Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis.

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 (r2 = 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).

A Digital Twin Framework for Precision Neuromusculoskeletal Health Care: Extension Upon Industrial Standards.

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.

Electromyography-Assisted Neuromusculoskeletal Models Can Estimate Physiological Muscle Activations and Joint Moments Across the Neck Before Impacts.

Knowledge of neck muscle activation strategies before sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations before impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the calibrated EMG-informed neuromusculoskeletal modeling toolbox and three neural solutions were compared: (i) static optimization (SO), (ii) EMG-assisted (EMGa), and (iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p < 0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14, and 2.32 N·m) but not in lateral bending (RMSE = 1.07, 2.07, and 0.84 N·m). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental cocontractions significantly (p < 0.01) outperforming SO, which was characterized by saturation and nonphysiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria before impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

Muscle contributions to tibiofemoral shear forces and valgus and rotational joint moments during single leg drop landing.

Anterior cruciate ligament (ACL) injuries commonly occur during single-leg landing tasks and are a burdensome condition. Previous studies indicate that muscle forces play an important role in controlling ligamentous loading, yet these studies have typically used cadaveric models considering only the knee-spanning quadriceps, hamstrings, and gastrocnemius muscle groups. Any muscles (including non-knee-spanning muscles) capable of opposing the anterior shear joint reaction force and the valgus joint reaction moment are thought to have the greatest potential for protecting the ACL from injury. Thus, the purpose of this study was to investigate how lower-limb muscles modulate knee joint loading during a single-leg drop landing task. An electromyography-informed neuromusculoskeletal modeling approach was used to compute lower-limb muscle force contributions to the anterior shear joint reaction force and the valgus joint reaction moment at the knee during a single-leg drop landing task. The average shear joint reaction force ranged from 153 N of anterior shear force to 744 N of posterior shear force. The muscles that generated the greatest posterior shear force were the soleus, medial hamstrings, and biceps femoris, contributing up to 393 N, 359 N, and 162 N, respectively. The average frontal plane joint reaction moment ranged from a 19 Nm varus moment to a 6 Nm valgus moment. The valgus moment was primarily opposed by the gluteus medius, gluteus minimus, and soleus, with these muscles providing contributions of up to 38, 22, and 20 Nm toward a varus moment, respectively. The findings identify key muscles that mitigate loads on the ACL.

Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers.

Wearable sensors and motion capture technology are accepted instruments to measure spatiotemporal variables during punching performance and to study the externally observable effects of fatigue. This study aimed to develop a computational framework enabling three-dimensional inverse dynamics analysis through the tracking of punching kinematics obtained from inertial measurement units and uniplanar videography. The framework was applied to six elite male boxers performing a boxing-specific punch fatigue protocol. OpenPose was used to label left side upper-limb landmarks from which sagittal plane kinematics were computed. Custom-made inertial measurement units were embedded into the boxing gloves, and three-dimensional punch accelerations were analyzed using statistical parametric mapping to evaluate the effects of both fatigue and laterality. Tracking simulations of a sub-set of left-handed punches were formulated as optimal control problems and converted to nonlinear programming problems for solution with a trapezoid collocation method. The laterality analysis revealed the dominant side fatigued more than the non-dominant, while tracking simulations revealed shoulder abduction and elevation moments increased across the fatigue protocol. In future, such advanced simulation and analysis could be performed in ecologically valid contexts, whereby multiple inertial measurement units and video cameras might be used to model a more complete set of dynamics.

Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion.

This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during overground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing 1) the elastic interplay between the knee and ankle joints, 2) the individual muscle contribution to joint stiffness, and 3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions, including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion.

Hip biomechanics in early recovery following fixation of intertrochanteric fractures: Results from a randomised controlled trial.

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.

Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy.

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, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 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.

Joint contact forces during semi-recumbent seated cycling.

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.

Flexible Iron-On Sensor Embedded in Smart Sock for Gait Event Detection.

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.

Inclusion of a skeletal model partly improves the reliability of lower limb joint angles derived from a markerless depth camera.

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.

Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video.

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.

Squatting biomechanics following physiotherapist-led care or hip arthroscopy for femoroacetabular impingement syndrome: a secondary analysis from a randomised controlled trial.

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.

Neuromusculoskeletal model calibration accounts for differences in electromechanical delay and maximum isometric muscle force.

Electromechanical delay (EMD) and maximum isometric muscle force (FoM) 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 FoM), making it challenging to establish representative values. One promising approach is to personalise candidate parameters to the participant (e.g., FoM 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 FoM. 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 FoM were examined in two models, both calibrated with the same initial EMD (50 ms), one with generic and one with personalised values for FoM. 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 FoM 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 FoM before calibration are advised for EMG-informed NMS modelling.

A digital twin framework for robust control of robotic-biological systems.

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.

EMG-Informed Neuromusculoskeletal Modelling Estimates Muscle Forces and Joint Moments During Electrical Stimulation.

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.

Predicting Free Achilles Tendon Strain From Motion Capture Data Using Artificial Intelligence.

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.

Hamstring harvest results in significantly reduced knee muscular protection during side-step cutting two years after anterior cruciate ligament reconstruction.

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.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision.

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.