EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling

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.

Case report: Movement analysis in oncological rehabilitation: proposal of a kinematic and surface electromyographic protocol in breast oncology

Introduction

Breast cancer disease often affects the ipsilateral shoulder joint, with pain and joint limitation. Proper pain management, which can be obtained using, for example, pulsed radiofrequency of the suprascapular nerve, can help the physiotherapist mitigate patient pain. The modern technologies of kinematic analysis and surface electromyography of movement analysis can give further support in building a personalized rehabilitation program, based on the quantitative study of movement, in this case of the upper limb.

Methods

A brief case report was conceived to develop and test the evolution of a shoulder joint analysis protocol based on an inertial accelerometer and non-invasive surface electromyography.

Results

An analysis algorithm was defined to adapt to the needs of patients operated on at the breast based on a kinematic component (ROM - range of movement - and Jerk index) and an electromyographic one (study of muscle behavior in groups of four). The coactivations were also evaluated, both as an average value and in graphical form, to offer the physiotherapist a complete overview of the movement of the upper limb.

Discussion

The promising protocol results underline its strengths, including the simplicity of use, combined with the reduced time required for processing the reports and the portability of the PC-sensors complex, making these analyses potentially valuable for patient care.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

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.

Latarjet's muscular alterations increase glenohumeral joint stability: A theoretical study

The surgical Latarjet procedure aims to stabilise the glenohumeral joint following anterior dislocations. Despite restoring joint stability, the procedure introduces alterations of muscle paths which likely modify the shoulder dynamics. Currently, these altered muscular functions and their implications are unclear. Hence, this work aims to predict changes in muscle lever arms, muscle and joint forces following a Latarjet procedure by using a computational approach. Planar shoulder movements of ten participants were experimentally assessed. A validated upper-limb musculoskeletal model was utilised in two configurations, i.e., a baseline model, simulating normal joint, and a Latarjet model simulating its related muscular alterations. Muscle lever arms and differences in muscle and joint forces between models were derived from the experimental marker data and static optimisation technique. Lever arms of most altered muscles, hence their role, were substantially changed after Latarjet. Altered muscle forces varied by up to 15% of the body weight. Total glenohumeral joint force increased by up to 14% of the body weight after Latarjet, mostly due to increase in compression force. Our simulation indicated that the Latarjet muscular alterations lead to changes in the muscular recruitment and contribute to the stability of the glenohumeral joint by increasing compression force during planar motions.

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.

The effectiveness of EMG-driven neuromusculoskeletal model calibration is task dependent

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.

The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries

This paper explores the use of biomechanics in identifying the mechanistic causes of musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been used to develop training programmes aiming to maintain or recover tissue health. Tissue health depends on the functional mechanical environment experienced by tissues during daily and rehabilitation activities. These environments are the result of the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing musculoskeletal tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and deformation), which may be enabled by appropriate real-time biofeedback. Recent research shows that biofeedback technologies may increase their quality and effectiveness by integrating a personalised neuromusculoskeletal modelling driven by real-time motion capture and medical imaging. Model personalisation is crucial in obtaining physically and physiologically valid predictions of tissue biomechanics. Model real-time execution is crucial and achieved by code optimisation and artificial intelligence methods. Furthermore, recent work has also shown that laboratory-based motion capture biomechanical measurements and modelling can be performed outside the laboratory with wearable sensors and artificial intelligence. The next stage is to combine these technologies into well-designed easy to use products to guide training to maintain or recover tissue health in the real-world.

Model-Based Comparison of Passive and Active Assistance Designs in an Occupational Upper Limb Exoskeleton for Overhead Lifting

OCCUPATIONAL APPLICATIONSIn recent years, various upper limb exoskeletons have been developed aiming to support industrial workers for a range of tasks and reduce risks of work-related musculoskeletal disorders. Most commercially available upper limb exoskeletons are passive systems that use compliant elements such as springs or elastic components to store and release energy to assist the user's motion. In contrast, many active exoskeletons, which are typically comprised of one or more powered actuators to provide joint assistance, are still in the research and development stages. Nevertheless, the functions and efficacy of various exoskeleton systems need to be further compared and assessed. This study presents a model-based approach to evaluate different designs of passive and active assistance and demonstrates the benefits of both assistance methods in an overhead lifting task. In addition, the modeling and simulation indicate the potential advantages of using the active assistance, based on electromyography.

Targeted Achilles Tendon Training and Rehabilitation Using Personalized and Real-Time Multiscale Models of the Neuromusculoskeletal System

Musculoskeletal tissues, including tendons, are sensitive to their mechanical environment, with both excessive and insufficient loading resulting in reduced tissue strength. Tendons appear to be particularly sensitive to mechanical strain magnitude, and there appears to be an optimal range of tendon strain that results in the greatest positive tendon adaptation. At present, there are no tools that allow localized tendon strain to be measured or estimated in training or a clinical environment. In this paper, we first review the current literature regarding Achilles tendon adaptation, providing an overview of the individual technologies that so far have been used in isolation to understand in vivo Achilles tendon mechanics, including 3D tendon imaging, motion capture, personalized neuromusculoskeletal rigid body models, and finite element models. We then describe how these technologies can be integrated in a novel framework to provide real-time feedback of localized Achilles tendon strain during dynamic motor tasks. In a proof of concept application, Achilles tendon localized strains were calculated in real-time for a single subject during walking, single leg hopping, and eccentric heel drop. Data was processed at 250 Hz and streamed on a smartphone for visualization. Achilles tendon peak localized strains ranged from ∼3 to ∼11% for walking, ∼5 to ∼15% during single leg hop, and ∼2 to ∼9% during single eccentric leg heel drop, overall showing large strain variation within the tendon. Our integrated framework connects, across size scales, knowledge from isolated tendons and whole-body biomechanics, and offers a new approach to Achilles tendon rehabilitation and training. A key feature is personalization of model components, such as tendon geometry, material properties, muscle geometry, muscle-tendon paths, moment arms, muscle activation, and movement patterns, all of which have the potential to affect tendon strain estimates. Model personalization is important because tendon strain can differ substantially between individuals performing the same exercise due to inter-individual differences in these model components.