclusion: The developed neural network predicted HCF and lower body joint angles and moments in individuals with hip OA using noisy synthesised key point locations with acceptable errors. Changes in HCF magnitude due to gait modifications were predicted with high accuracy. These findings have important implications for implementation of load-modification based gait retraining interventions for people with hip OA in a natural environment (i.e., home, clinic)., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R 2  = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R 2  = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment., (© 2024. The Author(s).)

Single and dual integrated screw femoral nails are both commonly used to treat intertrochanteric fractures. This study investigated if using single or dual integrated screw femoral nails result in different post-operative hip joint loading. In the presence of differences, we investigated potential contributing factors. Patients were randomised for treatment via single screw (Stryker, Gamma3) or dual-integrated screw nail (Smith and Nephew, Intertan). Pre-injury mobility levels were collected at enrolment. Hip radiographs and gait data were collected at six weeks (Gamma: 16; Intertan: 15) and six months (Gamma: 14; Intertan: 13) follow-up. The resultant hip joint reaction forces and abductor muscle forces were estimated using electromyography-assisted neuromusculoskeletal modelling during level walking gait. Our primary analysis focused on the resultant hip joint reaction force and abductor muscle forces. We compared between groups, across stance phase of walking gait, using statistical parametric mapping. At six weeks, the Intertan group showed a short (∼5% of stance phase) but substantial (33 % [0.3 × body weight] greater magnitude) resultant hip joint reaction force when compared to the Gamma group (P = 0.022). Higher gluteus medius forces (P = 0.009) were demonstrated in the Intertan group at six weeks. Harris Hip Scores followed the trend seen for the biomechanical outcomes with superior scores for the Intertan group at six weeks postoperative (P = 0.044). The use of dual-integrated screw femoral nails over single screw devices may allow for hip biomechanics more closely resembling normal hip function at earlier post-operative timepoints, but these appear to resolve by six months postoperative., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Semi-recumbent cycling performed from a wheelchair is a popular rehabilitation exercise following spinal cord injury (SCI) and is often paired with functional electrical stimulation. However, biomechanical assessment of this cycling modality is lacking, even in unimpaired populations, hindering the development of personalised and safe rehabilitation programs for those with SCI. This study developed a computational pipeline to determine lower limb kinematics, kinetics, and joint contact forces (JCF) in 11 unimpaired participants during voluntary semi-recumbent cycling using a rehabilitation ergometer. Two cadences (40 and 60 revolutions per minute) and three crank powers (15 W, 30 W, and 45 W) were assessed. A rigid body model of a rehabilitation ergometer was combined with a calibrated electromyogram-informed neuromusculoskeletal model to determine JCF at the hip, knee, and ankle. Joint excursions remained consistent across all cadence and powers, but joint moments and JCF differed between 40 and 60 revolutions per minute, with peak JCF force significantly greater at 40 compared to 60 revolutions per minute for all crank powers. Poor correlations were found between mean crank power and peak JCF across all joints. This study provides foundation data and computational methods to enable further evaluation and optimisation of semi-recumbent cycling for application in rehabilitation after SCI and other neurological disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Portable and wearable electronics for biomechanical data collection have become a growing part of everyday life. As smart technology improves and integrates into our lives, some devices remain ineffective, expensive, or difficult to access. We propose a washable iron-on textile pressure sensor for biometric data acquisition. Biometric data, such as human gait, are a powerful tool for the monitoring and diagnosis of ambulance and physical activity. To demonstrate this, our washable iron-on device is embedded into a sock and compared to gold standard force plate data. Biomechanical testing showed that our embedded sensor displayed a high aptitude for gait event detection, successfully identifying over 96% of heel strike and toe-off gait events. Our device demonstrates excellent attributes for further investigations into low-cost, washable, and highly versatile iron-on textiles for specialized biometric analysis.

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part of NMS modelling is the estimation of musculotendon kinematics, which comprise musculotendon unit lengths, moment arms, and lines of action. Musculotendon kinematics, which are partially dependent on joint angles, define the non-linear mapping of muscle forces to joint moments and contact forces. Currently, real-time computation of musculotendon kinematics requires creation of a per-individual surrogate model. The computational speed and accuracy of these surrogates degrade with increasing number of coordinates. We developed a feed-forward neural network that completely encodes musculotendon kinematics of a target model across a wide anthropometric range, enabling accurate real-time estimates of musculotendon kinematics without need for a priori creation of a per-individual surrogate model. Compared to reference, the neural network had median normalized errors ~0.1% for musculotendon lengths, <0.4% for moment arms, and <0.10° for line of action orientations. The neural network was employed within an electromyogram-informed NMS model to calculate hip contact forces, demonstrating little difference (normalized root mean square error 1.23±0.15 %) compared to using reference musculotendon kinematics. Finally, execution time was <0.04 ms per frame and constant for increasing number of model coordinates. Our approach to musculoskeletal kinematics may facilitate deployment of complex real-time NMS modelling in computer vision or wearable sensors applications to realize biomechanics monitoring, rehabilitation, and disease management outside the research laboratory.

Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) ≤ 0.21 , R 2 ≥ 0.81 . Walking task resulted the most accurate with RMSE = 189±62 N; nRMSE = 0.11±0.03 , R 2 = 0.92±0.04 . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.

The purpose of this study was to determine the effect of donor muscle morphology following tendon harvest in anterior cruciate ligament (ACL) reconstruction on muscular support of the tibiofemoral joint during sidestep cutting. Magnetic resonance imaging (MRI) was used to measure peak cross-sectional area (CSA) and volume of the semitendinosus (ST) and gracilis (GR) muscles and tendons (bilaterally) in 18 individuals following ACL reconstruction. Participants performed sidestep cutting tasks in a biomechanics laboratory during which lower-limb electromyography, ground reaction loads, whole-body motions were recorded. An EMG driven neuro-musculoskeletal model was subsequently used to determine force from 34 musculotendinous units of the lower limb and the contribution of the ST and GR to muscular support of the tibiofemoral joint based on a normal muscle-tendon model (Standard model). Then, differences in peak CSA and volume between the ipsilateral/contralateral ST and GR were used to adjust their muscle-tendon parameters in the model followed by a recalibration to determine muscle force for 34 musculotendinous units (Adjusted model). The combined contribution of the donor muscles to muscular support about the medial and lateral compartments were reduced by 52% and 42%, respectively, in the adjusted compared to standard model. While the semimembranosus (SM) increased its contribution to muscular stabilisation about the medial and lateral compartment by 23% and 30%, respectively. This computer simulation study demonstrated the muscles harvested for ACL reconstruction reduced their support of the tibiofemoral joint during sidestep cutting, while the SM may have the potential to partially offset these reductions. This suggests donor muscle impairment could be a factor that contributes to ipsilateral re-injury rates to the ACL following return to sport., Competing Interests: The authors declare that no competing interests exist., (Copyright: © 2023 Konrath et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study implemented an electromyogram (EMG)-informed neuromusculoskeletal (NMS) model evaluating the volitional contributions to muscle forces and joint moments during functional electrical stimulation (FES). The NMS model was calibrated using motion and EMG (biceps brachii and triceps brachii) data recorded from able-bodied participants (n=3) performing weighted elbow flexion and extension cycling movements while equipped with an EMG-controlled closed-loop FES system. Models were executed using three computational approaches (i) EMG-driven, (ii) EMG-hybrid and (iii) EMG-assisted to estimate muscle forces and joint moments. Both EMG-hybrid and EMG-assisted modes were able estimate the elbow moment (root mean squared error and coefficient of determination), but the EMG-hybrid method also enabled quantifying the volitional contributions to muscle forces and elbow moments during FES. The proposed modelling method allows for assessing volitional contributions of patients to muscle force during FES rehabilitation, and could be used as biomarkers of recovery, biofeedback, and for real-time control of combined FES and robotic systems.

There is a powerful global trend toward deeper integration of digital twins into modern life driven by Industry 4.0 and 5.0. Defense, agriculture, engineering, manufacturing, and urban planning sectors have thoroughly incorporated digital twins to great benefit across their respective product lifecycles. Despite clear benefits, a digital twin framework for health and medical sectors is yet to emerge. This paper proposes a digital twin framework for precision neuromusculoskeletal health care. We build upon the International Standards Organization framework for digital twins for manufacturing by presenting best available computational models within a digital twin framework for clinical application. We map a use case for modeling Achilles tendon mechanobiology, highlighting how current modeling practices align with our proposed digital twin framework. Similarly, we map a use case for advanced neurorehabilitation technology, highlighting the role of a digital twin in control of systems where human and machine are interfaced. Future work must now focus on creating an informatic representation to govern how digital data are passed to, from, and within the digital twin, as well as specific standards to declare which measurement systems and modeling methods are acceptable to move toward widespread use of the digital twin framework for precision neuromusculoskeletal health care.

Objectives: The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries., Design: Narrative review., Methods: Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field., Results: Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions., Conclusions: Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products., Competing Interests: Declaration of interest statement All authors disclose that they have no interests to declare with other people or organisations that could inappropriately influence this work. Furthermore, all co-authors have agreed by email to be co-authors on the manuscript., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Medical device regulatory standards are increasingly incorporating computational modelling and simulation to accommodate advanced manufacturing and device personalization. We present a method for robust testing of engineered soft tissue products involving a digital twin paradigm in combination with robotic systems. We developed and validated a digital twin framework for calibrating and controlling robotic-biological systems. A forward dynamics model of the robotic manipulator was developed, calibrated, and validated. After calibration, the accuracy of the digital twin in reproducing the experimental data improved in the time domain for all fourteen tested configurations and improved in frequency domain for nine configurations. We then demonstrated displacement control of a spring in lieu of a soft tissue element in a biological specimen. The simulated experiment matched the physical experiment with 0.09 mm (0.001%) root-mean-square error for a 2.9 mm (5.1%) length change. Finally, we demonstrated kinematic control of a digital twin of the knee through 70-degree passive flexion kinematics. The root-mean-square error was 2.00°, 0.57°, and 1.75° degrees for flexion, adduction, and internal rotations, respectively. The system well controlled novel mechanical elements and generated accurate kinematics in silico for a complex knee model. This calibration method could be applied to other situations where the specimen is poorly represented in the model environment (e.g., human or animal tissues), and the control system could be extended to track internal parameters such as tissue strain (e.g., control knee ligament strain). Further development of this framework can facilitate medical device testing and innovative biomechanics research., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Electromechanical delay (EMD) and maximum isometric muscle force (F o M ) are important parameters for joint contact force calculation with EMG-informed neuromusculoskeletal (NMS) models. These parameters can vary between tasks (EMD) and individuals (EMD and F o M ), making it challenging to establish representative values. One promising approach is to personalise candidate parameters to the participant (e.g., F o M by regression equation) and then adjust all parameters within a calibration (i.e., numerical optimisation) to minimise error between corresponding pairs of experimental measures and model-predicted values. The purpose of this study was to determine whether calibration of an NMS model resulted in consistent joint contact forces, regardless of EMD value or personalisation of F o M . Hip, knee, and ankle contact forces were predicted for 28 participants using EMG-informed NMS models. Differences in joint contact forces with EMD were examined in six models, calibrated with EMD from 15 to 110 ms. Differences in joint contact forces with personalisation of F o M were examined in two models, both calibrated with the same initial EMD (50 ms), one with generic and one with personalised values for F o M . For all models, joint contact force peaks during the first and second halves of stance were extracted and compared using a repeated-measures analysis of variance. Calibrated models with EMD set between 35 and 70 ms produced similar magnitude and timing of peak joint contact forces. Compared with generic values, personalising and then calibrating F o M resulted in comparable peak contact forces at hip, but not knee or ankle, while also producing muscle-specific tensions similar to reported literature. Overall, EMD between 35 and 70 ms and personalised initial values of F o M before calibration are advised for EMG-informed NMS modelling., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The Achilles tendon (AT) is sensitive to mechanical loading, with appropriate strain improving tissue mechanical and material properties. Estimating free AT strain is currently possible through personalized neuromusculoskeletal (NMSK) modeling; however, this approach is time-consuming and requires extensive laboratory data. To enable in-field assessments, we developed an artificial intelligence (AI) workflow to predict free AT strain during running from motion capture data. Ten keypoints commonly used in pose estimation algorithms (e.g., OpenPose) were synthesized from motion capture data and noise was added to represent real-world data obtained using video cameras. Two AI workflows were compared: (1) a Long Short-Term Memory (LSTM) neural network that predicted free AT strain directly (called LSTM only workflow); and (2) an LSTM neural network that predicted AT force which was subsequently converted to free AT strain using a personalized force-strain curve (called LSTM+ workflow). AI models were trained and evaluated using estimates of free AT strain obtained from a validated NMSK model with personalized AT force-strain curve. The effect of using different input features (position, velocity, and acceleration of keypoints, height and mass) on free AT strain predictions was also assessed. The LSTM+ workflow significantly improved the predictions of free AT strain compared to the LSTM only workflow (p < 0.001). The best free AT strain predictions were obtained using positions and velocities of keypoints as well as the height and mass of the participants as input, with average time-series root mean square error (RMSE) of 1.72±0.95% strain and r2 of 0.92±0.10, and peak strain RMSE of 2.20% and r2 of 0.54. In conclusion, we showed feasibility of predicting accurate free AT strain during running using low fidelity pose estimation data.

Neuromusculoskeletal models are a powerful tool to investigate the internal biomechanics of an individual. However, commonly used neuromusculoskeletal models are generated via linear scaling of generic templates derived from elderly adult anatomies and poorly represent a child, let alone children with a neuromuscular disorder whose musculoskeletal structures and muscle activation patterns are profoundly altered. Model personalization can capture abnormalities and appropriately describe the underlying (altered) biomechanics of an individual. In this work, we explored the effect of six different levels of neuromusculoskeletal model personalization on estimates of muscle forces and knee joint contact forces to tease out the importance of model personalization for normal and abnormal musculoskeletal structures and muscle activation patterns. For six children, with and without cerebral palsy, generic scaled models were developed and progressively personalized by (1) tuning and calibrating musculotendon units' parameters, (2) implementing an electromyogram-assisted approach to synthesize muscle activations, and (3) replacing generic anatomies with image-based bony geometries, and physiologically and physically plausible muscle kinematics. Biomechanical simulations of gait were performed in the OpenSim and CEINMS software on ten overground walking trials per participant. A mixed-ANOVA test, with Bonferroni corrections, was conducted to compare all models' estimates. The model with the highest level of personalization produced the most physiologically plausible estimates. Model personalization is crucial to produce physiologically plausible estimates of internal biomechanical quantities. In particular, personalization of musculoskeletal anatomy and muscle activation patterns had the largest effect overall. Increased research efforts are needed to ease the creation of personalized neuromusculoskeletal models., (© 2022. The Author(s).)

Purpose: The magnitude and location of hip contact force influence the local mechanical environment of the articular tissue, driving remodeling. We used a neuromusculoskeletal model to investigate hip contact force magnitudes and their regional loading patterns on the articular surfaces in those with femoroacetabular impingement (FAI) syndrome and controls during walking., Methods: An EMG-assisted neuromusculoskeletal model was used to estimate hip contact forces in eligible participants with FAI syndrome ( n = 41) and controls ( n = 24), walking at self-selected speed. Hip contact forces were used to determine the average and spread of regional loading for femoral and acetabular articular surfaces. Hip contact force magnitude and region of loading were compared between groups using statistical parametric mapping and independent t -tests, respectively ( P < 0.05)., Results: All of the following findings are reported compared with controls. Those with FAI syndrome walked with lower-magnitude hip contact forces (mean difference, -0.7 N·BW -1 ; P < 0.001) during first and second halves of stance, and with lower anteroposterior, vertical, and mediolateral contact force vector components. Participants with FAI syndrome also had less between-participant variation in average regional loading, which was located more anteriorly (3.8°, P = 0.035) and laterally (2.2°, P = 0.01) on the acetabulum but more posteriorly (-4.8°, P = 0.01) on the femoral head. Participants with FAI syndrome had a smaller spread of regional loading across both the acetabulum (-1.9 mm, P = 0.049) and femoral head (1 mm, P < 0.001) during stance., Conclusions: Compared with controls, participants with FAI syndrome walked with lower-magnitude hip contact forces that were constrained to smaller regions on the acetabulum and femoral head. Differences in regional loading patterns might contribute to the mechanobiological processes driving cartilage maladaptation in those with FAI syndrome., (Copyright © 2022 by the American College of Sports Medicine.)

Background: Femoroacetabular impingement syndrome is characterized by chondrolabral damage and hip pain. The specific biomechanics used by people with femoroacetabular impingement syndrome during daily activities may exacerbate their symptoms. Femoroacetabular impingement syndrome can be treated nonoperatively or surgically; however, differential treatment effects on walking biomechanics have not been examined., Purpose: To compare the 12-month effects of physical therapist-led care or arthroscopy on trunk, pelvis, and hip kinematics as well as hip moments during walking., Study Design: Secondary analysis of multi-centre, pragmatic, two-arm superiority randomized controlled trial subsample; Level of evidence, 1., Methods: A subsample of 43 participants from the Australian Full randomised controlled trial of Arthroscopic Surgery for Hip Impingement versus best cONventional (FASHIoN trial) underwent gait analysis and completed the International Hip Outcome Tool (iHOT-33) at both baseline and 12 months after random allocation to physical therapist-led care (personalized hip therapy; n = 22; mean age 35; 41% female) or arthroscopy (n = 21; mean age 36; 48% female). Changes in trunk, pelvis, and hip biomechanics were compared between treatment groups across the gait cycle using statistical parametric mapping. Associations between changes in iHOT-33 and changes in hip kinematics across 3 planes of motion were examined., Results: As compared with the arthroscopy group, the personalized hip therapy group increased its peak hip adduction moments (mean difference = 0.35 N·m/body weight·height [%] [95% CI, 0.05-0.65]; effect size = 0.72; P = .02). Hip adduction moments in the arthroscopy group were unchanged in response to treatment. No other between-group differences were detected. Improvements in iHOT-33 were not associated with changes in hip kinematics., Conclusion: Peak hip adduction moments were increased in the personalized hip therapy group and unchanged in the arthroscopy group. No biomechanical changes favoring arthroscopy were detected, suggesting that personalized hip therapy elicits greater changes in hip moments during walking at 12-month follow-up. Twelve-month changes in hip-related quality of life were not associated with changes in hip kinematics.