Inverse Dynamics Approach Research Articles

Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography

The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain underexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation.

Mechanical power distribution of the lower limbs changed during intermittent 300 countermovement jumps.

The aim of this study was to investigate the effect of 300 intermittent countermovement jumps (CMJs) on the mechanical power distribution at the joints of the lower limbs and the influence of the upper body to explain vertical jump performance. Fifteen male sport students (age 24.5 ± 2.3years; body height 1.85 ± 0.06m; body mass 84.8 ± 8.5kg) performed a set of intermittent 300 CMJs at maximal effort. An inverse-dynamic approach was used to calculate the mechanical power at the hip, knee, and ankle joint for each jump. Jump height and mechanical power in the knee and ankle joints decreased significantly (p < .010), while remained the same in the hip joint. In contrast, a significant increased vertical velocity was observed for the upper body segment. In addition, a significant higher angular momentum at the center of mass was detected during the braking and propulsion phase. The findings highlight a fatigue-related decrease in lower limb power, particularly in the knee and ankle joints, which changed the mechanical power distribution at the joints of the lower limbs. The trunk extensor muscles were probably able to counteract the fatigue-related decrease in lower limb power by increased vertical velocity of the upper body segment and higher angular momentum at the center of mass during the braking and propulsion phase. Accordingly, the most effective way to maintain jumping performance in fatigued state would be to improve the fatigue resistance of the knee extensors, ankle plantar flexors, and trunk extensor muscles.

Adaptive quadrotor control using online dynamic mode decomposition

Precision control of Unmanned Aerial Vehicles (UAVs) is essential for deployment in a wide range of applications. However, real-world flight conditions often deviate from ideal operating scenarios, presenting uncertainties such as external disturbances and unmodeled dynamics. These can dramatically impact tracking accuracy and stability. This study proposes a novel adaptive control technique for quadrotors based on Windowed Dynamic Mode Decomposition (DMDc) techniques. This techniques efficiently identifies dynamic models directly from data, and updates this model in real-time, allowing the controller to compensate for changing conditions. To facilitate realistic validation, the proposed system is integrated within a Hardware-in-the-Loop (HiL) framework. In a series of simulated experiments, the adaptive controller demonstrates improvement in trajectory tracking under disturbances when compared to a conventional inverse dynamics approach. This research underscores the promise of DMDc-based techniques combined with adaptive control to enhance UAV operation, enabling safer and more robust performance in demanding scenarios.

Poster 365: Impact of Graft Type and Meniscal Status on Patellofemoral Cartilage Early after ACL Reconstruction

Objectives: Anterior cruciate ligament reconstruction (ACLR) restores passive knee stability but does not reduce the rate of osteoarthritis (OA). Meniscal status, requiring repair or meniscectomy, is a known component of degenerative progression. Regarding the patellofemoral joint (PFJ) specifically, traditional imaging modalities have shown some evidence that patellar tendon autograft (BPTB) is associated with an increased risk of OA. T2 relaxation time using quantitative MRI (qMRI) is an established method to identify early cartilage changes. However, T2 relaxation in the PFJ as a function of graft type and meniscal status has not been previously evaluated. We aimed to evaluate the effect of autograft type (BPTB, hamstring [HT] and quadriceps tendon [QT]) as well as concomitant meniscal surgery (repair or meniscectomy) at the time of ACLR on T2 relaxation time in PFJ cartilage. We hypothesized that BPTB grafts, concomitant meniscal repair, and meniscectomy would result in higher (worse) T2 relaxation times at 6 months after ACLR. We also evaluated the effect of abnormal joint loading during gait on the PFJ cartilage. We hypothesized that T2 relaxation times would not differ in any group after controlling for knee joint loading during walking. Methods: Thirty-four participants aged 15-35 years were enrolled prospectively within 1 month of ACL injury. Exclusion criteria included prior knee injury or surgery, concomitant grade III tear to other knee ligaments that was symptomatic or required surgical intervention and presence of chondral damage. Sagittal qMRIs were obtained within 1 month of injury (but prior to ACLR) and 6 months after ACLR. Articular cartilage of the PFJ was manually segmented using ITK-SNAP software (Penn Image Computing and Science Laboratory, University of Pennsylvania) and was confirmed for accuracy by a board-certified, fellowship-trained musculoskeletal radiologist (Figure 1). Mean T2 relaxation times were extracted from the trochlear and patellar cartilage. Non-physiological T2 relaxation times less than 10 ms and greater than 90 ms were excluded. Gait biomechanics were captured using two in-ground force plates (Bertec Corporation, Columbus, OH) sampled at 1,080 Hz and retroreflective markers with an 8-camera motion capture system (Qualisys, Göteborg, Sweden) sampled at 120 Hz. Data was processed using Visual 3D software (C-motion, Bethesda, MD). Using an inverse dynamic approach, joint angle and force plate data were used to calculate the knee flexion moment (KFM) impulse normalized to mass and height during the stance phase. Separate one-way ANOVAs were used to evaluate the relationship between graft type and concomitant meniscus surgery with the percent change in T2 relaxation time in PFJ cartilage from baseline to 6 months after ACLR. To determine the influence of gait biomechanics on T2 relaxation time across groups, general linear models were used with the interlimb ratio (involved/uninvolved) of KFM impulse as a covariate. Results: The 34 participants had a mean age of 19.0±4.7 years, and 21 (61.8%) were female. Grafts used during ACLR included 22 BPTB, 4 HT and 8 QT. Seventeen participants (50.0%) had a concomitant meniscus repair and 3 (8.8%) had a meniscectomy. Overall, T2 relaxation time increased 8.7±7.3% in the patellar cartilage (pre-ACLR: 39.1±2.6 ms, 6 months: 42.4±3.6 ms, p&lt;0.001) and 7.1±7.3% in the trochlear cartilage (pre-ACLR: 46.3±2.6 ms, 6 months: 49.5±2.7 ms, p&lt;0.001). The change in T2 relaxation time 6 months after ACLR did not differ based on graft type in either the patellar (p=0.729) or trochlear cartilage (p=0.525) (Figure 2). Group differences remained insignificant after controlling for knee loading (KFM impulse) during gait (patellar: p=0.594; trochlear: p=0.222). Meniscal status was also not associated with a change in T2 relaxation time in either the patellar (p=0.392) or trochlear cartilage (p=0.190) (Figure 3) although there was a trend toward higher T2 relaxation times in those with a concomitant meniscus repair or meniscectomy. Group differences remained statistically insignificant after controlling for the KFM impulse during gait (patellar: p=0.400; trochlear: p=0.197). Conclusions: Our findings provide insufficient evidence that graft type is associated with worse quantitative chondral changes within the PFJ at 6 months following ACLR. While meniscal status is a known risk factor for increased contact pressure in the involved compartment and subsequent degenerative progression, our data does not demonstrate statistically significant changes to the PFJ cartilage when meniscal tears were present, likely due to the limited sample size and large variation within groups. Future work with larger cohorts is needed to confirm our findings and further investigate the role that graft type and meniscal status may play within the PFJ after ACLR. [Figure: see text][Figure: see text][Figure: see text]

Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers

BackgroundUnderstanding joint loading and the crucial role of joint moments is essential for developing treatment strategies in gait analysis, which often requires the precise estimation of joint moments through an inverse dynamic approach. This process necessitates the use of a force plate synchronized with a motion capture system. However, effectively capturing ground reaction force in typically developing (TD) children and those with congenital talipes equino varus (CTEV) presents challenges, while the availability and high cost of additional force plates pose additional challenges. Therefore the study aimed to develop, train, and identify the most effective machine learning (ML) model to predict joint moments from kinematics for TD children and those with CTEV. MethodIn a study at the Gait Lab, 13 children with bilateral CTEV and 17 TD children underwent gait analysis to measure kinematics and kinetics, using a 12-camera Qualisys Motion Capture System and an AMTI force plate. ML models were then trained to predict joint moments from kinematic data as input. ResultsThe random forest regressor and deep neural networks (DNN) proved most effective in predicting joint moments from kinematics for TD children, yielding better results. The Random Forest regressor achieved an average r of 0.75 and nRMSE of 23.03 % for TD children, and r of 0.74 and 23.82 % for CTEV. DNN achieved an average r of 0.75 and nRMSE of 22.83 % for TD children, and r of 0.76 and nRMSE of 23.9 % for CTEV. ConclusionsThe findings suggest that using machine learning to predict joint moments from kinematics shows moderate potential as an alternative to traditional gait analysis methods for both TD children and those with CTEV. Despite its potential, the current prediction accuracy limitations hinder the immediate clinical application of these techniques for decision-making in a pediatric population.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

Implementing an accelerometer-based pelvis segment for low back kinetic analyses during dynamic movement tasks

An accelerometer-based pelvis has been employed to study segment and joint kinematics during scenarios involving close human-object interface and/or line-of-sight obstructions. However, its accuracy for examining low back kinetic outcomes is unknown. This study compared reaction moments and contact forces of the L5S1 joint calculated with an accelerometer-based and optically tracked pelvis segment. An approach to correct the global pelvis position as a function of thigh angle was developed. One participant performed four dynamic tasks: forward bend, squat, sit-to-stand-to-sit, and forward lunge. A standard bottom-up inverse dynamics approach was used and the root mean square error (RMSE) and coefficient of determination (R2) were calculated to examine kinetic differences between the optical and accelerometer approaches. The RMSE observed for L5S1 reaction flexion-extension moments ranged from 1.32 Nm to 2.20 Nm (R2 ≥ 0.98). The RMSE for net shear and compression reaction forces ranged from 2.13 to 10.45 N and 0.63 – 4.96 N, respectively. Similarly, the RMSE for L5S1 joint contact shear and compression ranged from 13.45 N to 19.51 N (R2 ≥ 0.85) and 31.18 N – 55.97 N (R2 ≥ 0.97), respectively. In conclusion, the accelerometer-based pelvis together with the approach to correct the global pelvis position is a feasible approach for computing low back kinetics with a single equivalent muscle model. The observed error in joint contact forces represents less than 5 % of the NIOSH recommended action limits and is unlikely to alter the interpretation of low back injury risk.

How mechanics of individual muscle-tendon units define knee and ankle joint function in health and cerebral palsy-a narrative review.

This study reviews the relationship between muscle-tendon biomechanics and joint function, with a particular focus on how cerebral palsy (CP) affects this relationship. In healthy individuals, muscle size is a critical determinant of strength, with muscle volume, cross-sectional area, and moment arm correlating with knee and ankle joint torque for different isometric/isokinetic contractions. However, in CP, impaired muscle growth contributes to joint pathophysiology even though only a limited number of studies have investigated the impact of deficits in muscle size on pathological joint function. As muscles are the primary factors determining joint torque, in this review two main approaches used for muscle force quantification are discussed. The direct quantification of individual muscle forces from their relevant tendons through intraoperative approaches holds a high potential for characterizing healthy and diseased muscles but poses challenges due to the invasive nature of the technique. On the other hand, musculoskeletal models, using an inverse dynamic approach, can predict muscle forces, but rely on several assumptions and have inherent limitations. Neither technique has become established in routine clinical practice. Nevertheless, identifying the relative contribution of each muscle to the overall joint moment would be key for diagnosis and formulating efficient treatment strategies for patients with CP. This review emphasizes the necessity of implementing the intraoperative approach into general surgical practice, particularly for joint correction operations in diverse patient groups. Obtaining in vivo data directly would enhance musculoskeletal models, providing more accurate force estimations. This integrated approach can improve the clinicians' decision-making process and advance treatment strategies by predicting changes at the muscle and joint levels before interventions, thus, holding the potential to significantly enhance clinical outcomes.

Energy transfer in reactive movements as a function of individual stretch load

Background: By directly recording electromyographic activity profiles and muscle-tendon interaction, this study aimed to elucidate the mechanisms why well-trained track and field athletes (experts) are able to outperform untrained individuals without former systematic experience in reactive jump training (novices). In particular, reactive power output and the elastic recoil properties of the muscle-tendon unit (MTU) were of special interest. For this purpose, stiffness regulation on muscle and joint level, energy management in terms of storing or dissipating elastic energy were compared between experts and novices during various stretch loads.Methods: Experts were compared with novices during reactive drop jumps (DJs) from drop heights ranging between 25 and 61 cm. Delta kinetic energy (Ekin) was calculated as the difference between the Ekin at take-off and ground contact (GC) to determine energy management. By recording electromyography of the lower limb muscles, in vivo fascicle dynamics (gastrocnemius medialis) and by combining kinematics and kinetics in a 3D inverse dynamics approach to compute ankle and knee joint kinetics, this study aimed to compare reactive jump performance, the neuromuscular activity and muscle-tendon interaction between experts and novices among the tested stretch loads.Results: Experts demonstrated significantly higher power output during DJs. Among all drop heights experts realized higher delta Ekin compared to novices. Consequently, higher reactive jump performance shown for experts was characterized by shorter GC time (GCT), higher jump heights and higher neuromuscular activity before and during the GC phase compared to novices. Concomitantly, experts were able to realize highest leg stiffness and delta Ekin in the lowest stretch load; however, both groups compensated the highest stretch load by prolonged GCT and greater joint flexion. On muscle level, experts work quasi-isometrically in the highest stretch load, while in novices GM fascicles were forcefully stretched.Conclusion: Group-specific stiffness regulation and elastic recoil properties are primarily influenced by the neuromuscular system. Due to their higher neuromuscular activity prior and during the GC phase, experts demonstrate higher force generating capacity. A functionally stiffer myotendinous system through enhanced neuromuscular input enables the experts loading their elastic recoil system more efficiently, thus realizing higher reactive power output and allowing a higher amount of energy storage and return. This mechanism is regulated in a stretch load dependent manner.

Common modelling assumptions affect the joint moments measured during passive joint mobilizations

Joint resistance to passive mobilization has already been estimated in-vivo in several studies by measuring the applied forces and moments while manipulating the joint. Nevertheless, in most of the studies, simplified modelling approaches are used to calculate this joint resistance. The impact of these simplifications is still unknown. We propose a protocol that enables a reference 3D inverse dynamics approach to be implemented and compared to common simplified approaches. Eight typically developed children and eight children with cerebral palsy were recruited and underwent a passive testing protocol, while applied forces and moments were measured through a 3D handheld dynamometer, simultaneously to its 3D kinematics and the 3D kinematics of the different segments. Then, passive joint resistance was estimated using the reference 3D inverse dynamics approach and according to 5 simplified approaches found in the literature, i.e. ignoring either the dynamometer kinematics, the measured moments alone or together with the measured tangential forces, the gravity and the inertia of the different segments, or the distal segments kinematics. These simplifications lead to non-negligible differences with respect to the reference 3D inverse dynamics, from 3 to 32% for the ankle, 4 to 34% for the knee and 1 to 58% for the hip depending of the different simplifications. Finally, we recommend a complete 3D kinematics and dynamics modelling to estimate the joint resistance to passive mobilization.

Lower extremity joint power and work during recovery following trip-induced perturbations

BackgroundSuccessful recovery following a perturbation during walking depends on a quick well-coordinated response from the body. As such, lower limb joint power and work provide critical information characterizing the success of the recovery after a perturbation. Therefore, this study aimed to investigate lower-limb joint power and the relative contribution of each joint to the total leg work during the recovery following a trip-induced perturbation. MethodsTwenty-four young male volunteers walked at 1.1 m/s for 2 min, followed by two unexpected perturbations induced by rapidly decelerating the right belt of the split-belt treadmill. Joint moments and powers were calculated using an inverse dynamic approach. Joint work was found as the integral of joint power with respect to time. Statistical parametric mapping (SPM) and paired-sample t-tests were used to compare joint power and work between recovery and unperturbed steps. ResultsCompared to normal walking, recovery from the trip required a significant increase in both positive (+27 %, p < 0.05) and negative(+28 %,p < 0.05) leg work. During unperturbed walking, the ankle was the key contributor to both positive (ankle=50 %, hip=34 %, and knee=15 %) and negative (ankle=62 %, knee=32 %, and hip=6 %) leg work. During recovery, the knee eccentric work significantly increased (+83 %,p < 0.05) making it the main contributor to the negative leg work (knee=46 %, ankle=45 %, and hip=9 %). The hip positive work also increased during recovery (+62.7 %, p < 0.05), while ankle and the knee positive work remained unchanged. SignificanceThese findings highlight the importance of eccentric work of the knee, and concentric work of the hip joint during recovery from trip-induced perturbations. The additional mechanical demand of producing and absorbing more power during recovery is primarily imposed on the knee and hip, rather than the ankle. This new insight into the specific functions of lower-limb joints during recovery from trip-induced perturbations has important implications for the design of targeted fall prevention interventions.

Antigravity muscle efforts during walking determined using an inverse dynamics approach

Antigravity muscle efforts during walking determined using an inverse dynamics approach

Biomechanical effects of typical lower limb movements of Chen-style Tai Chi on knee joint.

The load and stress distribution on cartilage and meniscus of the knee joint in typical lower limb movements of Chen-style Tai Chi (TC) and deep squat (DS) were analyzed using finite element (FE) analysis. The loadings for this analysis consisted of muscle forces and ground reaction force (GRF), which were calculated through the inverse dynamic approach based on kinematics and force plate measurements obtained from motion capture experiments. Thirteen experienced practitioners performed four typical TC movements, namely, single whip (SW), brush knee and twist step (BKTS), stretch down (SD), and part the wild horse's mane (PWHM), which exhibit lower posture and greater lower limb force compared to other TC styles. The results indicated that TC required greater lower limb muscle strength than DS, resulting in greater knee joint forces. The stress on the medial cartilage in SW and BKTS fell within a range conductive to maintaining the balance between anabolism and catabolism of cartilage matrix. This was due to the fact that SW and BKTS reduce the medial to total tibiofemoral contact force ratios through knee abduction, which may effectively alleviate mild medial knee osteoarthritis (KOA). However, the greater medial contact force ratios observed in SD and PWHM resulted in great contact stresses that may aggravate the pain of patients with KOA. To mitigate these effects, practitioners should consider elevating their postures appropriately to reduce knee flexion angles, especially during the single-leg support phase. This adjustment can decrease the required muscle strength, load and stress on the knee joint.

Estimation of lower limb joint moments based on the inverse dynamics approach: a comparison of machine learning algorithms for rapid estimation.

The aim of this study is to estimate the joint moments of the ankle, knee, and hip joints during walking. A sit-to-stand (STS) movement analysis was first performed on 20 participants with different anthropometric characteristics. Then, analysis of the dynamics of the STS motion was used to develop a biomechanical model. Decision tree (DT), linear regression (LR), support vector machine (SVM), random forest (RF), and three deep learning (DL) algorithms and deep neural network (DNN), long-short-term memory (LSTM), and convolutional neural network (CNN) are examined in this work to estimate three joint moments: ankle, knee, and hip. The results of the seven algorithms were evaluated using four statistical benchmarks: MSR, RMSE, correlation coefficient (R), and MAE to find the most accurate one. The results show that the most successful algorithms were LSTM in estimating knee, hip, and ankle joint moments using 19 and 7 inputs. The R value was 0.9990 using 19 inputs and 0.9972 using 7 inputs. The other algorithms have a correlation coefficient (R) success of 0.9902, 0.9770, 0.9884, 0.9577, 0.9786, and 0.9022 for RF, CNN, DT, DNN, SVM, and LR, respectively. The prediction of joint moments plays a crucial role in the design of the biomechanical system with the desired mechanical properties. Especially, the need has arisen to predict joint moments in a shorter time to utilize in real-time active prosthesis/orthosis controllers.

VALIDATION OF A DYNAMIC MODEL OF THE NECK FOR APPLICATIONS IN ERGONOMICS AND FUNCTIONAL ASSESSMENT

A dynamic neck model is proposed for functional assessment or ergonomic studies using data obtained from conventional biomechanical tools such as video photogrammetry and force platforms. The head and neck inertial parameters are obtained through regression equations and refined through a calibration process to improve accuracy. Head movement is recorded through video photogrammetry, where marker coordinates are used to calculate finite displacements, linear and angular velocities, and accelerations. An inverse dynamics approach is used to estimate the forces and moments at the C7 vertebral level and the generated muscle power. The model was validated through an experimental study with 30 participants, where its estimates were compared to the measurements obtained from a force platform. The comparison aimed to assess the accuracy and reliability of the model. The results show excellent agreement, with a correlation of 0.976 or higher and a standard error of less than 1% of the head weight Keywords: Biomechanics, biomechanical model, neck, ergonomics, functional assessment

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network.

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

Walking in high-heel shoes induces redistribution of joint power and work

ABSTRACT Walking in high-heel shoes (HHS) decreases the push-off power and little research has examined the specific muscle groups that compensate for it. The purpose was to examine the effects of walking in HHS compared to barefoot on lower extremity net joint work and power. Fourteen young women walked in HHS and barefoot at a fixed speed of 1.3 m·s−1. Marker position and ground reaction force data were synchronously measured at 100 and 1000 Hz, respectively. Peak power and joint work variables were computed over the power phases of the gait cycle using an inverse dynamic approach. When walking in HHS was compared to barefoot, participants exerted a diminished push-off characterized by lesser peak power and lesser work by the ankle plantar flexors in late stance (A2 phase; p < 0.001). To compensate for the reduced ankle plantar flexor power, greater peak power was generated and work was performed in early stance by hip extensors (H1 phase; p ≤ 0.001), in mid-stance by knee extensors (K2 phase; p < 0.001) and in late stance and early swing phase by hip flexor muscles (H3 phase; p ≤ 0.001). Walking in HHS induces biomechanical plasticity and causes distal-to-proximal redistribution of net joint power and work during walking.

Subject-specific tribo-contact conditions in total knee replacements: a simulation framework across scales.

Fundamental knowledge about invivo kinematics and contact conditions at the articulating interfaces of total knee replacements are essential for predicting and optimizing their behavior and durability. However, the prevailing motions and contact stresses in total knee replacements cannot be precisely determined using conventional invivo measurement methods. Insilico modeling, in turn, allows for a prediction of the loads, velocities, deformations, stress, and lubrication conditions across the scales during gait. Within the scope of this paper, we therefore combine musculoskeletal modeling with tribo-contact modeling. In the first step, we compute contact forces and sliding velocities by means of inverse dynamics approach and force-dependent kinematic solver based upon experimental gait data, revealing contact forces during healthy/physiological gait of young subjects. In a second step, the derived data are employed as input data for an elastohydrodynamic model based upon the finite element method full-system approach taking into account elastic deformation, the synovial fluid's hydrodynamics as well as mixed lubrication to predict and discuss the subject-specific pressure and lubrication conditions.

Optical axis control system invariant to the character of disturbance

The task of synthesizing an optimal system of automatic control of the optical axis of a camera with guaranteed accuracy, which is invariant to the nature of disturbances, is considered. Disturbances in this context can be different and not limited. The process of creating a mathematical model of the control system and solving the mathematical synthesis problem using the inverse dynamic model approach is shown. The results of the study demonstrate the mechanism of ensuring the quality of the automatic control system during the compensation of disturbances, and also indicate the features of the system structure. A special method for creating the corrective effect necessary to compensate for disturbances is discussed in the context of the approach of the state variable of the automatic control system to the limit of permissible values. An important result is the discovery of a new mathematical structure - an algebraic-differential equation (for a system with one degree of freedom) or a system of such equations (for a system with several degrees of freedom), which corresponds to this structure of the automatic control system.

Estimating 3D ground reaction forces in running using three inertial measurement units.

To understand the mechanisms causing running injuries, it is crucial to get insights into biomechanical loading in the runners' environment. Ground reaction forces (GRFs) describe the external forces on the body during running, however, measuring these forces is usually only possible in a gait laboratory. Previous studies show that it is possible to use inertial measurement units (IMUs) to estimate vertical forces, however, forces in anterior-posterior direction play an important role in the push-off. Furthermore, to perform an inverse dynamics approach, for modelling tissue specific loads, 3D GRFs are needed as input. Therefore, the goal of this work was to estimate 3D GRFs using three inertial measurement units. Twelve rear foot strike runners did nine trials at three different velocities (10, 12 and 14 km/h) and three stride frequencies (preferred and preferred ± 10%) on an instrumented treadmill. Then, data from IMUs placed on the pelvis and lower legs were used as input for artificial neural networks (ANNs) to estimate 3D GRFs. Additionally, estimated vertical GRF from a physical model was used as input to create a hybrid machine learning model. Using different splits in validation and training data, different ANNs were fitted and assembled into an ensemble model. Leave-one-subject-out cross-validation was used to validate the models. Performance of the machine learning, hybrid machine learning and a physical model were compared. The estimated vs. measured GRF for the hybrid model had a RMSE normalized over the full range of values of 10.8, 7.8 and 6.8% and a Pearson correlation coefficient of 0.58, 0.91, 0.97 for the mediolateral direction, posterior-anterior and vertical direction respectively. Performance for the three compared models was similar. The ensemble models showed higher model accuracy compared to the ensemble-members. This study is the first to estimate 3D GRF during continuous running from IMUs and shows that it is possible to estimate GRF in posterior-anterior and vertical direction, making it possible to estimate these forces in the outdoor setting. This step towards quantification of biomechanical load in the runners' environment is helpful to gain a better understanding of the development of running injuries.