Animal locomotion is influenced by a combination of constituent joint torques (e.g., due to limb inertia and passive viscoelasticity), which determine the necessary muscular response to move the limb. Across animal size-scales, the relative contributions of these constituent joint torques affect the muscular response in different ways. We used a multi-muscle biomechanical model to analyze how passive torque components change due to an animal’s size-scale during locomotion. By changing the size-scale of the model, we characterized emergent muscular responses at the hip as a result of the changing constituent torque profile. Specifically, we found that activation phases between extensor and flexor torques to be opposite between small and large sizes for the same kinematic motion. These results suggest general principles of how animal size affects neural control strategies. Our modeled torque profiles show a strong agreement with documented hindlimb torque during locomotion and can provide insights into the neural organization and muscle activation behavior of animals whose motion has not been extensively documented.
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The Effects of Mechanical Scale on Neural Control and the Regulation of Joint Stability
Recent work has demonstrated how the size of an animal can affect neural control strategies, showing that passive viscoelastic limb properties have a significant role in determining limb movements in small animals but are less important in large animals. We extend that work to consider effects of mechanical scaling on the maintenance of joint integrity; i.e., the prevention of aberrant contact forces within joints that might lead to joint dislocation or cartilage degradation. We first performed a literature review to evaluate how properties of ligaments responsible for joint integrity scale with animal size. Although we found that the cross-sectional area of the anterior cruciate ligament generally scaled with animal size, as expected, the effects of scale on the ligament’s mechanical properties were less clear, suggesting potential adaptations in passive contributions to the maintenance of joint integrity across species. We then analyzed how the neural control of joint stability is altered by body scale. We show how neural control strategies change across mechanical scales, how this scaling is affected by passive muscle properties and the cost function used to specify muscle activations, and the consequences of scaling on internal joint contact forces. This work provides insights into how scale affects the regulation of joint integrity by both passive and active processes and provides directions for studies examining how this regulation might be accomplished by neural systems.
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- Award ID(s):
- 2015317
- NSF-PAR ID:
- 10251480
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 22
- Issue:
- 4
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract There is a functional trade‐off in the design of skeletal muscle. Muscle strength depends on the number of muscle fibers in parallel, while shortening velocity and operational distance depend on fascicle length, leading to a trade‐off between the maximum force a muscle can produce and its ability to change length and contract rapidly. This trade‐off becomes even more pronounced as animals increase in size because muscle strength scales with area (length2) while body mass scales with volume (length3). In order to understand this muscle trade‐off and how animals deal with the biomechanical consequences of size, we investigated muscle properties in the pectoral girdle of varanid lizards. Varanids are an ideal group to study the scaling of muscle properties because they retain similar body proportions and posture across five orders of magnitude in body mass and are highly active, terrestrially adapted reptiles. We measured muscle mass, physiological cross‐sectional area, fascicle length, proximal and distal tendon lengths, and proximal and distal moment arms for 27 pectoral girdle muscles in 13 individuals across 8 species ranging from 64 g to 40 kg. Standard and phylogenetically informed reduced major axis regression was used to investigate how muscle architecture properties scale with body size. Allometric growth was widespread for muscle mass (scaling exponent >1), physiological cross‐sectional area (scaling exponent >0.66), but not tendon length (scaling exponent >0.33). Positive allometry for muscle mass was universal among muscles responsible for translating the trunk forward and flexing the elbow, and nearly universal among humeral protractors and wrist flexors. Positive allometry for PCSA was also common among trunk translators and humeral protractors, though less so than muscle mass. Positive scaling for fascicle length was not widespread, but common among humeral protractors. A higher proportion of pectoral girdle muscles scaled with positive allometry than our previous work showed for the pelvic girdle, suggesting that the center of mass may move cranially with body size in varanids, or that the pectoral girdle may assume a more dominant role in locomotion in larger species. Scaling exponents for physiological cross‐sectional area among muscles primarily associated with propulsion or with a dual role were generally higher than those associated primarily with support against gravity, suggesting that locomotor demands have at least an equal influence on muscle architecture as body support. Overall, these results suggest that larger varanids compensate for the increased biomechanical demands of locomotion and body support at higher body sizes by developing larger pectoral muscles with higher physiological cross‐sectional areas. The isometric scaling rates for fascicle length among locomotion‐oriented pectoral girdle muscles suggest that larger varanids may be forced to use shorter stride lengths, but this problem may be circumvented by increases in limb excursion afforded by the sliding coracosternal joint.
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Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijms22042018
