A hybrid static optimisation method to estimate muscle forces during muscle co-activation

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Biomechanical compensations during a stand-to-sit maneuver using transfemoral osseointegrated prostheses: A case series

BACKGROUND

Patients with transfemoral amputation and socket prostheses are at a heightened risk of developing musculoskeletal overuse injuries, commonly due to altered joint biomechanics. Osseointegrated prostheses, which involve direct anchorage of the prosthesis to the residual limb through a bone anchored prosthesis, are a novel alternative to sockets yet their biomechanical effect is largely unknown.

METHODS

Four patients scheduled to undergo unilateral transfemoral prosthesis osseointegration completed two data collections (baseline with socket prosthesis and 12-months after prosthesis osseointegration) in which whole-body kinematics and ground reaction forces were collected during stand-to-sit tasks. Trunk, pelvis, and hip kinematics, and the surrounding muscle forces, were calculated using subject-specific musculoskeletal models developed in OpenSim. Peak joint angles and muscle forces were compared between timepoints using Cohen's d effect sizes.

FINDINGS

Compared to baseline with socket prostheses, patients with osseointegrated prostheses demonstrated reduced lateral trunk bending (d = 1.46), pelvic obliquity (d = 1.09), and rotation (d = 1.77) toward the amputated limb during the stand to sit task. This was accompanied by increased amputated limb hip flexor, abductor, and rotator muscle forces (d> > 0.8).

INTERPRETATION

Improved lumbopelvic movement patterns and stabilizing muscle forces when using an osseointegrated prosthesis indicate that this novel prosthesis type likely reduces the risk of the development and/or progression of overuse injuries, such as low back pain and osteoarthritis. We attribute the increased muscle hip muscle forces to the increased load transmission between the osseointegrated prosthesis and residual limb, which allows a greater eccentric ability of the amputated limb to control lowering during the stand-to-sit task.

An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.

A Novel Approach to Measuring Muscle Mechanics in Vehicle Collision Conditions

The aim of the study was to evaluate a novel approach to measuring neck muscle load and activity in vehicle collision conditions. A series of sled tests were performed on 10 healthy volunteers at three severity levels to simulate low-severity frontal impacts. Electrical activity-electromyography (EMG)-and muscle mechanical tension was measured bilaterally on the upper trapezius. A novel mechanical contraction (MC) sensor was used to measure the tension on the muscle surface. The neck extensor loads were estimated based on the inverse dynamics approach. The results showed strong linear correlation (Pearson's coefficient = 0.821) between the estimated neck muscle load and the muscle tension measured with the MC sensor. The peak of the estimated neck muscle force delayed 0.2 ± 30.6 ms on average vs. the peak MC sensor signal compared to the average delay of 61.8 ± 37.4 ms vs. the peak EMG signal. The observed differences in EMG and MC sensor collected signals indicate that the MC sensor offers an additional insight into the analysis of the neck muscle load and activity in impact conditions. This approach enables a more detailed assessment of the muscle-tendon complex load of a vehicle occupant in pre-impact and impact conditions.