Fig 7 - uploaded by Antoine Falisse
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EMG, sensory information, and sensory feedback of the gastrocnemius medialis (gastrocnemii) during a fast passive stretch motion. The EMG (top), normalized sensory information (left), and corresponding sensory feedback (right) are shown for one trial of a fast passive stretch motion for the gastrocnemius medialis (gastrocnemii) of one CP child (corresponding to Fig 6 (zoom between 2 and 3.2 s)). The vertical lines indicate the first three EMG peaks. Normalized muscle fiber acceleration is the first time derivative of normalized muscle fiber velocity. dF/dt is the first time derivative of normalized muscle force. https://doi.org/10.1371/journal.pone.0208811.g007
Source publication
Muscle spasticity is characterized by exaggerated stretch reflexes and affects about 85% of the children with cerebral palsy. However, the mechanisms underlying spasticity and its influence on gait are not well understood. Here, we first aimed to model the response of spastic hamstrings and gastrocnemii in children with cerebral palsy to fast passi...
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Objective
To investigate prevalence of EMG patterns underlying hypertonia in multiple sclerosis (MS) and whether these patterns indicate different levels of spinal excitability.
Methods
We investigated the EMG activity recorded from 108 hypertonic muscles of 59 consecutive MS patients. To investigate spastic dystonia (SD), we looked for the presen...
Citations
... Finally, we only evaluated stretch reflex threshold on muscle lengthening velocity. Nevertheless, some studies suggest that spasticity could be due to other factors such as muscle length [51], muscle lengthening acceleration [23], or the force applied [52]. Thus, differences between both methods (EMG-Onset and MaxAcc) on these thresholds could be present and should be further evaluated. ...
Spasticity might affect gait in children with cerebral palsy. Quantifying its occurrence during locomotion is challenging. One approach is to determine kinematic stretch reflex thresholds, usually on the velocity, during passive assessment and to search for their exceedance during gait. These thresholds are determined through EMG-Onset detection algorithms, which are variable in performance and sensitive to noisy data, and can therefore lack consistency. This study aimed to evaluate the feasibility of determining the velocity stretch reflex threshold from maximal musculotendon acceleration. Eighteen children with CP were recruited and underwent clinical gait analysis and a full instrumented assessment of their soleus, gastrocnemius lateralis, semitendinosus, and rectus femoris spasticity, with EMG, kinematics, and applied forces being measured simultaneously. Using a subject-scaled musculoskeletal model, the acceleration-based stretch reflex velocity thresholds were determined and compared to those based on EMG-Onset determination. Their consistencies according to physiological criteria, i.e., if the timing of the threshold was between the beginning of the stretch and the spastic catch, were evaluated. Finally, two parameters designed to evaluate the occurrence of spasticity during gait, i.e., the proportion of the gait trial time with a gait velocity above the velocity threshold and the number of times the threshold was exceeded, were compared. The proposed method produces velocity stretch reflex thresholds close to the EMG-based ones. For all muscles, no statistical difference was found between the two parameters designed to evaluate the occurrence of spasticity during gait. Contrarily to the EMG-based methods, the proposed method always provides physiologically consistent values, with median electromechanical delays of between 50 and 130 ms. For all subjects, the semitendinosus velocity during gait usually exceeded its stretch reflex threshold, while it was less frequent for the three other muscles. We conclude that a velocity stretch reflex threshold, based on musculotendon acceleration, is a reliable substitute for EMG-based ones.
... They reported that their parameters were sensitive to spasticity [51] and provided a more comprehensive assessment than clinical scales [129]. Interestingly, Falisse et al. [130] utilized this assessment method along with 3D gait analysis to develop spasticity models and recognized that a model relying on feedback from muscle force, and its time derivative (dF/dt) was best suited in explaining muscle activity during passive stretches and gait. This is consistent with a recent theory suggesting that muscle spindle receptors encode information about muscle force instead of length [131]. ...
A precise method to measure spasticity is fundamental in improving the quality of life of spastic patients. The measurement methods that exist for spasticity have long been considered scarce and inadequate, which can partly be explained by a lack of consensus in the definition of spasticity. Spasticity quantification methods can be roughly classified according to whether they are based on neurophysiological or biomechanical mechanisms, clinical scales, or imaging techniques. This article reviews methods from all classes and further discusses instrumentation, dimensionality, and EMG onset detection methods. The objective of this article is to provide a review on spasticity measurement methods used to this day in an effort to contribute to the advancement of both the quantification and treatment of spasticity.
... In the same years, similar (or slightly more advanced) models stemmed from this work, for example, that implemented a feedforward control to stabilize further the gait 92 or neural oscillators driven by synergies extracted via nonnegative matrix factorization methods to control the activation of muscle actuators. 106 Other authors 107,108 proposed ways to model the stretch reflex instead, which is responsible for muscle hyperresistance and hyperexcitability in children with cerebral palsy (eg, in spastic muscles), linking the excitations of specific muscles to the velocity and acceleration at which their fibers were contracting 107 and the muscle force produced. ...
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.
... Recently, it was suggested that a force-related hyperreflexia model outperformed a velocity-based model when comparing each model's estimated muscle activity to experimental muscle activity during passive stretches and gait in children with spasticity. 16 This finding is supported by a recent experimental study suggesting that muscle spindles encode force. 17 However, simulations were not fully predictive, limiting the evaluation of cause-effect mechanisms. ...
... Our study findings contrast with the findings of Falisse et al, 16 who showed that force-based hyperreflexia outperformed velocitybased hyperreflexia in predicting muscle activations during gait of children with CP. Differences in modeling approach and patient selection may explain this contradicting finding. ...
Spasticity is a common impairment within pediatric neuromusculoskeletal disorders. How spasticity contributes to gait deviations is important for treatment selection. Our aim was to evaluate the pathophysiological mechanisms underlying gait deviations seen in children with spasticity, using predictive simulations. A cluster analysis was performed to extract distinct gait patterns from experimental gait data of 17 children with spasticity to be used as comparative validation data. A forward dynamic simulation framework was employed to predict gait with either velocity- or force-based hyperreflexia. This framework entailed a generic musculoskeletal model controlled by reflexes and supraspinal drive, governed by a multiobjective cost function. Hyperreflexia values were optimized to enable the simulated gait to best match experimental gait patterns. Three experimental gait patterns were extracted: (1) increased knee flexion, (2) increased ankle plantar flexion, and (3) increased knee flexion and ankle plantar flexion when compared with typical gait. Overall, velocity-based hyperreflexia outperformed force-based hyperreflexia. The first gait pattern could mostly be explained by rectus femoris and hamstrings velocity-based hyperreflexia, the second by gastrocnemius velocity-based hyperreflexia, and the third by gastrocnemius, soleus, and hamstrings velocity-based hyperreflexia. This study shows how velocity-based hyperreflexia from specific muscles contributes to different spastic gait patterns, which may help in providing targeted treatment.
... Inspired by recent animal experiments, 41 we proposed a novel model in which reflex activity is modeled proportional to muscle force and derivative of force, that is, yank rather than muscle length and velocity. 41,42 Furthermore, by modeling movement historydependent muscle force, that is, short-range stiffness, we captured the interaction between muscle activity-driven by muscle force through the muscle spindle-and background muscle activity. 43,44 This novel neuro-MSK model was able to overcome the limitations of previous models and captured joint movement observed during clinical spasticity tests, in particular the pendulum test and muscle activity observed during passive joint rotations. ...
... 43,44 This novel neuro-MSK model was able to overcome the limitations of previous models and captured joint movement observed during clinical spasticity tests, in particular the pendulum test and muscle activity observed during passive joint rotations. 42,45 Furthermore, our novel model also led to a novel testable hypothesis. If reflex hyperexcitability, indeed, interacts with background muscle activity through movement-dependent short-range stiffness in the muscle, prior joint movement should normalize the response to stretch; that is, indeed, what we found in a follow-up experimental study in children with CP. 46 When we moved the lower leg up and down instead of keeping it still before dropping it from the horizontal position in a relaxed patient during the pendulum test, the pendulum movement of the lower leg was closer to healthy controls. ...
In this review, we elaborate on how musculoskeletal (MSK) modeling combined with dynamic movement simulation is gradually evolving from a research tool to a promising in silico tool to assist medical doctors and physical therapists in decision making by providing parameters relating to dynamic MSK function and loading. This review primarily focuses on our own and related work to illustrate the framework and the interpretation of MSK model-based parameters in patients with 3 different conditions, that is, degenerative joint disease, cerebral palsy, and adult spinal deformities. By selecting these 3 clinical applications, we also aim to demonstrate the differing levels of clinical readiness of the different simulation frameworks introducing in silico model-based biomarkers of motor function to inform MSK rehabilitation and treatment, with the application for adult spinal deformities being the most recent of the 3. Based on these applications, barriers to clinical integration and positioning of these in silico technologies within standard clinical practice are discussed in the light of specific challenges related to model assumptions, required level of complexity and personalization, and clinical implementation.
... Specifically, simulations allow us to explore changes in muscle fiber length, velocity, and force, which are challenging to measure experimentally [14]- [16]. For example, simulations have provided insights on how spasticity affects the response of muscles during stretches and in gait [17], plantarflexor muscle-tendon dynamics and energetics for rearfoot and forefoot striking during running [18], and the effects of elastic ankle exoskeletons [19]. Further, they have been used to better understand the energetics of walking on level ground, on an incline, with load, and with assistive devices [20]- [23]. ...
Connecting the legs with a spring attached to the shoelaces reduces the energy cost of running, but how the spring reduces the energy burden of individual muscles remains unknown. We generated muscle-driven simulations of seven individuals running with and without the spring to discern whether savings occurred during the stance phase or the swing phase, and to identify which muscles contributed to energy savings. We computed differences in muscle-level energy consumption, muscle activations, and changes in muscle-fiber velocity and force between running with and without the spring. Across participants, running with the spring reduced the measured rate of energy expenditure by 0.9 W/kg (8.3%). Simulations predicted a 1.4 W/kg (12.0%) reduction in the average rate of energy expenditure and correctly identified that the spring reduced rates of energy expenditure for all participants. Simulations showed most of the savings occurred during stance (1.5 W/kg), though the rate of energy expenditure was also reduced during swing (0.3 W/kg). The energetic savings were distributed across the quadriceps, hip flexor, hip abductor, hamstring, hip adductor, and hip extensor muscle groups, whereas no changes in the rate of energy expenditure were observed in the plantarflexor or dorsiflexor muscles. Energetic savings were facilitated by reductions in the rate of mechanical work performed by muscles and their estimated rate of heat production. The simulations provide insight into muscle-level changes that occur when utilizing an assistive device and the mechanisms by which a spring connecting the legs improves running economy.
... Nonetheless, we believe we are able to extend the current work for further investigations on patients with other pathologic conditions such as muscle contracture, muscle spasticity, cerebral palsy, and femoral anteversion. Such an extension will require further adjustment or randomization of muscle parameters such as optimal fiber lengths for contracture [46] or muscle contraction intensity for spasticity [47]. ...
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.
... However, the mechanisms underlying spasticity and its influence on gait are still not well understood. In [110], a model of the lower leg as a torquedriven single-link pendulum was used to gain insight into physiological mechanisms of spasticity, and in [111], a spasticity model based on feedback from muscle force was developed. This model was applied in a predictive simulation case study of a child with CP in [80]. ...
Over the past decade, there has been a rapid increase in applications of multibody system dynamics to the predictive simulation of human movement. Using predictive “what-if” human dynamic simulations that do not rely on experimental testing or prototypes, new medical interventions and devices can be developed more quickly, cheaply, and safely. In this paper, we provide a comprehensive review of research into the predictive multibody dynamic simulation of human movements, with applications in clinical practice, medical and assistive device design, sports, and industrial ergonomics. Multibody models of human neuromusculoskeletal systems are reviewed, including models of joints, contacts, and muscle forces or torques, followed by a review of simulation approaches that use optimal control methods and a cost function to predict human movements. Modelling and optimal control software are also reviewed, and directions for future research are suggested.
... This simulator is used in the physical rehabilitation of children precisely because it assists in moving around and rotation around its axis (Keawutan, 2017). The reviewed studies developed physical exercises for children with musculoskeletal disorders and cerebral palsy with the Gross simulator (Falisse et al., 2018;Kim et al., 2018). To better manage their bodies, a map of their optimal weights was developed for each child. ...
... To better manage their bodies, a map of their optimal weights was developed for each child. Such optimal weights are determined by the weight level at which a child is able to perform different movements independently (Falisse et al., 2018). With a mild degree of motor disorders, a child performs the exercise more effectively when their weight is reduced by 15-20%. ...
Research objectives: specify the framework of using cutting-edge approaches and innovations in the training of rehabilitation physicians; investigate existing innovative technologies; get an insight into the motivation of students majoring in rehabilitation when working with the Gross simulator in sports rehabilitation of patients. The research was conducted at a mixed-type rehabilitation center for disabled adults and children at Soochow University in China. The study was conducted among 200 students majoring in rehabilitation at Soochow University and 100 children from a rehabilitation center with motor disorders: 50 boys and 50 girls. The patients were aged 4-6 years, and the average age of the participating students was 21 years. The students were offered a 6-month training course in rehabilitation of patients with musculoskeletal disorders, by using innovative technology—the Gross simulator. Student motivation analysis yielded the following outcomes: 90% of the students said that training to operate simulators is useful for the rehabilitation specialist, because this affects the proficiency of such specialist; 15% of students reported they did not like innovative technology in teaching because they faced difficulties in understanding it; 80% of students believed such technology facilitated the learning, and 5% of students were neutral. Further studies, conducted on clinical research sites, might address the effectiveness of training rehabilitation specialists, the use of cutting-edge simulators and robotic systems, as well as development of rehabilitation protocols for patients with injuries and musculoskeletal disorders.
... That model replicated hamstring (HAMS) EMG global shape during passive stretching, but did not reproduce EMG fast variations. Falisse et al. (2018) also studied spasticity in CP children and compared three models based on velocity, acceleration and force feedback. The force-related model predicted HAMS and GAS activity that better correlated with measured activity during gait compared with the two other models. ...
This study investigates the pathological toe and heel gaits seen in human locomotion using neuromusculoskeletal modelling and simulation. In particular, it aims to investigate potential cause–effect relationships between biomechanical or neural impairments and pathological gaits. Toe and heel gaits are commonly present in spinal cord injury, stroke and cerebral palsy. Toe walking is mainly attributed to spasticity and contracture at plantar flexor muscles, whereas heel walking can be attributed to muscle weakness of biomechanical or neural origin. To investigate the effect of these impairments on gait, this study focuses on the soleus and gastrocnemius muscles as they contribute to ankle plantarflexion. We built a reflex circuit model based on previous work by Geyer and Herr with additional pathways affecting the plantar flexor muscles. The SCONE software, which provides optimisation tools for 2D neuromechanical simulation of human locomotion, is used to optimise the corresponding reflex parameters and simulate healthy gait. We then modelled various bilateral plantar flexor biomechanical and neural impairments, and individually introduced them in the healthy model. We characterised the resulting simulated gaits as pathological or not by comparing ankle kinematics and ankle moment with the healthy optimised gait based on metrics used in clinical studies. Our simulations suggest that toe walking can be generated by hyperreflexia, whereas muscle and neural weaknesses partially induce heel gait. Thus, this ‘what if’ approach is deemed of great interest as it allows investigation of the effect of various impairments on gait and suggests an important contribution of active reflex mechanisms to pathological toe gait.
Key points
Pathological toe and heel gaits are commonly present in various conditions such as spinal cord injury, stroke and cerebral palsy.
These conditions present various neural and biomechanical impairments, but the cause–effect relationships between these impairments and pathological gaits are difficult to establish clinically.
Based on neuromechanical simulation, this study focuses on the plantar flexor muscles and builds a new reflex circuit controller to model and evaluate the potential effect of both neural and biomechanical impairments on gait.
Our results suggest an important contribution of active reflex mechanisms to pathological toe gait.
This ‘what if’ based on neuromechanical modelling is thus deemed of great interest to target potential causes of pathological gait.
