Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion

An assessment of co-contraction in reinnervated muscle

Aim: To investigate co-contraction in reinnervated elbow flexor muscles following a nerve transfer. Materials & methods: 12 brachial plexus injury patients who received a nerve transfer to reanimate elbow flexion were included in this study. Surface electromyography (EMG) recordings were used to quantify co-contraction during sustained and repeated isometric contractions of reinnervated and contralateral uninjured elbow flexor muscles. Reuslts: For the first time, this study reveals reinnervated muscles demonstrated a trend toward higher co-contraction ratios when compared with uninjured muscle and this is correlated with an earlier onset of muscle fatigability. Conclusion: Measurements of co-contraction should be considered within muscular function assessments to help drive improvements in motor recovery therapies.

Time-varying and speed-matched model for the evaluation of stroke-induced changes in ankle mechanics

The ankle mechanics (stiffness and moment) are modulated continuously when interacting with the environment during human walking. However, it remains unclear how ankle mechanics vary with walking speeds, and how they are affected by stroke. This study aimed to determine time-varying ankle stiffness and moment in stroke participants during walking, comparing them with healthy participants at matched speeds. A motion capture system, surface electromyography (EMG) system and force plates were used to measure biomechanics of seven healthy participants walking at 5 controlled speeds and ten patients with stroke at self-selected speeds. The ankle moment and stiffness during the stance phase were calculated using an EMG-driven musculoskeletal model. Surface equations of ankle moment and stiffness in healthy participants, with walking speed and stance phase as variables, were proposed based on polynomial fitting. Results showed that as walking speed increased, there was an increase in the ankle stiffness and moment of healthy participants during 77 %-89 % and 63 %-91 % of stance phase, respectively. Patients with stroke had lower ankle stiffness and moment at self-selected walking speed than healthy participants at 1.04 m/s walking speed during 52 %-87 % and 52 %-91 % of stance phase, respectively. At matched walking speed, the peak values of ankle stiffness and moment in patients with stroke were significantly less than those in healthy participants (p = 0.007; p = 0.028, respectively). This study proposes a novel approach to evaluate the ankle mechanics of patients with stroke using the speed-matched model of healthy participants and may provide insights into understanding speed-dependent movement mechanisms of human walking.

The Simultaneous Model-Based Estimation of Joint, Muscle, and Tendon Stiffness is Highly Sensitive to the Tendon Force-Strain Relationship

OBJECTIVE

Accurate estimation of stiffness across anatomical levels (i.e., joint, muscle, and tendon) in vivo has long been a challenge in biomechanics. Recent advances in electromyography (EMG)-driven musculoskeletal modeling have allowed the non-invasive estimation of stiffness during dynamic joint rotations. Nevertheless, validation has been limited to the joint level due to a lack of simultaneous in vivo experimental measurements of muscle and tendon stiffness.

METHODS

With a focus on the triceps surae, we employed a novel perturbation-based experimental technique informed by dynamometry and ultrasonography to derive reference stiffness at the joint, muscle, and tendon levels simultaneously. Here, we propose a new EMG-driven model-based approach that does not require external joint perturbation, nor ultrasonography, to estimate multi-level stiffness. We present a novel set of closed-form equations that enables the person-specific tuning of musculoskeletal parameters dictating biological stiffness, including passive force-length relationships in modeled muscles and tendons.

RESULTS

Calibrated EMG-driven musculoskeletal models estimated the reference data with average normalized root-mean-square error ≈ 20%. Moreover, only when calibrated tendons were approximately four times more compliant than typically modeled, our approach could estimate multi-level reference stiffness.

CONCLUSION

EMG-driven musculoskeletal models can be calibrated on a larger set of reference data to provide more realistic values for the biomechanical variables across multiple anatomical levels. Moreover, the tendon models that are typically used in musculoskeletal modeling are too stiff.

SIGNIFICANCE

Calibrated musculoskeletal models informed by experimental measurements give access to an augmented range of biomechanical variables that might not be easily measured with sensors alone.

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.

Leading limb biomechanical response following compelled forward and descending body shift in old versus young adults

BACKGROUND

Falls pose a significant health risk in older adults, with stair descent falls carrying particularly severe consequences. Reduced balance control and limb support due to aging-related physiological and neuromuscular decline are critical components in increased falling risk in older adults. Understanding the age-associated abnormalities in balance control and limb support strategies during sudden forward and downward body shift could reveal potential biomechanical deficits responsible for increased falling risks in older adults. This study investigates balance regulatory responses following first-time exposure to compelled forward and downward body shift in young and older adults.

METHODS

Thirteen healthy old and thirteen healthy young adults participated in this study. Participants stood on two adjacent perturbation platforms in modified tandem stance. The leading limb support surface dropped 3 in. vertically at an unknown time. The anterior margin of stability and center of mass velocity, peak vertical ground reaction forces, and leading limb ankle and knee joint angular displacement, torque, and power during the initial response phase were compared between age groups.

FINDINGS

Compared to young adults, older adults showed higher center of mass velocity, lower margin of stability, peak vertical ground reaction force, peak ankle and knee joint power, and peak knee joint torque during the initial response phase.

INTERPRETATIONS

The abnormalities potentially identified in our study, particularly in dynamic stability regulation, limb support force generation, and shock absorption may affect the ability to arrest the body's forward and downward motion. These deficits may contribute to an increased risk of forward falls in aging.

Higher coactivations of lower limb muscles increase stability during walking on slippery ground in forward dynamics musculoskeletal simulation

The energy efficiency theory of human bipedal locomotion has been widely accepted as a neuro-musculoskeletal control method. However, coactivation of agonist and antagonist muscles in the lower limb has been observed during various limb movements, including walking. The emergence of this coactivation cannot be explained solely by the energy efficiency theory and remains a subject of debate. To shed light on this, we investigated the role of muscle coactivations in walking stability using a forward dynamics musculoskeletal simulation combined with neural-network-based gait controllers. Our study revealed that a gait controller with minimal muscle activations had a high probability of falls under challenging gait conditions such as slippery ground and uneven terrain. Lower limb muscle coactivations emerged in the process of gait controller training on slippery ground. Controllers with physiological coactivation levels demonstrated a significantly reduced probability of falls. Our results suggest that achieving stable walking requires muscle coactivations beyond the minimal level of muscle energy. This study implies that coactivations likely emerge to maintain gait stability under challenging conditions, and both coactivation and energy optimization of lower limb muscles should be considered when exploring the foundational control mechanisms of human walking.

Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals

BACKGROUND

Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces.

METHODS

Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization.

FINDINGS

The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks.

INTERPRETATION

Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

The History and Future of Neuromusculoskeletal Biomechanics

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Assisting walking balance using a bio-inspired exoskeleton controller

BACKGROUND

Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity.

METHODS

Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton.

RESULTS

Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p < 0.001). Proportional feedback of the center of mass velocity improved balance support after perturbation. Muscle activity is reduced in response to push and pull perturbations by 10% (p = 0.006) and 16% (p < 0.001) and center of mass deviations by 9% (p = 0.026) and 18% (p = 0.002) with respect to the same controller without center of mass feedback.

CONCLUSION

Our control approach implemented on bilateral ankle exoskeletons can thus effectively support steady-state walking and balance control and therefore has the potential to improve mobility in balance-impaired individuals.

EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R² scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

Lower extremity joint stiffness of autistic adolescents during running at dual speeds

Running is one of the most common forms of physical activity for autistic adolescents. However, research examining their lower extremity dynamics is sparse. In particular, no information exists regarding lower extremity joint stiffness in autistic adolescents. This study compared knee and ankle joint stiffness during the absorption phase of running between autistic adolescents and non-autistic controls. Motion capture and ground reaction forces were recorded for 22 autistic adolescents and 17 non-autistic age, sex, and BMI matched peers who ran at self-selected and standardized (3.0 m/s) speeds. Group × speed knee and ankle joint stiffness, change in moment, and range of motion were compared using mixed-model ANOVAs. There were no group × speed interactions for any variable. Autistic adolescents presented with significant (12 % and 19 %) reduced knee and ankle joint stiffness, respectively. In addition, autistic adolescents had significant reduced changes in knee and ankle joint moments by 11 % and 21 %, respectively, compared to their non-autistic peers. Only knee joint stiffness and knee joint moments were sensitive to running speed, each significantly increasing with speed by 6 %. Current literature suggests joint stiffness is an important mechanism for stability and usage of the stretch shortening cycle (or elastic recoil); as such, it is possible that the reduced ankle plantar flexor and knee extensor stiffness found in autistic adolescents in this study could be indicative of reduced efficiency during running. As group differences existed across both speeds, autistic adolescents may benefit from therapeutic and/or educational interventions targeting efficient running mechanics.

The impact of age-related increase in passive muscle stiffness on simulated upper limb reaching

Ageing changes the musculoskeletal and neural systems, potentially affecting a person's ability to perform daily living activities. One of these changes is increased passive stiffness of muscles, but its contribution to performance is difficult to separate experimentally from other ageing effects such as loss of muscle strength or cognitive function. A computational upper limb model was used to study the effects of increasing passive muscle stiffness on reaching performance across the model's workspace (all points reachable with a given model geometry). The simulations indicated that increased muscle stiffness alone caused deterioration of reaching accuracy, starting from the edges of the workspace. Re-tuning the model's control parameters to match the ageing muscle properties does not fully reverse ageing effects but can improve accuracy in selected regions of the workspace. The results suggest that age-related muscle stiffening, isolated from other ageing effects, impairs reaching performance. The model also exhibited oscillatory instability in a few simulations when the controller was tuned to the presence of passive muscle stiffness. This instability is not observed in humans, implying the presence of natural stabilizing strategies, thus pointing to the adaptive capacity of neural control systems as a potential area of future investigation in age-related muscle stiffening.

Joint torques estimation in human gait based on Gaussian process

BACKGROUND

Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions.

OBJECTIVE

The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process.

METHOD

The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs.

RESULTS

The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s).

CONCLUSION

The results show that it is possible to estimate the joint torques at different scenarios.

Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System

In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The "virtual stiffness" method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations.

Electromyography-driven model-based estimation of ankle torque and stiffness during dynamic joint rotations in perturbed and unperturbed conditions

The simultaneous modulation of joint torque and stiffness enables humans to perform large repertoires of movements, while versatilely adapting to external mechanical demands. Multi-muscle force control is key for joint torque and stiffness modulation. However, the inability to directly measure muscle force in the intact moving human prevents understanding how muscle force causally links to joint torque and stiffness. Joint stiffness is predominantly estimated via joint perturbation-based experiments in combination with system identification techniques. However, these techniques provide joint-level stiffness estimations with no causal link to the underlying muscle forces. Moreover, the need for joint perturbations limits the generalizability and applicability to study natural movements. Here, we present an electromyography (EMG)-driven musculoskeletal modeling framework that can be calibrated to match reference joint torque and stiffness profiles simultaneously via a multi-term objective function. EMG-driven models calibrated on <2 s of reference torque and stiffness data could blindly estimate reference profiles across 100 s of data not used for calibration. Model calibrations using an objective function comprising torque and stiffness terms always provided less feasible solutions than an objective function comprising solely a torque term, thereby reducing the space of feasible muscle-tendon parameters. Results also showed the proposed framework's ability to estimate joint stiffness in unperturbed conditions, while capturing differences against stiffness profiles derived during perturbed conditions. The proposed framework may provide new ways for studying causal relationships between muscle force and joint torque and stiffness during movements in interaction with the environment, with broad implications across biomechanics, rehabilitation and robotics.

Deep-Learning to Map a Benchmark Dataset of Non-amputee Ambulation for Controlling an Open Source Bionic Leg

Powered lower-limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to those of non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as intended. This paper proposes the direct mapping of the voluntary movement of a residual limb (i.e., thigh) to the desired impedance parameters for amputated limbs (i.e., knee and ankle). The proposed model was learned from the gait trajectories of intact limb individuals from a publicly available biomechanics dataset, and was applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require training time with individuals with amputation nor configuration time for its use, and it provides a closely resembling gait trajectory of the intact limb. For preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long-, and short-step lengths. The results indicate that intact benchmark data with different sensor configurations can be directly used to train the model to control prosthetic legs.

Tele-Impedance Control Approach Using Wearable Sensors

Tele-operational tasks often suffer from instability issues and limited reliability during unpredictable interactions. We propose a real-time control law reproducing the impedance and kinematic behaviour of a subject's arm (shoulder and elbow) on a remote avatar in a 2-DoF task. The human arm impedance and kinematics are estimated respectively from EMG and M-IMU data and then mapped into the avatar arm through an impedance control. Contrary to literature methods, our portable tele-impedance controller relies only on wearable sensors and enables an easy use in unstructured environments. The good performance (R²> 0.7) of the muscle model used to map on the robot the human stiffness of five healthy subjects indicates the possibility of applying the proposed algorithm for a tele-impedance control.

Validation of Methods for Estimation of Knee Joint Mechanical Impedance During Locomotion Using a Torque-Controllable Knee Exoskeleton

The mechanical impedance of the joints of the leg governs the body's response to external disturbances, and its regulation is essential for the completion of tasks of daily life. However, it is still unclear how this quantity is regulated at the knee during dynamic tasks. In this work, we introduce a method to estimate the mechanical impedance of spring-mass systems using a torque-controllable exoskeleton with the intention of extending these methods to characterize the mechanical impedance of the human knee during locomotion. We characterize system bandwidth and intrinsic impedance and present a perturbation-based methodology to identify the mechanical impedance of known spring-mass systems. Our approach was able to obtain accurate estimates of stiffness and inertia, with errors under 3% and ∼13-16%, respectively. This work provides a qualitative and quantitative foundation that will enable accurate estimates of knee joint impedance during locomotion in future works.

A Supervised Classification of Children with Fragile X Syndrome and Controls Based on Kinematic and sEMG Parameters

Fragile X syndrome (FXS) is caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. There are two main categories of FMR1 mutations, “premutation” and “full mutation”, that are associated with different clinical phenotypes, and somatic mosaicism can represent a strong FXS phenotype modulator. FXS is the leading cause of inherited intellectual disability and autism, and it is characterized by musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia. The former have been associated with altered joint kinematics and muscle activity during gait. The aim of this study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls. Results showed that the k-NN algorithm outperformed in terms of accuracy (100%) in classifying FXS children from controls, while CN2 rule induction obtained the best accuracy (97%) in classifying FXS children with mosaicism. The proposed pipeline might be used for developing assisted decision-making systems aiming at identifying and treating the musculoskeletal alterations associated with FXS.

Reduced Achilles Tendon Stiffness Disrupts Calf Muscle Neuromechanics in Elderly Gait

Older adults walk slower and with a higher metabolic energy expenditure than younger adults. In this review, we explore the hypothesis that age-related declines in Achilles tendon stiffness increase the metabolic cost of walking due to less economical calf muscle contractions and increased proximal joint work. This viewpoint may motivate interventions to restore ankle muscle-tendon stiffness, improve walking mechanics, and reduce metabolic cost in older adults.

How Compliance of Surfaces Affects Ankle Moment and Stiffness Regulation During Walking

Humans can regulate ankle moment and stiffness to cope with various surfaces during walking, while the effect of surfaces compliance on ankle moment and stiffness regulations remains unclear. In order to find the underlying mechanism, ten healthy subjects were recruited to walk across surfaces with different levels of compliance. Electromyography (EMG), ground reaction forces (GRFs), and three-dimensional reflective marker trajectories were recorded synchronously. Ankle moment and stiffness were estimated using an EMG-driven musculoskeletal model. Our results showed that the compliance of surfaces can affect both ankle moment and stiffness regulations during walking. When the compliance of surfaces increased, the ankle moment increased to prevent lower limb collapse and the ankle stiffness increased to maintain stability during the mid-stance phase of gait. Our work improved the understanding of gait biomechanics and might be instructive to sports surface design and passive multibody model development.

Are weight shifting and dynamic control strategies different in postmenopausal women with and without type-I osteoporosis?

OBJECTIVE

Tracking postural control processes at dynamic conditions might help develop an appropriate rehabilitation program in osteoporotic women. This study aimed to investigate the differences in center of pressure (COP) control at weight shifting and dynamic tasks between postmenopausal women with and without type-I osteoporosis. Also, we investigated the correlations between bone mineral density (BMD), the activity-specific balance confidence questionnaire (ABC-Q) score, and postural control parameters.

METHOD

A total of 62 volunteer postmenopausal women participated in this study. The participants were classified into non-osteoporotic (NOP, T-score >1, n = 35, age = 60.04± 5.33 years) and osteoporotic (OP, T-score < -2.5, n = 27, age = 61.88 ± 5.34 years) groups. The COP sway was recorded using a Kistler force plate during performance-based Limits of Stability (LOS), Curve Tracking (CT), Sit to Stand (STS), and Turn tasks. In addition, the level of balance confidence in daily activities was evaluated by ABC-Q.

RESULTS

In the LOS task, COP sway velocity in the anterior direction (P = 0.02) and COP maximum excursion in the side-to-side direction (right-side P = 0.027 and left-side P = 0.044) were significantly lower in the OP than the NOP group. In the CT task, all the quantified parameters, including errors and area, showed significantly lower values in the OP group than the NOP group (P < 0.05). In the STS task, the rising index score was significantly higher in the OP group than the NOP group (P = 0.014). The two groups had an equal ABC-Q score (P = 0.175). The COP sway variables correlated significantly with the lumbar and femoral neck T-score (P < 0.05).

CONCLUSION

BMD decline can change weight shifting and dynamic postural control in postmenopausal women.

The deep hip muscles are unlikely to contribute to hip stability in the sagittal plane during walking: a stiffness approach

- Objective: This study determined whether the deep hip muscles could contribute to hip stability.

METHODS

Hip stability was defined as rotational hip stiffness in the sagittal plane, which was calculated for walking trials for 12 participants via electromyography (EMG)informed neuromusculoskeletal modelling that included all 22 hip spanning muscles. Three model configurations were compared that differed in the excitations of the deep hip muscles, but were identical in the excitations of all other muscles: (1) deep hip muscles informed by intramuscular EMG measurements (assisted activation); (2) deep hip muscles with simulated zero activation (no activation); (3) deep hip muscles with simulated maximal activation (maximal activation). Sagittal plane rotational hip stiffness over the gait cycle was compared between model configurations using a within-participant analysis of variance via statistical parametric mapping (p<0.05).

RESULTS

Compared to the assisted activation configuration, hip stiffness (mean (95% confidence interval)) was 0.8% (0.7 to 0.9) lower in the no activation configuration, and 3.2% (2.9 to 3.5) higher in the maximal activation configuration over the gait cycle.

CONCLUSION

Regardless of activation level, the deep hip muscles made little contribution to sagittal plane rotational hip stiffness, which casts uncertainty around their assumed function as hip stabilizers.

SIGNIFICANCE

The merit of targeted deep hip muscle strengthening to improve hip stability in rehabilitation programs for remains unclear.

Similar sensorimotor transformations control balance during standing and walking

Standing and walking balance control in humans relies on the transformation of sensory information to motor commands that drive muscles. Here, we evaluated whether sensorimotor transformations underlying walking balance control can be described by task-level center of mass kinematics feedback similar to standing balance control. We found that delayed linear feedback of center of mass position and velocity, but not delayed linear feedback from ankle angles and angular velocities, can explain reactive ankle muscle activity and joint moments in response to perturbations of walking across protocols (discrete and continuous platform translations and discrete pelvis pushes). Feedback gains were modulated during the gait cycle and decreased with walking speed. Our results thus suggest that similar task-level variables, i.e. center of mass position and velocity, are controlled across standing and walking but that feedback gains are modulated during gait to accommodate changes in body configuration during the gait cycle and in stability with walking speed. These findings have important implications for modelling the neuromechanics of human balance control and for biomimetic control of wearable robotic devices. The feedback mechanisms we identified can be used to extend the current neuromechanical models that lack balance control mechanisms for the ankle joint. When using these models in the control of wearable robotic devices, we believe that this will facilitate shared control of balance between the user and the robotic device.

The stability of human standing and walking is remarkable, given that from a mechanical point of view standing and walking are highly unstable and therefore require well-coordinated control actions from the central nervous system. The nervous system continuously receives information on the state of the body through sensory inputs, which is processed to generate descending motor commands to the muscles. It remains, however, unclear how the central nervous system uses information from multiple sensors to control walking balance. In standing balance, such sensorimotor transformations have been studied. When standing balance is perturbed, previous studies suggest that the central nervous system estimates the movement of the whole body center of mass to activate muscles and control balance. Here, we investigated whether the same sensorimotor transformations underlie control of walking balance. We found that changes in muscle activity and ankle moments in response to perturbations of walking balance were indeed proportional to center of mass movement. These findings suggest that common processes underlie control of standing and walking balance. Our work is significant because it captures the result of complex underlying neural processes in a simple relation between the body’s center of mass movement and corrective joint moments that can be implemented in the control of prostheses and exoskeletons to support balance control in a human-like manner.

Evaluation of force feedback in walking using joint torques as "naturalistic" stimuli

Control of adaptive walking requires the integration of sensory signals of muscle force and load. We have studied how mechanoreceptors (tibial campaniform sensilla) encode “naturalistic” stimuli derived from joint torques of stick insects walking on a horizontal substrate. Previous studies showed that forces applied to the legs using the mean torque profiles of a proximal joint were highly effective in eliciting motor activities. However, substantial variations in torque direction and magnitude occurred at the more distal femorotibial joint, which can generate braking or propulsive forces and provide lateral stability. To determine how these forces are encoded, we used torque waveforms of individual steps that had maximum values in stance in the directions of flexion or extension. Analysis of kinematic data showed that the torques in different directions tended to occur in different ranges of joint angles. Variations within stance were not accompanied by comparable changes in joint angle but often reflected vertical ground reaction forces and leg support of body load. Application of torque waveforms elicited sensory discharges with variations in firing frequency similar to those seen in freely walking insects. All sensilla directionally encoded the dynamics of force increases and showed hysteresis to transient force decreases. Smaller receptors exhibited more tonic firing. Our findings suggest that dynamic sensitivity in force feedback can modulate ongoing muscle activities to stabilize distal joints when large forces are generated at proximal joints. Furthermore, use of “naturalistic” stimuli can reproduce characteristics seen in freely moving animals that are absent in conventional restrained preparations.

NEW & NOTEWORTHY Sensory encoding of forces during walking by campaniform sensilla was characterized in stick insects using waveforms of joint torques calculated by inverse dynamics as mechanical stimuli. Tests using the mean joint torque and torques of individual steps showed the system is highly sensitive to force dynamics (dF/dt). Use of “naturalistic” stimuli can reproduce characteristics of sensory discharges seen in freely walking insects, such as load transfer among legs.

Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson's disease

Electromyography (EMG)-driven neuromusculoskeletal modeling (NMSM) enables simulating the mechanical function of multiple muscle-tendon units as controlled by nervous system in the generation of complex movements. In the context of clinical assessment this may enable understanding biomechanical factor contributing to gait disorders such as one induced by Parkinson's disease (PD). In spite of the challenges in the development of patient-specific models, this preliminary study aimed at establishing a feasible and noninvasive experimental and modeling pipeline to be adopted in clinics to detect PD-induced gait alterations. Four different NMSM have been implemented for three healthy controls using CEINMS, an OpenSim-compatible toolbox. Models differed in the EMG-normalization methods used for calibration purposes (i.e. walking trial normalization and maximum voluntary contraction normalization) and in the set of experimental EMGs used for the musculotendon-unit mapping (i.e. 4 channels vs. 15 channels). Model accuracy assessment showed no statistically significant differences between the more complete model (non-clinically viable) and the proposed reduced one (clinically viable). The clinically viable reduced model was systematically applied on a dataset including ten PD's and thirteen healthy controls. Results showed significant differences in the neuromuscular control strategy of the PD group in term of muscle forces and joint torques. Indeed, PD patients displayed a significantly lower magnitude on force production and revealed a higher amount of force variability with the respect of the healthy controls. The estimated variables could become a measurable biomechanical outcome to assess and track both disease progression and its impact on gait in PD subjects.

How Well Do Commonly Used Co-contraction Indices Approximate Lower Limb Joint Stiffness Trends During Gait for Individuals Post-stroke?

Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) (CCI₁) and Falconer and Winter (1985) (CCI₂). CCI₁ measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI₂ measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) CCI₁ was generally more correlated with joint stiffness than was CCI₂, (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human–Robot Collaborative Technologies

Patients affected by neurological pathologies with motor disorders when they are of working age have to cope with problems related to employability, difficulties in working, and premature work interruption. It has been demonstrated that suitable job accommodation plans play a beneficial role in the overall quality of life of pathological subjects. A well-designed return-to-work program should consider several recent innovations in the clinical and ergonomic fields. One of the instrument-based methods used to monitor the effectiveness of ergonomic interventions is surface electromyography (sEMG), a multi-channel, non-invasive, wireless, wearable tool, which allows in-depth analysis of motor coordination mechanisms. Although the scientific literature in this field is extensive, its use remains significantly underexploited and the state-of-the-art technology lags expectations. This is mainly attributable to technical and methodological (electrode-skin impedance, noise, electrode location, size, configuration and distance, presence of crosstalk signals, comfort issues, selection of appropriate sensor setup, sEMG amplitude normalization, definition of correct sEMG-related outcomes and normative data) and cultural limitations. The technical and methodological problems are being resolved or minimized also thanks to the possibility of using reference books and tutorials. Cultural limitations are identified in the traditional use of qualitative approaches at the expense of quantitative measurement-based monitoring methods to design and assess ergonomic interventions and train operators. To bridge the gap between the return-to-work rehabilitation and other disciplines, several teaching courses, accompanied by further electrodes and instrumentations development, should be designed at all Bachelor, Master and PhD of Science levels to enhance the best skills available among physiotherapists, occupational health and safety technicians and ergonomists.

Quasi-stiffness of the knee joint is influenced by walking on a destabilising terrain

BACKGROUND

Predictive models have been devised to estimate the necessary quasi-stiffness that a transfemoral prosthesis should be set to aligning the body and gait parameters of the user. Current recommendations exist only for walking over level ground. This study aimed to ascertain whether walking across destabilising terrain influences the quasi-stiffness of the knee joint thus influencing prosthetic engineering.

METHODS

Ten healthy males (age: 25.1 ± 2.5 years; mean ± sd, height: 1.78 ± 0.05 m, weight: 84.40 ± 11.02 kg) performed 14 gait trials. Seven trials were conducted over even ground and seven over 20 mm ballast. Three-dimensional motion capture and ground reaction force were collected. Paired samples t-tests and Wilcoxon signed ranked test compared variables including; quasi-stiffness, gait speed, stride length and stride width.

RESULTS

Quasi-stiffness (d = 0.562, P = 0.001) and stride width (d = 0.909, P < 0.001) were significantly greater in the destabilising terrain condition. Gait speed (r = -0.731, P = 0.001) was significantly greater in the control condition. No significant difference was seen in stride length (d = 0.583, P = 0.016).

CONCLUSIONS

An increase in quasi-stiffness when walking across destabilising terrain was attributed to a magnified shock absorption mechanism, facilitating an increased flexion angle during the stance phase. This causes a lower centre of mass resulting in the musculoskeletal system having to produce a greater knee extensor moment to prevent the knee collapsing. Therefore, transfemoral prostheses should be tuned to apply increased extension moments if ambulation is to occur on a destabilising terrain.

Biomechanical control of paretic lower limb during imposed weight transfer in individuals post-stroke

Background

Stroke is a leading cause of disability with associated hemiparesis resulting in difficulty bearing and transferring weight on to the paretic limb. Difficulties in weight bearing and weight transfer may result in impaired mobility and balance, increased fall risk, and decreased community engagement. Despite considerable efforts aimed at improving weight transfer after stroke, impairments in its neuromotor and biomechanical control remain poorly understood. In the present study, a novel experimental paradigm was used to characterize differences in weight transfer biomechanics in individuals with chronic stroke versus able-bodied controls

Methods

Fifteen participants with stroke and fifteen age-matched able-bodied controls participated in the study. Participants stood with one foot on each of two custom built platforms. One of the platforms dropped 4.3 cm vertically to induce lateral weight transfer and weight bearing. Trials involving a drop of the platform beneath the paretic lower extremity (non-dominant limb for control) were included in the analyses. Paretic lower extremity joint kinematics, vertical ground reaction forces, and center of pressure velocity were measured. All participants completed the clinical Step Test and Four-Square Step Test.

Results

Reduced paretic ankle, knee, and hip joint angular displacement and velocity, delayed ankle and knee inter-joint timing, increased downward displacement of center of mass, and increased center of pressure (COP) velocity stabilization time were exhibited in the stroke group compared to the control group. In addition, paretic COP velocity stabilization time during induced weight transfer predicted Four-Square Step Test scores in individuals post-stroke.

Conclusions

The induced weight transfer approach identified stroke-related abnormalities in the control of weight transfer towards the paretic limb side compared to controls. Decreased joint flexion of the paretic ankle and knee, altered inter-joint timing, and increased COP stabilization times may reflect difficulties in neuromuscular control during weight transfer following stroke. Future work will investigate the potential of improving functional weight transfer through induced weight transfer training exercise.

Quantification and Modeling of Ankle Stiffness During Standing Balance

OBJECTIVE

This study investigates the factors contributing to the modulation of ankle stiffness during standing balance and evaluates the reliability of linear stiffness models.

METHODS

A dual-axis robotic platform and a visual feedback display were used to quantify ankle stiffness in both the sagittal and frontal planes while subjects controlled different levels of ankle muscle co-contraction, center-of-pressure (CoP), and loading on the ankle.

RESULTS

Results of 40 subjects demonstrated that ankle stiffness in the sagittal plane linearly increased with the increasing level of these three factors. The linear model relating the change in these factors from the baseline measurements during quiet standing to the change in weight normalized ankle stiffness resulted in high reliability (R² = 0.83). Ankle stiffness in the frontal plane increased with the increasing ankle muscle co-contraction and ankle loading, but the linearity was less obvious. It also exhibited a clear nonlinear trend when CoP was shifted mediolaterally. Consequently, the reliability of the linear model was low for ankle stiffness in the frontal plane (R² = 0.37).

CONCLUSION

During standing balance, ankle stiffness in the sagittal plane could be well explained by a linear model if ankle muscle activation, CoP, and ankle loading were collectively considered. However, the linear model cannot capture highly variable and nonlinear ankle stiffness characteristics in the frontal plane.

SIGNIFICANCE

The outcomes of this study could benefit the development of lower-extremity robots and their controllers. Furthermore, the ankle stiffness models could be used as a baseline in developing patient-specific ankle rehabilitation protocols.

Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging

OBJECTIVE

Reliable measurement of voluntary human effort is essential for effective and safe interaction between the wearer and an assistive robot. Existing voluntary effort prediction methods that use surface electromyography (sEMG) are susceptible to prediction inaccuracies due to non-selectivity in measuring muscle responses. This technical challenge motivates an investigation into alternative non-invasive effort prediction methods that directly visualize the muscle response and improve effort prediction accuracy. The paper is a comparative study of ultrasound imaging (US)-derived neuromuscular signals and sEMG signals for their use in predicting isometric ankle dorsiflexion moment. Furthermore, the study evaluates the prediction accuracy of model-based and model-free voluntary effort prediction approaches that use these signals.

METHODS

The study evaluates sEMG signals and three US imaging-derived signals: pennation angle, muscle fascicle length, and echogenicity and three voluntary effort prediction methods: linear regression (LR), feedforward neural network (FFNN), and Hill-type neuromuscular model (HNM).

RESULTS

In all the prediction methods, pennation angle and fascicle length significantly improve the prediction accuracy of dorsiflexion moment, when compared to echogenicity. Also, compared to LR, both FFNN and HNM improve dorsiflexion moment prediction accuracy.

CONCLUSION

The findings indicate FFNN or HNM approach and using pennation angle or fascicle length predict human ankle movement intent with higher accuracy.

SIGNIFICANCE

The accurate ankle effort prediction will pave the path to safe and reliable robotic assistance in patients with drop foot.

Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: a Validation-Based Approach

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.

Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury

Concurrent stimulation and reinforcement of motor and sensory pathways has been proposed as an effective approach to restoring function after developmental or acquired neurotrauma. This can be achieved by applying multimodal rehabilitation regimens, such as thought-controlled exoskeletons or epidural electrical stimulation to recover motor pattern generation in individuals with spinal cord injury (SCI). However, the human neuromusculoskeletal (NMS) system has often been oversimplified in designing rehabilitative and assistive devices. As a result, the neuromechanics of the muscles is seldom considered when modeling the relationship between electrical stimulation, mechanical assistance from exoskeletons, and final joint movement. A powerful way to enhance current neurorehabilitation is to develop the next generation prostheses incorporating personalized NMS models of patients. This strategy will enable an individual voluntary interfacing with multiple electromechanical rehabilitation devices targeting key afferent and efferent systems for functional improvement. This narrative review discusses how real-time NMS models can be integrated with finite element (FE) of musculoskeletal tissues and interface multiple assistive and robotic devices with individuals with SCI to promote neural restoration. In particular, the utility of NMS models for optimizing muscle stimulation patterns, tracking functional improvement, monitoring safety, and providing augmented feedback during exercise-based rehabilitation are discussed.

Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking

Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed "synergy optimization," or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2-6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability.

Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling

BACKGROUND

Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.

METHODS

We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.

RESULTS

We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.

CONCLUSIONS

Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio.

Activation-Dependent Changes in Soleus Length-Tension Behavior Augment Ankle Joint Quasi-Stiffness

The triceps surae muscle-tendon units are important in governing walking performance, acting to regulate mechanical behavior of the ankle through interaction between active muscle and passive elastic structures. Ankle joint quasi-stiffness (the slope of the relation between ankle moment and ankle rotation, k_A) is a useful aggregate measure of this mechanical behavior. However, the role of muscle activation and length-tension behavior in augmenting k_A remains unclear. In this study, 10 subjects completed eccentric isokinetic contractions at rest and at 2 soleus activation levels (25% and 75% isometric voluntary contraction) prescribed using electromyographic biofeedback. Ultrasound imaging quantified activation-dependent modulation of soleus muscle length-tension behavior and its role in augmenting k_A. The authors found that soleus muscle stiffness (k_M) and k_A exhibit nonlinear relations with muscle activation and both were more sensitive to the onset of activation than to subsequent increases in activation. Our findings also suggest that k_A can be modulated via activation through changes in soleus muscle length-tension behavior. However, this modulation is more complex than previously appreciated-reflecting interaction between active muscle and passive elastic tissues. Our findings may have implications for understanding normal and pathological ankle joint function and the design of impedance-based prostheses.

Single-trial estimation of quasi-static EMG-to-joint-mechanical-impedance relationship over a range of joint torques

Joint mechanical impedance is commonly measured by applying dynamic perturbations about a joint at a fixed operating point/background torque, and quantifying torque change vs. angle change. Impedance characterization in functional tasks, therefore, requires multiple experimental trials over a range of operating points-a cumbersome, invasive, time-consuming and impractical task. As an alternative, studies have related EMG to impedance, after which EMG can estimate impedance without applying joint perturbations. But, the cumbersome calibration trials are still required. We describe a method of single contraction perturbations in which the background torque slowly ramps over the operating range, with EMG simultaneously acquired. Using one such "quasi-static" contraction for model training and another for testing, we show this method to be a reasonable surrogate for traditional second-order, linear impedance modeling. A simple, short-duration calibration results. We compared our single-trial ramp method to multiple constant background torque trials at 10, 20, 30, and 40% maximum effort (extension and flexion), finding only limited differences in traditional vs. EMG-based ramp impedance estimates (12-22%, most prominent at the two lower contraction levels). Such constant force and slowly-variable force contractions are relevant to many practical applications, including ergonomics assessment, prosthetic control and clinical biomechanics.

A Linear Approach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints

The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints. However, a model optimization is needed to adjust the model's parameters to a specific subject and most of the optimization approaches presented in literature consider complex NMS models that are unsuitable for being used in a control paradigm since they suffer from long-lasting setup and optimization phases. In this work we present a minimal NMS model for predicting the elbow and shoulder torques and we compare two optimization approaches: a linear optimization method (LO) and a non-linear method based on a genetic algorithm (GA). The LO optimizes only one parameter per muscle, whereas the GA-based approach performs a deep customization of the muscle model, adjusting 12 parameters per muscle. EMG and force data have been collected from 7 healthy subjects performing a set of exercises with an arm exoskeleton. Although both optimization methods substantially improved the performance of the raw model, the findings of the study suggest that the LO might be beneficial with respect to GA as the latter is much more computationally heavy and leads to minimal improvements with respect to the former. From the comparison between the two considered joints, it emerged also that the more accurate the NMS model is, the more effective a complex optimization procedure could be. Overall, the two optimized NMS models were able to predict the shoulder and elbow moments with a low error, thus demonstrating the potentiality for being used in an admittance-based myo-control scheme. Thanks to the low computational cost and to the short setup phase required for wearing and calibrating the system, obtained results are promising for being introduced in industrial or rehabilitation real time scenarios.

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

OBJECTIVE

Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces.

APPROACH

This work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system.

MAIN RESULTS

Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks.

SIGNIFICANCE

The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies.

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS) consisting of an admittance filter, inner position controller, and electromyography (EMG)-driven musculoskeletal model (EDMM). The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM. The inner position controller tracks the intended motion, and its parameters are adjusted according to the estimated joint stiffness. Eight healthy subjects were instructed to wear the ankle exoskeleton robot, and they completed a series of sinusoidal tracking tasks involving ankle dorsiflexion and plantarflexion. The robot was controlled by the AACS and a non-adaptive admittance control scheme (NAACS) at four fixed parameter levels. The tracking performance was evaluated using the jerk value, position error, interaction torque, and EMG levels of the tibialis anterior (TA) and gastrocnemius (GAS). For the NAACS, the jerk value and position error increased with the parameter levels, and the interaction torque and EMG levels of the TA tended to decrease. In contrast, the AACS could maintain a moderate jerk value, position error, interaction torque, and TA EMG level. These results demonstrate that the AACS achieves a good tradeoff between accurate tracking and compliant assistance because it can produce a real-time response to stiffness changes in the ankle joint. The AACS can alleviate the conflict between accurate tracking and compliant assistance and has potential for application in robot-assisted rehabilitation.

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users' anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.