Gait Event Detector Research Articles

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

BackgroundFunctional 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.MethodsThe 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.ResultsThe 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.ConclusionsA 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.

A stimulation-driven exoskeleton for walking after paraplegia.

An untethered version of a stimulation-driven exoskeleton was evaluated for its ability to restore walking after paralysis from spinal cord injury. The hybrid neuroprosthesis (HNP) combined a passive variable-constraint exoskeleton for stability and support with functional neuromuscular stimulation (FNS) to contract the paralyzed muscles to drive limb movement. This self-contained HNP was operated by an onboard controller that sampled sensor signals, generated appropriate commands to both the exoskeletal constraints and integrated stimulator, and transmitted data wirelessly via Bluetooth to an off-board computer for real-time monitoring and recording for offline analysis. The subject selected the desired function (i.e. standing up, stepping, or sitting down) by means of a wireless finger switch that communicated with the onboard controller. Within the stepping function, a gait event detector supervisory controller transitioned between the different phases of gait such as double stance, swing, and weight acceptance based on signals from sensors incorporated into the exoskeleton. The different states of the control system governed the locking and unlocking of the exoskeletal hip and knee joints as well as the stimulation patterns activating hip and knee flexor or extensor muscles at the appropriate times and intensities to enable stepping. This study was one of our first successful implementations of the self-contained "muscle-first" HNP and successfully restored gait to an individual with motor complete mid-thoracic paraplegia.

An oscillator-based smooth real-time estimate of gait phase for wearable robotics

This paper presents a novel methodology for estimating the gait phase of human walking through a simple sensory apparatus. Three subsystems are combined: a primary phase estimator based on adaptive oscillators, a desired gait event detector and a phase error compensator. The estimated gait phase is expected to linearly increase from 0 to 2$$\pi $$ź rad in one stride and remain continuous also when transiting to the next stride. We designed two experimental scenarios to validate this gait phase estimator, namely treadmill walking at different speeds and free walking. In the case of treadmill walking, the maximum phase error at the desired gait events was found to be 0.155 rad, and the maximum phase difference between the end of the previous stride and beginning of the current stride was 0.020 rad. In the free walking trials, phase error at the desired gait event was never larger than 0.278 rad. Our algorithm outperformed against two other benchmarked methods. The good performance of our gait phase estimator could provide consistent and finely tuned assistance for an exoskeleton designed to augment the mobility of patients.

Walking speed estimation using foot-mounted inertial sensors: Comparing machine learning and strap-down integration methods

In this paper we implemented machine learning (ML) and strap-down integration (SDI) methods and analyzed them for their capability of estimating stride-by-stride walking speed. Walking speed was computed by dividing estimated stride length by stride time using data from a foot mounted inertial measurement unit. In SDI methods stride-by-stride walking speed estimation was driven by detecting gait events using a hidden Markov model (HMM) based method (HMM-based SDI); alternatively, a threshold-based gait event detector was investigated (threshold-based SDI). In the ML method a linear regression model was developed for stride length estimation. Whereas the gait event detectors were a priori fixed without training, the regression model was validated with leave-one-subject-out cross-validation. A subject-specific regression model calibration was also implemented to personalize the ML method.Healthy adults performed over-ground walking trials at natural, slower-than-natural and faster-than-natural speeds. The ML method achieved a root mean square estimation error of 2.0% and 4.2%, with and without personalization, against 2.0% and 3.1% by HMM-based SDI and threshold-based SDI. In spite that the results achieved by the two approaches were similar, the ML method, as compared with SDI methods, presented lower intra-subject variability and higher inter-subject variability, which was reduced by personalization.

Online decoding of hidden Markov models for gait event detection using foot-mounted gyroscopes.

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.

A Low-Cost Biofeedback System for Electromyogram-Triggered Functional Electrical Stimulation Therapy: An Indo-German Feasibility Study

Functional electrical stimulation (FES) facilitates ambulatory function after paralysis by activating the muscles of the lower extremities. The FES-assisted stepping can either be triggered by a heel-swich, or by an electromyogram-(EMG-) based gait event detector. A group of six chronic (>6 months poststroke) hemiplegic stroke survivors underwent transcutaneous FES-assisted training for 1 hour on stepping task with EMG biofeedback from paretic tibialis anterior (TA) and medial gastrocnemius (GM) muscles, where the stimulation of the paretic TA or GM was triggered with surface EMG from the same muscle. During the baseline, postintervention, and 2-day-postintervention assessments, a total of 5 minutes of surface EMG was recorded from paretic GM and TA muscles during volitional treadmill walking. Two-way ANOVA showed significant effects in terms of P values for the 6 stroke subjects, 0.002, the 3 assessments, 0, and the interaction between subjects and assessments, 6.21E-19. The study showed a significant improvement from baseline in paretic GM and TA muscles coordination during volitional treadmill walking. Moreover, it was found that the EMG-triggered FES-assisted therapy for stand-to-walk transition helped in convergence of the deviation in centroidal angular momentum from the normative value to a quasi-steady state during the double-support phase of the nonparetic. Also, the observational gait analysis showed improvement in ankle plantarflexion during late stance, knee flexion, and ground clearance of the foot during swing phase of the gait.

Nonlinear analysis of electromyogram following gait training with myoelectrically triggered neuromuscular electrical stimulation in stroke survivors

Neuromuscular electrical stimulation (NMES) facilitates ambulatory function after paralysis by activating the muscles of the lower extremities. The NMES-assisted stepping can either be triggered by a heel-switch (switch-trigger), or by an electromyogram (EMG)-based gait event detector (EMG-trigger). The command sources—switch-trigger or EMG-trigger—were presented to each group of six chronic (>6 months post-stroke) hemiplegic stroke survivors. The switch-trigger group underwent transcutaneous NMES-assisted gait training for 1 h, five times a week for 2 weeks, where the stimulation of the tibialis anterior muscle of the paretic limb was triggered with a heel-switch detecting heel-rise of the same limb. The EMG-trigger group underwent transcutaneous NMES-assisted gait training of the same duration and frequency where the stimulation was triggered with surface EMG from medial gastrocnemius (MG) of the paretic limb in conjunction with a heel-switch detecting heel-rise of the same limb. During the baseline and post-intervention surface EMG assessment, a total of 10 s of surface EMG was recorded from bilateral MG muscle while the subjects tried to stand steady on their toes. A nonlinear tool—recurrence quantification analysis (RQA)—was used to analyze the surface EMG. The objective of this study was to find the effect of NMES-assisted gait training with switch-trigger or EMG-trigger on two RQA parameters—the percentage of recurrence (%Rec) and determinism (%Det), which were extracted from surface EMG during fatiguing contractions of the paretic muscle. The experimental results showed that during fatiguing contractions, (1) %Rec and %Det have a higher initial value for paretic muscle than the non-paretic muscle, (2) the rate of change in %Rec and %Det was negative for the paretic muscle but positive for the non-paretic muscle, (3) the rate of change in %Rec and %Det significantly increased from baseline for the paretic muscle after EMG-triggered NMES-assisted gait training. Therefore, the study showed an improvement in paretic muscle function during a fatiguing task following gait training with EMG-triggered NMES. This study also showed that RQA parameters—%Rec and %Det—were sensitive to changes in paretic/non-paretic muscle properties due to gait training and can be used for non-invasive muscle monitoring in stroke survivors undergoing rehabilitation.

Real-time gait event detection for paraplegic FES walking.

A real-time method for the detection of gait events that occur during the electrically stimulated locomotion of paraplegic subjects is described. It consists of a two-level algorithm for the processing of sensor signals and the determination of gait event times. Sensor signals and information about the progression of the stimulator though its pre-specified stimulation "pattern" are processed by a machine intelligence (fuzzy logic) algorithm to determine an initial estimate of the patient's current phase of gait. This is then reviewed and modified by a second algorithm that removes spurious gait estimates, and determines gait event times. These gait event times are known to the system within approximately one-half of a gait cycle. The resulting gait event detection system was successfully evaluated on three subjects. Detection accuracy is not adversely affected by day-to-day gait variability. This work resolved technical and practical issues that previously limited the real time application of these methods. In particular, cosmetically acceptable insole force transducers were used. This gait event detector is designed for use in a real time controller for the automatic adjustment of the intensity and timing of stimulation while the subject is walking using functional electrical stimulation (FES).

Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation

Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.