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Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

Background The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. Methods We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time. Results IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60–0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (− 0.14–0.17 deg/min). Conclusions Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

Calibration of neuromusculoskeletal models using functional tasks is performed to calculate subject-specific musculotendon parameters, as well as coefficients describing the shape of muscle excitation and activation functions. The objective of the present study was to employ a neuromusculoskeletal model of the shoulder driven entirely from muscle electromyography (EMG) to quantify the influence of different model calibration strategies on muscle and joint force predictions. Three healthy adults performed dynamic shoulder abduction and flexion, followed by calibration tasks that included reaching, head touching as well as active and passive abduction, flexion and axial rotation, and submaximal isometric abduction, flexion and axial rotation contractions. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using subject-specific EMG-driven neuromusculoskeletal models that were uncalibrated and calibrated using (i) all calibration tasks (ii) sagittal plane calibration tasks, and (iii) scapular plane calibration tasks. Joint forces were compared to published instrumented implant data. Calibrating models across all tasks resulted in glenohumeral joint force magnitudes that were more similar to instrumented implant data than those derived from any other model calibration strategy. Muscles that generated greater torque were more sensitive to calibration than those that contributed less. This study demonstrates that extensive model calibration over a broad range of contrasting tasks produces the most accurate and physiologically relevant musculotendon and EMG-to-activation parameters. This study will assist in development and deployment of subject-specific neuromusculoskeletal models.

Upper limb muscle force estimation using Hill-type muscle models depends on musculotendon parameter values, which cannot be readily measured non-invasively. Generic and scaled-generic parameters may be quickly and easily employed, but these approaches do not account for an individual subject’s joint torque capacity. The objective of the present study was to develop a subject-specific experimental testing and modeling framework to evaluate shoulder muscle and joint function during activities of daily living, and to assess the capacity of generic and scaled-generic musculotendon parameters to predict muscle and joint function. Three-dimensional musculoskeletal models of the shoulders of 6 healthy subjects were developed to calculate muscle and glenohumeral joint loading during abduction, flexion, horizontal flexion, nose touching and reaching using subject-specific, scaled-generic and generic musculotendon parameters. Muscle and glenohumeral joint forces calculated using generic and scaled-generic models were significantly different to those of subject-specific models (p<0.05), and task dependent; however, scaled-generic model calculations of shoulder glenohumeral joint force demonstrated better agreement with those of subject-specific models during abduction and flexion. Muscles in generic musculoskeletal models operated further from the plateau of their force–length curves than those of scaled-generic and subject-specific models, while muscles in subject-specific models operated over a wider region of their force length curves than those of the generic or scaled-generic models, reflecting diversity of subject shoulder strength. The findings of this study suggest that generic and scaled-generic musculotendon parameters may not provide sufficient accuracy in prediction of shoulder muscle and joint loading when compared to models that employ subject-specific parameter-estimation approaches.

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models.

Creep in the viscoelastic tissues of the lumbar spine reduces the force-producing capability of these tissues. This study aimed to explore the impact of passive tissue creep on lumbar biomechanics. Sixteen participants performed controlled sagittally symmetric trunk flexion motions after a 30-minute protocol consisting of 12 min of full trunk flexion and 18 min of upright standing. Trunk kinematics and EMG activities of trunk muscles were captured as input variables in three biomechanical models: a) EMG-assisted model with no passive tissue (Active), b) EMG-assisted model with time-invariant passive tissue (No-Creep), and c) EMG-assisted model with time-variant passive tissue components (Creep). The mean absolute error (MAE) between the external moment and the estimated internal moment was calculated as a function of model type and trunk flexion. Results revealed no significant difference in MAE between the three models at 0-30° trunk flexion but as the angle exceeded 30°, the MAE of the No-Creep and Creep models were significantly smaller than that of the Active model. Beyond thetrunk flexion angle of flexion-relaxation of erector spinae muscles, the MAE of the Creep model was significantly smaller than that of the No-Creep model (21.8 Nm vs. 40.3 Nm), leading to reduced compression and shear forces of the L4/L5 disc by 784.7 N (31.7 %) and 280.6 N (21.6 %) at full flexion. These results indicate the modulation of the time-dependent stiffness of passive tissues led to amore accurate prediction of the net internal moment at near full flexion postures, preventing overestimation of spinal loads.

Static optimization is commonly employed in musculoskeletal modeling to estimate muscle and joint loading; however, the ability of this approach to predict antagonist muscle activity at the shoulder is poorly understood. Antagonist muscles, which contribute negatively to a net joint moment, are known to be important for maintaining glenohumeral joint stability. This study aimed to compare muscle and joint force predictions from a subject-specific neuromusculoskeletal model of the shoulder driven entirely by measured muscle electromyography (EMG) data with those from a musculoskeletal model employing static optimization. Four healthy adults performed six sub-maximal upper-limb contractions including shoulder abduction, adduction, flexion, extension, internal rotation and external rotation. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using both a calibrated EMG-driven neuromusculoskeletal modeling framework, and musculoskeletal model simulations that employed static optimization. The EMG-driven model predicted antagonistic muscle function for pectoralis major, latissimus dorsi and teres major during abduction and flexion; supraspinatus during adduction; middle deltoid during extension; and subscapularis, pectoralis major and latissimus dorsi during external rotation. In contrast, static optimization neural solutions showed little or no recruitment of these muscles, and preferentially activated agonistic prime movers with large moment arms. As a consequence, glenohumeral joint force calculations varied substantially between models. The findings suggest that static optimization may under-estimate the activity of muscle antagonists, and therefore, their contribution to glenohumeral joint stability.

In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached inertial measurement units (IMUs). This requires the relative poses of the IMUs w.r.t. the segments—the IMU-to-segment calibrations, subsequently called I2S calibrations—to be known. Since calibration methods based on static poses, movements and manual measurements are still the most widely used, potentially large human-induced calibration errors have to be expected. This work compares three newly developed/adapted extended Kalman filter (EKF) and optimization-based sensor fusion methods with an existing EKF-based method w.r.t. their segment orientation estimation accuracy in the presence of model calibration errors with and without using magnetometer information. While the existing EKF-based method uses a segment-centered kinematic chain biomechanical model and a constant angular acceleration motion model, the newly developed/adapted methods are all based on a free segments model, where each segment is represented with six degrees of freedom in the global frame. Moreover, these methods differ in the assumed motion model (constant angular acceleration, constant angular velocity, inertial data as control input), the state representation (segment-centered, IMU-centered) and the estimation method (EKF, sliding window optimization). In addition to the free segments representation, the optimization-based method also represents each IMU with six degrees of freedom in the global frame. In the evaluation on simulated and real data from a three segment model (an arm), the optimization-based method showed the smallest mean errors, standard deviations and maximum errors throughout all tests. It also showed the lowest dependency on magnetometer information and motion agility. Moreover, it was insensitive w.r.t. I2S position and segment length errors in the tested ranges. Errors in the I2S orientations were, however, linearly propagated into the estimated segment orientations. In the absence of magnetic disturbances, severe model calibration errors and fast motion changes, the newly developed IMU centered EKF-based method yielded comparable results with lower computational complexity.

In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human–robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

To better understand stress-related foot ulceration in diabetes, the cumulative plantar tissue stress (CPTS) should be quantified accurately, but also feasibly for clinical use. We developed multiple CPTS models with varying complexity and investigated their agreement with the most comprehensive reference model available. We assessed 52 participants with diabetes and high foot ulcer risk for barefoot and in-shoe plantar pressures during overground walking at different speeds, standing, sit-to-stand transitions, and stair walking. Level of these weight-bearing activities along with footwear adherence were objectively measured over seven days. The reference CPTS-model included the pressure–time integrals of each walking stride (barefoot and shod), specified for speed; standing period (barefoot and shod); transition and stair walking stride. We compared four CPTS-models with increasing number of input parameters (models 1–4) with the reference model, using repeated measures ANOVA, Pearson’s correlation and Bland-Altman plots. For the clinically-relevant metatarsal 1 region, calculated CPTS was lower for the four CPTS-models compared to reference (Δ770, Δ466, Δ24 and Δ12 MPa.s/day, respectively). CPTS associated moderately with the reference model for model 1 (r = 0.551) and very strongly for models 2–4 (r ≥ 0.937). Limits of agreement were large for models 1 and 2 (−728;2269 and −302;1233 MPa.s/day), and small for models 3 and 4 (−43;92 and −54;78 MPa.s/day). CPTS in models 3 and 4 best agreed with the reference model, where model 3 required fewer parameters, i.e., pressure–time integrals of each walking stride and standing period while barefoot and shod. These parameters need to be included for accurate and feasible CPTS assessment.