ROBOT-ASSISTED TARGETED GAIT TRAINING.

Background: Millions of people are affected yearly by "runner's knee" and osteoarthritis, which is thought to be related to impact force. Millions are also affected by chronic falling, who are usually both difficult to identify and train. While at first glance, these topics seem to be entirely disconnected, there appears to be a need for a device that would address both issues. This paper proposes and investigates the use of the Variable Stiffness Treadmill (VST) as a targeted training device for the different populations described above. Materials and Methods: The VST is the authors' unique robotic split-belt treadmill that can reduce the vertical ground stiffness of the left belt, while the right belt remains rigid. In this work, heart rate and energy expenditure are measured for healthy subjects in the challenging asymmetric environment created by the VST and compared to a traditional treadmill setting. Results: This study shows that this asymmetric environment results in an increase in heart rate and energy expenditure, an increase in activity in the muscles about the hip and knee, and a decrease in impact force at heel strike. Conclusions: Compliant environments, like those created on the VST, may be a beneficial tool as they can: reduce high-impact forces during running and walking, significantly engage the muscles surrounding the hip and knee allowing for targeted training and rehabilitation, and assist in identifying and training high fall-risk individuals.

On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals.

Walking surfaces of varying compliance are encountered frequently in everyday life C, and transitions between them are usually not a challenging task for most people. The human brain, based on feedback from the environment, as well as previous experience, controls the lower limb dynamics to transition to new surfaces ensuring stability and safety. However, this is not always possible for people with lower limb impairments, especially those using wearable (orthotic) or prosthetic devices. Current control methodologies for lower limb wearables and powered ankle prostheses have successfully replicated conditions for walking on rigid surfaces. However, agility and walking stability on non-flat and compliant surfaces remain a significant challenge for individuals with gait disabilities. C There is therefore the need to incorporate the human wearer in the loop and proactively adjust their control to transition to surfaces of different compliance. This work proposes a subject-specific pattern recognition (PR) and classification strategy using kinematic data and surface electromyographic (EMG) signals to recognize user intent to transition from a rigid to a compliant surface. Using a k-Nearest Neighbors (k-NN) methodology in combination with an Artificial Neural Network (ANN), our strategy can accurately predict upcoming surface stiffness transitions C in real time. C This would allow for a fast parameter control of the prosthesis C or wearable device and for adaptation to the new terrain. Classification results after employing the proposed strategy reach a prediction accuracy of up to 87.5%, proving that C predicting transitions to compliant surfaces in real time is feasible and efficient. The proposed framework can lead to increased robustness and safety of lower-limb prosthetic C or wearable devices that will eventually improve the quality of life of individuals living with C a lower limb impairment.

Tunable, Textile-Based Joint Impedance Module for Soft Robotic Applications.

The design of soft actuators is often focused on achieving target trajectories or delivering specific forces and torques, rather than controlling the impedance of the actuator. This article outlines a new soft, tunable pneumatic impedance module based on an antagonistic actuator setup of textile-based pneumatic actuators intended to deliver bidirectional torques about a joint. Through mechanical programming of the actuators (select tuning of geometric parameters), the baseline torque to angle relationship of the module can be tuned. A high bandwidth fluidic controller that can rapidly modulate the pressure at up to 8 Hz in each antagonistic actuator was also developed to enable tunable impedance modulation. This high bandwidth was achieved through the characterization and modeling of the proportional valves used, derivation of a fluidic model, and derivation of control equations. The resulting impedance module was capable of modulating its stiffness from 0 to 100 Nm/rad, at velocities up to 120°/s and emulating asymmetric and nonlinear stiffness profiles, typical in wearable robotic applications.

A Systematic Method for Outlier Detection in Human Gait Data.

When it comes to observing and measuring human gait data for further analysis, determining whether the observed behavior is within the normal range of variability, or should be considered abnormal, is very challenging. Moreover, usually gait data are multivariate including motion capture, electromyography, force measurements, etc., each source having its own unique causes of irregularities and anomalies. This paper introduces a unique algorithm for outlier detection in periodic gait data using multiple sources and multiple procedures to improve the overall accuracy. The proposed algorithm's performance is evaluated using realistic synthetic gait data to gauge its accuracy to a truly objective known solution. It is shown that the proposed method is able to detect 91.2% of the true outliers in an extensive synthetic dataset, while only producing false positives at a rate of 0.1%, outperforming other procedures usually utilized in gait data outlier detection. The proposed method is a systematic way of removing outliers from gait data, with direct applications to human biomechanics, rehabilitation and robotics, and can be applied to other scientific fields dealing with periodic data.

Using robot-assisted stiffness perturbations to evoke aftereffects useful to post-stroke gait rehabilitation.

Stroke is a major global issue, affecting millions every year. When a stroke occurs, survivors are often left with physical disabilities or difficulties, frequently marked by abnormal gait. Post-stroke gait normally presents as one of or a combination of unilaterally shortened step length, decreased dorsiflexion during swing phase, and decreased walking speed. These factors lead to an increased chance of falling and an overall decrease in quality of life due to a reduced ability to locomote quickly and safely under one's own power. Many current rehabilitation techniques fail to show lasting results that suggest the potential for producing permanent changes. As technology has advanced, robot-assisted rehabilitation appears to have a distinct advantage, as the precision and repeatability of such an intervention are not matched by conventional human-administered therapy. The possible role in gait rehabilitation of the Variable Stiffness Treadmill (VST), a unique, robotic treadmill, is further investigated in this paper. The VST is a split-belt treadmill that can reduce the vertical stiffness of one of the belts, while the other belt remains rigid. In this work, we show that the repeated unilateral stiffness perturbations created by this device elicit an aftereffect of increased step length that is seen for over 575 gait cycles with healthy subjects after a single 10-min intervention. These long aftereffects are currently unmatched in the literature according to our knowledge. This step length increase is accompanied by kinematics and muscle activity aftereffects that help explain functional changes and have their own independent value when considering the characteristics of post-stroke gait. These results suggest that repeated unilateral stiffness perturbations could possibly be a useful form of post-stroke gait rehabilitation.

Real-time kinematic-based detection of foot-strike during walking.

Detection of foot-strike events is an integral part of gait analysis, as it allows the temporal registration of gait cycles. At the same time, it is necessary to register gait phases in real-time for applications such as wearable assistive devices and gait biofeedback that work synchronously with the human gait. Although many algorithms have been proposed for detecting foot-strikes with either wearable (e.g. Inertial Measurement Units (IMUs)) or non-wearable (e.g. force plates) sensors, there is a great need for real-time algorithms that rely only on recording the kinematics of the leg motion. This work proposes a novel and efficient kinematic algorithm, called the Foot VErtical & Sagittal Position Algorithm (F-VESPA), which has several advantages over existing methods. First, it accurately estimates foot-strike events using kinematic data without requiring access to future data points, hence achieving reduced latency during real-time implementation. Moreover, it does not require tuning of the utilized parameters, rendering it robust to different subjects and treadmill speeds. The algorithm is tested in a large set of subjects across various treadmill speeds, and it is shown to outperform even offline implementations of existing prominent kinematic algorithms. Using a 150 Hz data collection system, the F-VESPA achieved a median of 33 ms for the total true errors in detecting foot-strike. The F-VESPA is a highly responsive kinematic algorithm that can detect foot-strike events in real-time, with high accuracy, robustness and reduced latency, enabling real-time temporal registration of gait cycles.

A Model-Based Analysis of Supraspinal Mechanisms of Inter-Leg Coordination in Human Gait: Toward Model-Informed Robot-Assisted Rehabilitation.

Stroke survivors are often left suffering from gait instability due to hemiparesis. This gait dysfunction can lead to higher fall rates and an overall decrease in quality of life. Though there are many post-stroke gait rehabilitation methods in use currently, none of them allow patients to regain complete functionality. Interlimb coordination is one of the main mechanisms of walking and is usually overlooked in most post-stroke gait rehabilitation protocols. This work attempts to help further understand the mechanism of interlimb coordination and how the brain is involved in it, studying the contralateral response to unilateral stiffness perturbations. A unique robotic device, the Variable Stiffness Treadmill (VST), is used in conjunction with a pre-established neuromuscular gait model to analyze for the first time the supraspinal control mechanisms involved in inter-leg coordination induced after unilateral perturbations. The attempt to explain the observed kinematic and muscular activation data via the gait model results in the identification of two control variables that seem to play an important role in gait stability and recovery after perturbations: the target angle of attack and target hip to ankle span. This is significant because these two parameters are directly related to longer stride length and larger foot clearance during swing phase. Both variables work toward correcting common issues with hemiparetic gait, such as a shorter stride and toe drag during swing phase of the paretic leg. The results of this work could aid in the design of future model-based stroke rehabilitation methods that would perturb the subject in a systematic way and allow targeted interventions with specific functional outcomes on gait. Additionally, this work-along with future studies-could assist in improving controllers for robust bipedal robots as well as our understanding of how the brain controls balance during perturbed walking.

Effects of Transcutaneous Electric Nerve Stimulation on Upper Extremity Proprioceptive Function.

Electrical stimulation of the vagus nerve has been shown to enhance cortical plasticity and may benefit upper extremity rehabilitation following stroke. As an initial step towards assessing the potential of other craniocervical nerves as neuromodulation targets during rehabilitation, we explored the ability of non-invasive stimulation of cervical spine afferents, paired with a proprioceptive discrimination task, to improve sensory function in neurologically intact human subjects. On each trial, subjects' arms were moved by a robot from a test position, along a random path, to a judgment position located 1-4 cm away. Subjects responded 'same' if the judgment position was the same as the test or 'different' if it was not. These responses were used to compute proprioceptive sensitivity and bias. Three groups of 20 subjects received transcutaneous electric nerve stimulation to the C3/C4 cervical spine at one of three frequencies (30 Hz, 300 Hz, 3 kHz) for 10 minutes prior to task performance. A fourth group served as a sham. We found a statistically significant interaction between stimulation frequency and displacement distance on proprioceptive sensitivity. In summary, stimulation of cervical spine afferents may enhance arm proprioceptive function, though in unimpaired subjects these gains depend on both stimulation frequency and discrimination distance.Clinical Relevance- This study provides preliminary data on the potential for non-invasive stimulation of cervical spine afferents to enhance recovery of function following stroke and other neurological disorders.

A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation.

Stroke affects one out of every six people on Earth. Approximately 90% of stroke survivors have some functional disability with mobility being a major impairment, which not only affects important daily activities but also increases the likelihood of falling. Originally intended to supplement traditional post-stroke gait rehabilitation, robotic systems have gained remarkable attention in recent years as a tool to decrease the strain on physical therapists while increasing the precision and repeatability of the therapy. While some of the current methods for robot-assisted rehabilitation have had many positive and promising outcomes, there is moderate evidence of improvement in walking and motor recovery using robotic devices compared to traditional practice. In order to better understand how and where robot-assisted rehabilitation has been effective, it is imperative to identify the main schools of thought that have prevailed. This review intends to observe those perspectives through three different lenses: the goal and type of interaction, the physical implementation, and the sensorimotor pathways targeted by robotic devices. The ways that researchers approach the problem of restoring gait function are grouped together in an intuitive way. Seeing robot-assisted rehabilitation in this unique light can naturally provoke the development of new directions to potentially fill the current research gaps and eventually discover more effective ways to provide therapy. In particular, the idea of utilizing the human inter-limb coordination mechanisms is brought up as an especially promising area for rehabilitation and is extensively discussed.

A Comprehensive Analysis of Sensorimotor Mechanisms of Inter-Leg Coordination in Gait Using the Variable Stiffness Treadmill: Physiological Insights for Improved Robot-Assisted Gait Therapy.

Rehabilitation robotics is an emerging field in which gait training has been largely automated allowing more intensive, repetitive motions which are important for facilitating recovery. However, there is no clear evidence that robot-assisted gait training is superior to conventional therapy. A limitation of current approaches to gait therapy is that they do not consider mechanisms of inter-leg coordination and how the sensory feedback from one leg affects the motion of the other leg. Instead they impose motion on the impaired limb. Recent research suggests that utilizing the coupling between limbs in stroke rehabilitation therapies could lead to improved functional outcome. Therefore, a fundamental understanding of underlying sensorimotor mechanisms of inter-leg coordination may facilitate improved interventions in gait therapy. This paper systematically explores and analyzes a sensorimotor mechanism of inter-leg coordination that is stimulated through sudden unilateral low-stiffness perturbations to the walking surface. The potential contribution of each sensory modality to the perception and response of the perturbation will be investigated. Additionally, the neural pathway that relays the sensory signal into the motor output will be described in order to fully characterize this sensorimotor mechanism of inter-leg coordination. This work provides physiological understanding of inter-leg coordination that will benefit robot-assisted gait therapies.

Anticipatory muscle responses in transitions from rigid to compliant surfaces: towards smart ankle-foot prostheses.

Locomotion is paramount in enabling human beings to effectively respond in space and time to meet different needs. There are 2 million Americans living with an amputation and the majority of those amputations are of the lower limbs. Although current powered prostheses can accommodate walking, and in some cases running, basic functions like hiking or walking on various non-rigid or dynamic terrains are requirements that have yet to be met. This paper focuses on the mechanisms involved during human locomotion, while transitioning from rigid to compliant surfaces such as from pavement to sand, grass or granular media. Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for human locomotion, rigid to compliant surface transitions are simulated. The analysis of muscular activation during the transition from rigid to compliant surfaces reveals specific anticipatory muscle activation that precedes stepping on the compliant surface. These results are novel and important since the evoked activation changes can be used for altering the powered prosthesis control parameters to adapt to the new surface, and therefore result in significantly increased robustness for smart powered lower limb prostheses.

Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications.

Perception of Arm Position in Three-Dimensional Space.

Proprioception refers to the senses of body position, movement, force and effort. Previous studies have demonstrated workspace and direction-dependent differences in arm proprioceptive sensitivity within the horizontal plane. In addition, studies of reaching in the vertical plane have shown that proprioception plays a key role in anticipating arm configuration dependent effects of gravity. This suggests that proprioceptive sensitivity could vary with the direction of arm displacement relative to the gravitational vector, as well as with arm configuration. To test these hypotheses, and to characterize proprioception more generally, we assessed the direction-dependence and arm postural-dependence of proprioceptive sensitivity in 3D space using a novel robotic paradigm. A subject's right arm was coupled to a 7-df robot through a trough that stabilized the wrist and forearm, allowing for changes in configuration largely at the elbow and shoulder. Sensitivity was evaluated using a "same-different" task, where the subject's hand was moved 1-4 cm away from an initial "test" position to a 2nd "judgment" position. The proportion of trials where subjects responded "different" when the positions were different ("hit rate"), and where they responded "different" when the positions were the same, ("false alarm rate"), were used to calculate d', a measure of sensitivity derived from signal detection theory (SDT). Initially, a single initial arm posture was used and displacements were performed in six directions: upward, downward, forward, backward, leftward and rightward of the test position. In a follow-up experiment, data were obtained for four directions and two initial arm postures. As expected, sensitivity (d') increased monotonically with distance for all six directions. Sensitivity also varied between directions, particularly at position differences of 2 and 3 cm. Overall, sensitivity reached near maximal values in this task at 2 cm for the leftward/rightward directions, 3 cm for upward/forward and 4 cm for the downward/backward directions. In addition, when data were grouped together for opposing directions, sensitivity showed a dependence upon arm posture. These data suggest arm proprioceptive sensitivity is both anisotropic in 3D space and configuration-dependent, which has important implications for sensorimotor control of the arm and human-robot interactions.

Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review.

In the last years, several studies have been focused on understanding how the central nervous system controls muscles to perform a specific motor task. Although it still remains an open question, muscle synergies have come to be an appealing theory to explain the modular organization of the central nervous system. Even though the neural encoding of muscle synergies remains controversial, a large number of papers demonstrated that muscle synergies are robust across different tested conditions, which are within a day, between days, within a single subject, and between subjects that have similar demographic characteristics. Thus, muscle synergy theory has been largely used in several research fields, such as clinics, robotics, and sports. The present systematical review aims at providing an overview on the applications of muscle synergy theory in clinics, robotics, and sports; in particular, the review is focused on the papers that provide tangible information for (i) diagnosis or pathology assessment in clinics, (ii) robot-control design in robotics, and (iii) athletes' performance assessment or training guidelines in sports.

Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.

OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. APPROACH: A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects. MAIN RESULTS: The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words. SIGNIFICANCE: The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction.

Of particular interest to the neuroscience and robotics communities is the understanding of how two humans could physically collaborate to perform motor tasks such as holding a tool or moving it across locations. When two humans physically interact with each other, sensory consequences and motor outcomes are not entirely predictable as they also depend on the other agent's actions. The sensory mechanisms involved in physical interactions are not well understood. The present study was designed (1) to quantify human-human physical interactions where one agent ("follower") has to infer the intended or imagined-but not executed-direction of motion of another agent ("leader") and (2) to reveal the underlying strategies used by the dyad. This study also aimed at verifying the extent to which visual feedback (VF) is necessary for communicating intended movement direction. We found that the control of leader on the relationship between force and motion was a critical factor in conveying his/her intended movement direction to the follower regardless of VF of the grasped handle or the arms. Interestingly, the dyad's ability to communicate and infer movement direction with significant accuracy improved (>83%) after a relatively short amount of practice. These results indicate that the relationship between force and motion (interpreting as arm impedance modulation) may represent an important means for communicating intended movement direction between biological agents, as indicated by the modulation of this relationship to intended direction. Ongoing work is investigating the application of the present findings to optimize communication of high-level movement goals during physical interactions between biological and non-biological agents.

Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.

INTRODUCTION: Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution. METHODS: We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach's ability to control hand posture and finger forces. RESULTS: Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83-99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution. DISCUSSION: This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.

Unilateral changes in walking surface compliance evoke dorsiflexion in paretic leg of impaired walkers.

INTRODUCTION: Gait impairments due to stroke impact millions of individuals throughout the world. Despite the growing interest in automating gait therapy with robotic devices, there is no clear evidence that robot-assisted gait therapy is superior to traditional treadmill-based therapy. METHODS: This work investigates the effect of perturbations to the compliance of the walking surface on the paretic leg of impaired walkers. Using a novel robotic device, the variable stiffness treadmill, we apply perturbations to the compliance of the walking surface underneath the non-paretic leg of two hemi-paretic walkers and analyze the kinematic and neuromuscular response of the contralateral (paretic) leg with motion capture and surface electromyography systems. RESULTS: We present results of evoked muscle activity (predominately tibialis anterior) and increased dorsiflexion in the paretic leg during the swing phase of gait at stiffness values of 60 kN/m and less for all subjects. CONCLUSIONS: This work provides evidence for the first time of reducing the drop-foot effect in the impaired leg of hemiparetic walkers in response to unilateral perturbations to the compliance of the treadmill platform, thus providing direction for targeted robot-assisted gait rehabilitation.