Balance Evaluation Based on Walking Experiments with Exoskeleton Interference

The impairment of walking balance function seriously affects human health and will lead to a significantly increased risk of falling. It is important to assess and improve the walking balance of humans. However, existing evaluation methods for human walking balance are relatively subjective, and the selected metrics lack effectiveness and comprehensiveness. We present a method to construct a comprehensive evaluation index of human walking balance. We used it to generate personal and general indexes. We first pre-selected some preliminary metrics of walking balance based on theoretical analysis. Seven healthy subjects walked with exoskeleton interference on a treadmill at 1.25 m/s while their ground reaction force information and kinematic data were recorded. One subject with Charcot-Marie-Tooth walked at multiple speeds without the exoskeleton while the same data were collected. Then, we picked a number of effective evaluation metrics based on statistical analysis. We finally constructed the Walking Balance Index (WBI) by combining multiple metrics using principal component analysis. The WBI can distinguish walking balance among different subjects and gait conditions, which verifies the effectiveness of our method in evaluating human walking balance. This method can be used to evaluate and further improve the walking balance of humans in subsequent simulations and experiments.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton ( Exo ) gait, and (iii) the Discrepancy ( i.e. , response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R 2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), ( p < 0.042)) than the Nominal model (median R 2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R 2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors

Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke

Robotic devices can provide physical assistance to people who have suffered neurological impairments such as stroke. Neurological disorders related to this condition induce abnormal gait patterns, which impede the independence to execute different Activities of Daily Living (ADLs). From the fundamental role of the ankle in walking, Powered Ankle-Foot Orthoses (PAFOs) have been developed to enhance the users’ gait patterns, and hence their quality of life. Ten patients who suffered a stroke used the actuation system of the T-FLEX exoskeleton triggered by an inertial sensor on the foot tip. The VICONmotion capture system recorded the users’ kinematics for unassisted and assisted gait modalities. Biomechanical analysis and usability assessment measured the performance of the system actuation for the participants in overground walking. The biomechanical assessment exhibited changes in the lower joints’ range of motion for 70% of the subjects. Moreover, the ankle kinematics showed a correlation with the variation of other movements analyzed. This variation had positive effects on 70% of the participants in at least one joint. The Gait Deviation Index (GDI) presented significant changes for 30% of the paretic limbs and 40% of the non-paretic, where the tendency was to decrease. The spatiotemporal parameters did not show significant variations between modalities, although users’ cadence had a decrease of 70% of the volunteers. Lastly, the satisfaction with the device was positive, the comfort being the most user-selected aspect. This article presents the assessment of the T-FLEX actuation system in people who suffered a stroke. Biomechanical results show improvement in the ankle kinematics and variations in the other joints. In general terms, GDI does not exhibit significant increases, and the Movement Analysis Profile (MAP) registers alterations for the assisted gait with the device. Future works should focus on assessing the full T-FLEX orthosis in a larger sample of patients, including a stage of training.

Predicting walking response to ankle exoskeletons using data-driven models

Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

Gait training with Achilles ankle exoskeleton in chronic incomplete spinal cord injury subjects

Powered exoskeletons (EXOs) have emerged as potential devices for Spinal Cord Injury (SCI) to support the intervention of physical therapists during therapy (rehabilitation EXOs) as well as to assist lower limb motion during the daily life (assistive EXOs). Although the ankle is considered a key joint for gait restoration after SCI, very few ankle exoskeletons were developed and tested in incomplete SCI (iSCI) population. Among those, the Achilles ankle exoskeleton is the only one embedding a Controller inspired by the neuromuscular system (NeuroMuscular Controller, NMC). In a previous study we demonstrated that a period dedicated to train iSCI subjects in using the Achilles EXO as an assistive aid, improved robot-aided walking speed and surprisingly also generated a positive trend in free walking speed on long and short distances thus suggesting a possible unexpected rehabilitation effect. To further investigate this result, a case-control longitudinal study was conducted in the present work. The aim of this study was to test the hypothesis that Achilles-aided training could improve performance of free walking of chronic iSCI people more than conventional intensity-matched gait rehabilitation. Before and after conventional and robot-aided rehabilitation a number of variables were analyzed, including spatiotemporal parameters, joint kinematics, ground reaction forces, muscle force, spasticity and its related symptoms, balance and personal experience about the training. Results showed that only the NMC-controlled Achilles training allowed participants to significantly walk faster, with a longer step length and a reduced gait cycle time. A slight force and spasticity improvements were also experienced. In terms of subjects' personal experience, Achilles training was perceived more interesting and less physically demanding than conventional rehabilitation.

Confidence in the curve: Establishing instantaneous cost mapping techniques using bilateral ankle exoskeletons

Lower extremity robotic prostheses and exoskeletons can require tuning a large number of control parameters on a subject-specific basis to reduce users' metabolic power during locomotion. We refer to the functional relationship between control parameter configurations and users' metabolic power as the metabolic cost landscape. Standard practice for estimating a metabolic cost landscape, and thus identifying optimal parameter configurations, is to vary control parameters while measuring steady-state metabolic power during walking. This approach is time consuming, tedious, and inefficient. We have developed an instantaneous cost mapping analysis that allows for an estimate of the metabolic cost landscape without the explicit need for steady-state measurements. Here we present novel methods to quantify the confidence in an estimated metabolic cost landscape, allowing for an objective subject-specific comparison of protocols regardless of which metabolic analysis is used. We validated these techniques by estimating metabolic cost landscapes for healthy subjects walking with bilateral robotic ankle exoskeletons using a standard practice protocol and two innovative protocols that use an instantaneous cost mapping analysis. All cost landscapes were a function of the devices' actuation timing. Results showed that for this device a protocol using an instantaneous cost mapping analysis could accurately identify optimal parameter configurations in 20 min, where the standard practice protocol required 42 min. Additionally, using an instantaneous cost mapping analysis with the standard practice's parameter exploration significantly improved fit confidence. These methods could greatly improve real-time optimization of robotic assistive devices or studies focused on biomechanical manipulations of locomotion.

NEW & NOTEWORTHY

We are presenting novel subject-specific metabolic cost landscape confidence analyses. These confidence analyses can greatly improve experimental design, intersubject analysis, and the comparison of landscape mapping protocols. We validated these methods by mapping subject-specific metabolic cost landscapes using bilateral ankle exoskeletons and are presenting the first full study using instantaneous cost mapping techniques to optimally tune an assistive robotic device.

A Simple Model to Estimate Plantarflexor Muscle-Tendon Mechanics and Energetics During Walking With Elastic Ankle Exoskeletons

GOAL

A recent experiment demonstrated that when humans wear unpowered elastic ankle exoskeletons with intermediate spring stiffness, they can reduce their metabolic energy cost to walk by ∼7%. Springs that are too compliant or too stiff have little benefit. The purpose of this study was to use modeling and simulation to explore the muscle-level mechanisms for the "sweet spot" in stiffness during exoskeleton assisted walking.

METHODS

We developed a simple lumped uniarticular musculoskeletal model of the plantarflexors operating in parallel with an elastic "exo-tendon." Using an inverse approach with constrained kinematics and kinetics, we rapidly simulated human walking over a range of exoskeleton stiffness values and examined the underlying neuromechanics and energetics of the biological plantarflexors.

RESULTS

Stiffer ankle exoskeleton springs resulted in larger decreases in plantarflexor muscle forces, activations, and metabolic energy consumption. However, in the process of unloading the compliant biological muscle-tendon unit, the muscle fascicles experienced larger excursions that negatively impacted series elastic element recoil that is characteristic of a tuned "catapult mechanism."

CONCLUSION

The combination of disrupted muscle-tendon dynamics and the need to produce compensatory forces/moments to maintain overall net ankle moment invariance could explain the "sweet spot" in metabolic performance at intermediate ankle exoskeleton stiffness. Future work will aim to provide experimental evidence to support the model predictions presented here using ultrasound imaging of muscle-level dynamics during walking with elastic ankle exoskeletons.

SIGNIFICANCE

Engineers must account for the muscle-level effects of exoskeleton designs in order to achieve maximal performance objectives.