Center of Mass Estimation for Impaired Gait Assessment Using Inertial Measurement Units.

Injury or disease often compromise walking dynamics and negatively impact quality of life and independence. Assessing methods to restore or improve pathological gait can be expedited by examining a global parameter that reflects overall musculoskeletal control. Center of mass (CoM) kinematics follow well-defined trajectories during unimpaired gait, and change predictably with various gait pathologies. We propose a method to estimate CoM trajectories from inertial measurement units (IMUs) using a bidirectional Long Short-Term Memory neural network to evaluate rehabilitation interventions and outcomes. Five non-disabled volunteers participated in a single session of various dynamic walking trials with IMUs mounted on various body segments. A neural network trained with data from four of the five volunteers through a leave-one-subject out cross validation estimated the CoM with average root mean square errors (RMSEs) of 1.44cm, 1.15cm, and 0.40cm in the mediolateral (ML), anteroposterior (AP), and inferior/superior (IS) directions respectively. The impact of number and location of IMUs on network prediction accuracy was determined via principal component analysis. Comparing across all configurations, three to five IMUs located on the legs and medial trunk were the most promising reduced sensor sets for achieving CoM estimates suitable for outcome assessment. Lastly, the networks were tested on data from an individual with hemiparesis with the greatest error increase in the ML direction, which could stem from asymmetric gait. These results provide a framework for assessing gait deviations after disease or injury and evaluating rehabilitation interventions intended to normalize gait pathologies.

Stabilizing leaning postures with feedback controlled functional neuromuscular stimulation after trunk paralysis.

Spinal cord injury (SCI) can cause paralysis of trunk and hip musculature that negatively impacts seated balance and ability to lean away from an upright posture and interact fully with the environment. Constant levels of electrical stimulation of peripheral nerves can activate typically paralyzed muscles and aid in maintaining a single upright seated posture. However, in the absence of a feedback controller, such seated postures and leaning motions are inherently unstable and unable to respond to perturbations. Three individuals with motor complete SCI who had previously received a neuroprosthesis capable of activating the hip and trunk musculature volunteered for this study. Subject-specific muscle synergies were identified through system identification of the lumbar moments produced via neural stimulation. Synergy-based calculations determined the real-time stimulation parameters required to assume leaning postures. When combined with a proportional, integral, derivative (PID) feedback controller and an accelerometer to infer trunk orientation, all individuals were able to assume non-erect postures of 30-40° flexion and 15° lateral bending. Leaning postures increased forward reaching capabilities by 10.2, 46.7, and 16 cm respectively for each subject when compared with no stimulation. Additionally, the leaning controllers were able to resist perturbations of up to 90 N, and all subjects perceived the leaning postures as moderately to very stable. Implementation of leaning controllers for neuroprostheses have the potential of expanding workspaces, increasing independence, and facilitating activities of daily living for individuals with paralysis.

Feedback control of upright seating with functional neuromuscular stimulation during a reaching task after spinal cord injury: a feasibility study.

BACKGROUND: Restoring or improving seated stability after spinal cord injury (SCI) can improve the ability to perform activities of daily living by providing a dynamic, yet stable, base for upper extremity motion. Seated stability can be obtained with activation of the otherwise paralyzed trunk and hip musculature with neural stimulation, which has been shown to extend upper limb reach and improve seated posture. METHODS: We implemented a proportional, integral, derivative (PID) controller to maintain upright seated posture by simultaneously modulating both forward flexion and lateral bending with functional neuromuscular stimulation. The controller was tested with a functional reaching task meant to require trunk movements and impart internal perturbations through rapid changes in inertia due to acquiring, moving, and replacing objects with one upper extremity. Five subjects with SCI at various injury levels who had received implanted stimulators targeting their trunk and hip muscles participated in the study. Each subject was asked to move a weighted jar radially from a center home station to one of three target stations. The task was performed with the controller active, inactive, or with a constant low level of neural stimulation. Trunk pitch (flexion) and roll (lateral bending) angles were measured with motion capture and plotted against each other to generate elliptical movement profiles for each task and condition. Postural sway was quantified by calculating the ellipse area. Additionally, the mean effective reach (distance between the shoulder and wrist) and the time required to return to an upright posture was determined during reaching movements. RESULTS: Postural sway was reduced by the controller in two of the subjects, and mean effective reach was increased in three subjects and decreased for one. Analysis of the major direction of motion showed return to upright movements were quickened by 0.17 to 0.32 s. A 15 to 25% improvement over low/no stimulation was observed for four subjects. CONCLUSION: These results suggest that feedback control of neural stimulation is a viable way to maintain upright seated posture by facilitating trunk movements necessary to complete reaching tasks in individuals with SCI. Replication of these findings on a larger number of subjects would be necessary for generalization to the various segments of the SCI population.

Trunk Posture from Randomly Oriented Accelerometers.

Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in modules distributed throughout the trunk. Typically, inertial sensors are aligned with the relevant body segment. However, NNP implanted modules are placed according to surgical constraints and their precise locations and orientations are generally unknown. We have developed a method for calibrating multiple randomly oriented accelerometers and fusing their signals into a measure of trunk orientation. Six accelerometers were externally attached in random orientations to the trunks of six individuals with SCI. Calibration with an optical motion capture system resulted in RMSE below 5° and correlation coefficients above 0.97. Calibration with a handheld goniometer resulted in RMSE of 7° and correlation coefficients above 0.93. Our method can obtain trunk orientation from a network of sensors without a priori knowledge of their relationships to the body anatomical axes. The results of this study will be invaluable in the design of feedback control systems for stabilizing the trunk of individuals with SCI in combination with the NNP implanted technology.

Facilitation of dependent transfers with functional neuromuscular stimulation: a computer simulation study.

A two-part simulation process was developed to investigate the facilitation of vertical patient lifts with functional neuromuscular stimulation (FNS) in individuals with spinal cord injury (SCI). First, external lifting forces representing caregiver assistance were applied to a 3D musculoskeletal model representing the patient and optimized to enforce a specific lifting trajectory during a forward dynamic simulation. The process was repeated with and without the activation of the knee, hip, and trunk extensor muscles of the patient model to represent contractions of the paralyzed muscles generated via FNS. Secondly, the spinal compression experienced by a caregiver at the L5/S1 joint while generating these external lifting forces was estimated using a second musculoskeletal model representing the caregiver. Simulation without muscle activation predicted spinal compression in the caregiver model approximately 1.3 × the National Institute for Occupational Safety and Health (NIOSH) recommended "Action Limit." Comparatively, simulations with two unique patterns of muscle activation both predicted caregiver spinal compressions below NIOSH recommendations. These simulation results support the hypothesis that FNS activation of a patient's otherwise paralyzed muscles would lower the force output required of a caregiver during a dependent transfer, thus lowering the spinal compression and risk of injury experienced by a caregiver.

Robust Control of the Human Trunk Posture Using Functional Neuromuscular Stimulation: A Simulation Study.

The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by functional neuromuscular stimulation (FNS), which applies low-level current to the motor nerves to activate the paralyzed muscles to generate useful torques, to actuate the trunk. FNS can be modulated to vary the biotorques to drive the trunk to follow a user-defined reference motion and maintain it at a desired postural set-point. However, a stabilizing modulation policy (i.e., control law) is difficult to derive as the biomechanics of the spine and pelvis are complex and the neuromuscular dynamics are highly nonlinear, nonautonomous, and input redundant. Therefore, a control method that can stabilize it with FNS without knowing the accurate skeletal and neuromuscular dynamics is desired. To achieve this goal, we propose a control framework consisting of a robust control module that generates stabilizing torques while an artificial neural network-based mapping mechanism with an anatomy-based updating law ensures that the muscle-generated torques converge to the stabilizing values. For the robust control module, two sliding-mode robust controllers (i.e., a high compensation controller and an adaptive controller), were investigated. System stability of the proposed control method was rigorously analyzed based on the assumption that the skeletal dynamics can be approximated by Euler-Lagrange equations with bounded disturbances, which enables the generalization of the control framework. We present experiments in a simulation environment where an anatomically realistic three-dimensional musculoskeletal model of the human trunk moved in the anterior- posterior and medial-lateral directions while perturbations were applied. The satisfactory simulation results suggest the potential of this control technique for trunk tracking tasks in a typical clinical environment.

Feedback Control of Upright Seating with Functional Neuromuscular Stimulation during a Functional Task after Spinal Cord Injury: A Case Study.

Seated stability is a major concern of individuals with trunk paralysis. Trunk paralysis is commonly caused by spinal cord injuries (SCI) at or above the thoracic spine. Current methods to improve stability restrict the movement of the user by constraining their trunk to an upright position. Feedback control of functional neuromuscular stimulation (FNS) can help maintain seated stability while still allowing the user to perform movements to accomplish functional tasks. In this study, an individual with a SCI (C7, AIS B) and an implanted stimulator capable of recruiting trunk and hip musculature unilaterally moved a weighted jar on a countertop to and from three prescribed stations directly in front, laterally, and across midline. For comparison, the tasks were performed with constant baseline stimulation and with feedback modulated stimulation based on the tilt of the trunk obtained from an external accelerometer fed into two PID controllers; one for forward trunk pitch and the other for lateral roll. The trunk pitch and roll angles were obtained through motion capture cameras and various measures of postural sway (95% fitted ellipse area, root mean squared (RMS), path length) and the repeatability (coefficient of variation (CoV), variance ratio (VR)) were calculated. Feedback control significantly increased RMS of trunk movement along the major axis of the fitted ellipse, but decreased RMS values during bending along the minor axis of motion. As a result, the fitted ellipse area decreased when deploying the jar to one of the stations and increased with the other two. The CoV indicated reduced variation in the presence of feedback controlled stimulation for all stations, and VR showed higher repeatability in trunk pitch. Plots of the trunk pitch and roll revealed a faster return to upright motion due to feedback stimulation.Clinical relevance- Feedback control in combination with FNS is a viable method to improve seated stability while still allowing dynamic movements in individuals with a SCI, thus addressing a major concern of the population.

Neural Networks Trained via Reinforcement Learning Stabilize Walking of a Three-Dimensional Biped Model With Exoskeleton Applications.

Our group is developing a cyber-physical walking system (CPWS) for people paralyzed by spinal cord injuries (SCI). The current CPWS consists of a functional neuromuscular stimulation (FNS) system and a powered lower-limb exoskeleton for walking with leg movements in the sagittal plane. We are developing neural control systems that learn to assist the user of this CPWS to walk with stability. In a previous publication (Liu et al., Biomimetics, 2019, 4, 28), we showed a neural controller that stabilized a simulated biped in the sagittal plane. We are considering adding degrees of freedom to the CPWS to allow more natural walking movements and improved stability. Thus, in this paper, we present a new neural network enhanced control system that stabilizes a three-dimensional simulated biped model of a human wearing an exoskeleton. Results show that it stabilizes human/exoskeleton models and is robust to impact disturbances. The simulated biped walks at a steady pace in a range of typical human ambulatory speeds from 0.7 to 1.3 m/s, follows waypoints at a precision of 0.3 m, remains stable, and continues walking forward despite impact disturbances and adapts its speed to compensate for persistent external disturbances. Furthermore, the neural network controller stabilizes human models of different statures from 1.4 to 2.2 m tall without any changes to the control parameters. Please see videos at the following link: 3D biped walking control.

Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers.

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm's performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

Characterization of the Force Production Capabilities of Paralyzed Trunk Muscles Activated With Functional Neuromuscular Stimulation in Individuals With Spinal Cord Injury.

Paralysis of the trunk results in seated instability leading to difficulties performing activities of daily living. Functional neuromuscular stimulation (FNS) combined with control systems have the potential to restore some dynamic functions of the trunk. However, design of multi-joint, multi-muscle control systems requires characterization of the stimulation-driven muscles responsible for movement. OBJECTIVE: This study characterizes the input-output properties of paralyzed trunk muscles activated by FNS, and explores co-activation of muscles. METHODS: Four participants with various spinal cord injuries (C7 AIS-B, T4 AIS-B, T5 AIS-A, C5 AIS-C) were constrained so lumbar forces were transmitted to a load cell while an implanted neuroprosthesis activated otherwise paralyzed hip and paraspinal muscles. Isometric force recruitment curves in the nominal seated position were generated by inputting the level of stimulation (pulse width modulation) while measuring the resulting muscle force. Two participants returned for a second experiment where muscles were co-activated to determine if their actions combined linearly. RESULTS: Recruitment curves of most trunk and hip muscles fit sigmoid shaped curves with a regression coefficient above 0.75, and co-activation of the muscles combined linearly across the hip and lumbar joint. Subject specific perturbation plots showed one subject is capable of resisting up to a 300N perturbation anteriorly and 125N laterally; with some subjects falling considerably below these values. CONCLUSION: Development of a trunk stability control system can use sigmoid recruitment dynamics and assume muscle forces combine linearly. SIGNIFICANCE: This study informs future designs of multi-muscle, and multi-dimensional FNS systems to maintain seated posture and stability.

A closed-loop self-righting controller for seated balance in the coronal and diagonal planes following spinal cord injury.

Spinal cord injury (SCI) often results in loss of the ability to keep the trunk erect and stable while seated. Functional neuromuscular stimulation (FNS) can cause muscles paralyzed by SCI to contract and assist with trunk stability. We have extended the results of a previously reported threshold-based controller for restoring upright posture using FNS in the sagittal plane to more challenging displacements of the trunk in the coronal plane. The system was applied to five individuals with mid-thoracic or higher SCI, and in all cases the control system successfully restored upright sitting. The potential of the control system to maintain posture in forward-sideways (diagonal) directions was also tested in three of the subjects. In all cases, the controller successfully restored posture to erect. Clinically, these results imply that a simple, threshold based control scheme can restore upright sitting from forward, lateral or diagonal leaning without a chest strap; and that removal of barriers to upper extremity interaction with the surrounding environment could potentially allow objects to be more readily retrieved from around the wheelchair. Technical performance of the system was assessed in terms of three variables: response time, recovery time and percent maximum deviation from erect. Overall response and recovery times varied widely among subjects in the coronal plane (415±213 ms and 1381±883 ms, respectively) and in the diagonal planes (530±230 ms and 1800±820 ms, respectively). Average response time was significantly lower (p < 0.05) than the recovery time in all cases. The percent maximum deviation from erect was of the order of 40% or less for 9 out of 10 cases in the coronal plane and 5 out of 6 cases in diagonal directions.

Estimating total maximum isometric force output of trunk and hip muscles after spinal cord injury.

Functional neuromuscular stimulation (FNS) can be used to restore seated trunk function in individuals paralyzed due to spinal cord injury (SCI). Musculoskeletal models allow for the design and tuning of controllers for use with FNS; however, these models often use aggregated estimates for parameters of the musculotendon elements, the most significant of which is maximum isometric force (MIF). Stimulated MIF for individuals with SCI is typically assumed to be approximately 50% of the values exhibited by able-bodied muscles, which itself varies between studies and individuals. A method for estimating subject-specific MIF during dynamic motions in individuals with SCI produced by electrical stimulation has been developed to test this assumption and obtained more accurate estimates for biomechanical analysis and controller design. A simple on-off controller was applied to individuals with SCI seated in the workspace of a motion capture system to record joint angles of three types of trunk motions: forward flexion, left and right lateral bending followed by returning, un-aided, to upright posture via neural stimulation delivered to activate the muscles of the hips and trunk. System identification was used with a musculoskeletal model to find the optimal MIF values that reproduced the experimentally observed motions. Experiments with five volunteers with SCI indicate that an MIF of the 50% able-bodied values commonly used is significantly lower than the identified estimates in 33 of 44 muscle groups tested. This suggests that the strengths of paralyzed muscles when stimulated with FNS have been underestimated in many situations and their true force outputs may be higher than the values suggested for use in simulation studies with musculoskeletal models. These findings indicate that subject-specific musculoskeletal models can more closely mimic the motions of subjects by using individualized estimates of MIF, which may allow the design and tuning of controllers while reducing the time spent with subjects in the loop.

Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking.

A control system for bipedal walking in the sagittal plane was developed in simulation. The biped model was built based on anthropometric data for a 1.8 m tall male of average build. At the core of the controller is a deep deterministic policy gradient (DDPG) neural network that was trained in GAZEBO, a physics simulator, to predict the ideal foot placement to maintain stable walking despite external disturbances. The complexity of the DDPG network was decreased through carefully selected state variables and a distributed control system. Additional controllers for the hip joints during their stance phases and the ankle joint during toe-off phase help to stabilize the biped during walking. The simulated biped can walk at a steady pace of approximately 1 m/s, and during locomotion it can maintain stability with a 30 kg·m/s impulse applied forward on the torso or a 40 kg·m/s impulse applied rearward. It also maintains stable walking with a 10 kg backpack or a 25 kg front pack. The controller was trained on a 1.8 m tall model, but also stabilizes models 1.4-2.3 m tall with no changes.

Experimental Implementation of Automatic Control of Posture-Dependent Stimulation in an Implanted Standing Neuroprosthesis.

Knowledge of the upper extremity (UE) effort exerted under real-world conditions is important for understanding how persons with motor or sensory disorders perform the postural shifts necessary to complete many activities of daily living while standing. To this end, a feedback controller, named the "Posture Follower Controller", was developed to aid in task-dependent posture shifting by individuals with spinal cord injury standing with functional neuromuscular stimulation. In this experimental feasibility study, the controller modulated activation to the paralyzed lower extremity muscles as a function of the position of overall center of pressure (CoP), which was prescribed to move in a straight line in forward and diagonal directions. Posture-dependent control of stimulation enabled leaning movements that translated the CoP up to 48 mm away from the nominal position during quiet standing. The mean 95% prediction ellipse area, a measure of the CoP dispersion in the forward, forward-right, and forward-left directions, was 951.0 ± 341.1 mm2, 1095.9 ± 251.2 mm2, and 1364.5 ± 688.2 mm2, respectively. The average width of the prediction ellipses across the three directions was 15.1 mm, indicating that the CoP deviated from the prescribed path as task-dependent postures were assumed. The average maximal UE effort required to adjust posture across all leaning directions was 24.1% body weight, which is only slightly more than twice of what is required to maintain balance in an erect standing posture. These preliminary findings suggest that stimulation can be modulated to effectively assume user-specified, task-dependent leaning postures characterized by the CoP shifts that deviate away from the nominal position and which require moderate UE effort to execute.

Automatic application of neural stimulation during wheelchair propulsion after SCI enhances recovery of upright sitting from destabilizing events.

BACKGROUND: The leading cause of injury for manual wheelchair users are tips and falls caused by unexpected destabilizing events encountered during everyday activities. The purpose of this study was to determine the feasibility of automatically restoring seated stability to manual wheelchair users with spinal cord injury (SCI) via a threshold-based system to activate the hip and trunk muscles with electrical stimulation during potentially destabilizing events. METHODS: We detected and classified potentially destabilizing sudden stops and turns with a wheelchair-mounted wireless inertial measurement unit (IMU), and then applied neural stimulation to activate the appropriate muscles to resist trunk movement and restore seated stability. After modeling and preliminary testing to determine the appropriate inertial signatures to discriminate between events and reliably trigger stimulation, the system was implemented and evaluated in real-time on manual wheelchair users with SCI. Three participants completed simulated collision events and four participants completed simulated rapid turns. Data were analyzed as a series of individual case studies with subjects acting as their own controls with and without the system active. RESULTS: The controller achieved 93% accuracy in detecting collisions and right turns, and 100% accuracy in left turn detection. Two of the three subjects who participated in collision testing with stimulation experienced significantly decreased maximum anterior-posterior trunk angles (p < 0.05). Similar results were obtained with implanted and surface stimulation systems. CONCLUSIONS: This study demonstrates the feasibility of a neural stimulation control system based on simple inertial measurements to improve trunk stability and overall safety of people with spinal cord injuries during manual wheelchair propulsion. Further studies are required to determine clinical utility in real world situations and generalizability to the broader SCI or other population of manual or powered wheelchair users. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01474148 . Registered 11/08/2011 retrospectively registered.

Control of standing balance at leaning postures with functional neuromuscular stimulation following spinal cord injury.

This study systematically explored the potential of applying feedback control of functional neuromuscular stimulation (FNS) for stabilizing various erect and leaning standing postures after spinal cord injury (SCI). Perturbations ranging from 2 to 6% body weight were applied to two subjects with motor complete thoracic level SCI who were proficient at standing with implanted multichannel neural stimulators to activate the ankle, knee, hip and trunk muscles. The subjects stood with four different postures: erect, forward, forward-right and forward-left. Repeatable and controlled perturbations were applied in the forward, backward, rightward and leftward directions by linear actuators pulling on ropes attached to the subjects via a belt worn just above the waist. Upper extremity (UE) forces exerted on a stationary walker were measured with load cells attached to the handles. A feedback controller based on center of pressure (CoP) varied the stimulation levels to the otherwise paralyzed muscles so as to resist the effects of the perturbations. The effect of the feedback controller was compared to the case where only open-loop baseline stimulation was applied. This was done in terms of: (a) maximum resultant UE force exerted by the subjects on the walker, (b) maximum resultant CoP overshoot and (c) CoP root-mean-square deviation (RMSD). Feedback control resulted in significant reductions in the mean values of the majority of outcome values compared to baseline open-loop stimulation. Maximum resultant UE force was reduced by as much as 50% in one of the postures for one of the subjects. RMSD and maximum CoPs were reduced by as much as 75 and 70%, respectively, with feedback control. These results indicate that feedback control can be used to reject destabilizing disturbances in individuals with SCI using FNS not only for erect postures but also for leaning postures typically adopted during reaching while attempting various activities of daily living.

Reactive stepping with functional neuromuscular stimulation in response to forward-directed perturbations.

BACKGROUND: Implanted motor system neuroprostheses can be effective at increasing personal mobility of persons paralyzed by spinal cord injuries. However, currently available neural stimulation systems for standing employ patterns of constant activation and are unreactive to changing postural demands. METHODS: In this work, we developed a closed-loop controller for detecting forward-directed body disturbances and initiating a stabilizing step in a person with spinal cord injury. Forward-directed pulls at the waist were detected with three body-mounted triaxial accelerometers. A finite state machine was designed and tested to trigger a postural response and apply stimulation to appropriate muscles so as to produce a protective step when the simplified jerk signal exceeded predetermined thresholds. RESULTS: The controller effectively initiated steps for all perturbations with magnitude between 10 and 17.5 s body weight, and initiated a postural response with occasional steps at 5% body weight. For perturbations at 15 and 17.5% body weight, the dynamic responses of the subject exhibited very similar component time periods when compared with able-bodied subjects undergoing similar postural perturbations. Additionally, the reactive step occurred faster for stronger perturbations than for weaker ones (p < .005, unequal varience t-test.) CONCLUSIONS: This research marks progress towards a controller which can improve the safety and independence of persons with spinal cord injury using implanted neuroprostheses for standing.

A muscle-driven approach to restore stepping with an exoskeleton for individuals with paraplegia.

BACKGROUND: Functional neuromuscular stimulation, lower limb orthosis, powered lower limb exoskeleton, and hybrid neuroprosthesis (HNP) technologies can restore stepping in individuals with paraplegia due to spinal cord injury (SCI). However, a self-contained muscle-driven controllable exoskeleton approach based on an implanted neural stimulator to restore walking has not been previously demonstrated, which could potentially result in system use outside the laboratory and viable for long term use or clinical testing. In this work, we designed and evaluated an untethered muscle-driven controllable exoskeleton to restore stepping in three individuals with paralysis from SCI. METHODS: The self-contained HNP combined neural stimulation to activate the paralyzed muscles and generate joint torques for limb movements with a controllable lower limb exoskeleton to stabilize and support the user. An onboard controller processed exoskeleton sensor signals, determined appropriate exoskeletal constraints and stimulation commands for a finite state machine (FSM), and transmitted data over Bluetooth to an off-board computer for real-time monitoring and data recording. The FSM coordinated stimulation and exoskeletal constraints to enable functions, selected with a wireless finger switch user interface, for standing up, standing, stepping, or sitting down. In the stepping function, the FSM used a sensor-based gait event detector to determine transitions between gait phases of double stance, early swing, late swing, and weight acceptance. RESULTS: The HNP restored stepping in three individuals with motor complete paralysis due to SCI. The controller appropriately coordinated stimulation and exoskeletal constraints using the sensor-based FSM for subjects with different stimulation systems. The average range of motion at hip and knee joints during walking were 8.5°-20.8° and 14.0°-43.6°, respectively. Walking speeds varied from 0.03 to 0.06 m/s, and cadences from 10 to 20 steps/min. CONCLUSIONS: A self-contained muscle-driven exoskeleton was a feasible intervention to restore stepping in individuals with paraplegia due to SCI. The untethered hybrid system was capable of adjusting to different individuals' needs to appropriately coordinate exoskeletal constraints with muscle activation using a sensor-driven FSM for stepping. Further improvements for out-of-the-laboratory use should include implantation of plantar flexor muscles to improve walking speed and power assist as needed at the hips and knees to maintain walking as muscles fatigue.

Restoring standing capabilities with feedback control of functional neuromuscular stimulation following spinal cord injury.

This paper reviews the field of feedback control for neuroprosthesis systems that restore advanced standing function to individuals with spinal cord injury. Investigations into closed-loop control of standing by functional neuromuscular stimulation (FNS) have spanned three decades. The ultimate goal for FNS standing control systems is to facilitate hands free standing and enabling the user to perform manual functions at self-selected leaning positions. However, most clinical systems for home usage currently only provide basic upright standing using preprogrammed stimulation patterns. To date, online modulation of stimulation to produce advanced standing functions such as balance against postural disturbances or the ability to assume leaning postures have been limited to simulation and laboratory investigations. While great technological advances have been made in biomechanical sensing and interfaces for neuromuscular stimulation, further progress is still required for finer motor control by FNS. Another major challenge is the development of sophisticated control schemes that produce the necessary postural adjustments, adapt against accelerating muscle fatigue, and consider volitional actions of the intact upper-body of the user. Model-based development for novel control schemes are proven and sensible approaches to prototype and test the basic operating efficacy of potentially complex and multi-faceted control systems. The major considerations for further innovation of such systems are summarized in this paper prior to describing the evolution of closed-loop FNS control of standing from previous works. Finally, necessary emerging technologies to for implementing FNS feedback control systems for standing are identified. These technological advancements include novel electrodes that more completely and selectively activate paralyzed musculature and implantable sensors and stimulation modules for flexible neuroprosthesis system deployment.

Simulating the restoration of standing balance at leaning postures with functional neuromuscular stimulation following spinal cord injury.

In this simulation study, we present and examine methods to develop a feedback controller for a neuroprosthesis that restores forward and side leaning function during standing following complete thoracic-level spinal cord injury. Achieving leaning postures away from erect stance with functional neuromuscular stimulation (FNS) would allow users to extend their reaching capabilities. Utilizing a 3-D computer model of human stance, an FNS control system based on total-body center of mass (CoM) kinematics (position, acceleration) is developed and tested in simulation. CoM kinematics drive an artificial neural network to modulate muscle excitations and reduce the upper extremity loading, presumably against a walker or similar support surface, required to resist the effects of postural perturbations. Furthermore, a novel method to robustly estimate the feedback kinematics for standing applications is also presented while assuming 3-D accelerometer signals at locations consistent with a proposed implantable networked neuroprosthesis system. For shifting and balance at leaning postures, respectively, center of mass position and acceleration could be approximated to within 20% of the maximum value, with strong correlations (R > 0.9) between values estimated by the proposed method and the true values derived from model dynamics. When utilizing the estimated feedback kinematics for FNS control, standing performance in terms of maximum upper extremity loading was still significantly reduced (p < 0.001) compared to conventionally applying constant and maximal stimulation. In the future, these simulation-based methods will be employed to develop experimental approaches for restoring leaning standing function by FNS.