Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 Nm (9% of peak moment) and increased up to 35.0 Nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 Nm (10% of peak moment), and not substantially different than that of the extensive calibration.

Muscle torques and joint accelerations provide more sensitive measures of post-stroke movement deficits than joint angles.

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The impact of stroke on the complex multi-joint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested whether post-stroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of post-stroke motor deficits than joint angles. The motion of twenty-two participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamics analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multi-joint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that muscle torques characterize individual reaching movements with higher information content than joint angles do. Moreover, muscle torques enable distinguishing the individual motor deficits caused by aging or stroke from the typical differences in reaching between healthy individuals. Similar results were obtained using metrics derived from joint accelerations. This novel quantitative assessment method may be used in conjunction with home-based gaming motion-capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions.

Wearables-Only Analysis of Muscle and Joint Mechanics: An EMG-Driven Approach

Complex sensor arrays prohibit practical deployment of existing wearables-based algorithms for free-living analysis of muscle and joint mechanics. Machine learning techniques have been proposed as a potential solution, however, they are less interpretable and generalizable when compared to physics-based techniques. Herein, we propose a hybrid method utilizing inertial sensor- and electromyography (EMG)-driven simulation of muscle contraction to characterize knee joint and muscle mechanics during walking gait. Machine learning is used only to map a subset of measured muscle excitations to a full set thereby reducing the number of required sensors. We demonstrate the utility of the approach for estimating net knee flexion moment (KFM) as well as individual muscle moment and work during the stance phase of gait across nine unimpaired subjects. Across all subjects, KFM was estimated with 0.91 %BW·H RMSE and strong correlations (r = 0.87) compared to ground truth inverse dynamics analysis. Estimates of individual muscle moments were strongly correlated (r = 0.81-0.99) with a reference EMG-driven technique using optical motion capture and a full set of electrodes as were estimates of muscle work (r = 0.88-0.99). Implementation of the proposed technique in the current work included instrumenting only three muscles with surface electrodes (lateral and medial gastrocnemius and vastus medialis) and both the thigh and shank segments with inertial sensors. These sensor locations permit instrumentation of a knee brace/sleeve facilitating a practically deployable mechanism for monitoring muscle and joint mechanics with performance comparable to the current state-of-the-art.

Vastus lateralis muscle architecture to estimate knee extension moment of older individuals

The aim of this study was to compare the knee extension moment of older individuals with the muscle moment estimated through a biomechanical model. This was accomplished by using (1) the specific muscle architecture data of individuals, and (2) the generic muscle architecture available in the literature. The muscle force estimate was determined using a model with the muscle architecture from cadavers and the individual vastus lateralis muscle architecture of sixteen older volunteers. For the muscle moment comparison, all of the volunteers performed maximal voluntary isometric contractions (MVIC) in five different knee extension position angles. The architectural data was acquired using both resonance and ultrasound imaging. Both estimated muscle moments (generic and individual) were higher than the experimental. The architecture of the other vastii may be necessary to make the model more accurate for the older population. Although other factors inherent to ageing, such as co-contractions, fiber type percentage, and passive forces are not considered in the model, they could be responsible for the differences between moments in older people.

Computational Prediction of Muscle Moments During ARED Squat Exercise on the International Space Station

Prevention of muscle atrophy caused by reduced mechanical loading in microgravity conditions remains a challenge for long-duration spaceflight. To combat leg muscle atrophy, astronauts on the International Space Station (ISS) often perform squat exercise using the Advanced Resistive Exercise Device (ARED). While the ARED is effective at building muscle strength and volume on Earth, NASA researchers do not know how closely ARED squat exercise on the ISS replicates Earth-level squat muscle moments, or how small variations in exercise form affect muscle loading. This study used dynamic simulations of ARED squat exercise on the ISS to address these two questions. A multibody dynamic model of the complete astronaut-ARED system was constructed in OpenSim. With the ARED base locked to ground and gravity set to 9.81 m/s2, we validated the model by reproducing muscle moments, ground reaction forces, and foot center of pressure (CoP) positions for ARED squat exercise on Earth. With the ARED base free to move relative to the ISS and gravity set to zero, we then used the validated model to simulate ARED squat exercise on the ISS for a reference squat motion and eight altered squat motions involving changes in anterior–posterior (AP) foot or CoP position on the ARED footplate. The reference squat motion closely reproduced Earth-level muscle moments for all joints except the ankle. For the altered squat motions, changing the foot position was more effective at altering muscle moments than was changing the CoP position. All CoP adjustments introduced an undesirable shear foot reaction force that could cause the feet to slip on the ARED footplate, while some foot and CoP adjustments introduced an undesirable sagittal plane foot reaction moment that would cause the astronaut to rotate off the ARED footplate without the use of some type of foot fixation. Our results provide potentially useful information for achieving desired increases or decreases in specific muscle moments during ARED squat exercise performed on the ISS.

Comments and corrections on 3D modeling studies of locomotor muscle moment arms in archosaurs

In a number of recent studies we used computer modeling to investigate the evolution of muscle leverage (moment arms) and function in extant and extinct archosaur lineages (crocodilians, dinosaurs including birds and pterosaurs). These studies sought to quantify the level of disparity and convergence in muscle moment arms during the evolution of bipedal and quadrupedal posture in various independent archosaur lineages, and in doing so further our understanding of changes in anatomy, locomotion and ecology during the group’s >250 million year evolutionary history. Subsequent work by others has led us to re-evaluate our models, which revealed a methodological error that impacted on the results obtained from the abduction–adduction and long-axis rotation moment arms in our published studies. In this paper we present corrected abduction–adduction and long axis rotation moment arms for all our models, and evaluate the impact of this new data on the conclusions of our previous studies. We find that, in general, our newly corrected data differed only slightly from that previously published, with very few qualitative changes in muscle moments (e.g., muscles originally identified as abductors remained abductors). As a result the majority of our previous conclusions regarding the functional evolution of key muscles in these archosaur groups are upheld.

Comments and corrections on 3D modeling studies of locomotor muscle moment arms in archosaurs

In a number of recent studies we used computer modeling to investigate the evolution of muscle leverage (moment arms) and function in extant and extinct archosaur lineages (crocodilians, dinosaurs including birds and pterosaurs). These studies sought to quantify the level of disparity and convergence in muscle moment arms during the evolution of bipedal and quadrupedal posture in various independent archosaur lineages, and in doing so further our understanding of changes in anatomy, locomotion and ecology during the group’s >250 million year evolutionary history. Subsequent work by others has led us to re-evaluate our models, which revealed a methodological error that impacted on the results obtained from the abduction-adduction and long-axis rotation moment arms in our published studies. In this paper we present corrected abduction-adduction and long axis rotation moment arms for all our models, and evaluate the impact of this new data on the conclusions of our previous studies. We find that, in general, our newly corrected data differed only slightly from that previously published, with very few qualitative changes in muscle moments (e.g. muscles originally identified as abductors remained abductors). As a result the majority of our previous conclusions regarding the functional evolution of key muscles in these archosaur groups are upheld.

Comments and corrections on 3D modeling studies of locomotor muscle moment arms in archosaurs

In a number of recent studies we used computer modeling to investigate the evolution of muscle leverage (moment arms) and function in extant and extinct archosaur lineages (crocodilians, dinosaurs including birds and pterosaurs). These studies sought to quantify the level of disparity and convergence in muscle moment arms during the evolution of bipedal and quadrupedal posture in various independent archosaur lineages, and in doing so further our understanding of changes in anatomy, locomotion and ecology during the group’s >250 million year evolutionary history. Subsequent work by others has led us to re-evaluate our models, which revealed a methodological error that impacted on the results obtained from the abduction-adduction and long-axis rotation moment arms in our published studies. In this paper we present corrected abduction-adduction and long axis rotation moment arms for all our models, and evaluate the impact of this new data on the conclusions of our previous studies. We find that, in general, our newly corrected data differed only slightly from that previously published, with very few qualitative changes in muscle moments (e.g. muscles originally identified as abductors remained abductors). As a result the majority of our previous conclusions regarding the functional evolution of key muscles in these archosaur groups are upheld.