Stimulation Combined With Externally Powered Motorized Orthoses for Stroke
Trial Snapshot
- Phase
- Not Applicable
- Status
- Active, not recruiting
- Enrollment
- 5
- Locations
- 1
- Primary Endpoint
- Controller accuracy
Study Overview
Brief Summary
Objective: The goal of this study is to implement and test a neuro-mechanical gait assist (NMGA) device to correct walking characterized by muscle weakness, incoordination or excessive tone in Veterans with hemiparesis after stroke that adversely affects their ability to walk, exercise, perform activities of daily living, and participate fully in personal, professional and social roles.
Research Plan: A prototype NMGA device will be used to develop a finite state controller (FSC) to coordinate each user's volitional effort with surface muscle stimulation and motorized knee assistance as needed. Brace mounted sensors will be used to develop a gait event detector (GED) which will serve the FSC to advance through the phases of gait or stair climbing. In addition, a rule-base intent detection algorithm will be developed using brace mounted sensors and user interface input to select among various functions including walking, stairs climbing, sit-to-stand and stand-to-sit maneuvers. The FSC controller tuning and intent algorithm development and evaluation will be on pilot subjects with difficulty walking after stroke. Outcome measures during development will provide specifications for a new prototype NMGA design which will be evaluated on pilot subjects to test the hypothesis that the NMGA improves walking speed, distance and energy consumption of walking. These baseline data and device will be used to design a follow-up clinical trial to measure orthotic impact of NMGA on mobility in activities of daily living at home and community.
Methodology: After meeting inclusion criteria, pilot subjects will undergo baseline gait evaluation with EMG activities of knee flexors and extensors, ankle plantar and dorsiflexors and isokinetic knee strength and passive resistance. They will be fitted with a NMGA combining a knee-ankle-foot-orthosis with a motorized knee joint and surface neuromuscular stimulation of plantar- and dorsi- flexors, vasti and rectus femoris. Brace mounted sensor data will be used for gait event detector (GED) algorithm development and evaluation. The GED will serve the FSC to proceed through phases of gait based on supervisory rule-based user intent recognition algorithm detected by brace mounted sensors and user input interface. The FSC will coordinate feed-forward control of tuned stimulation patterns and closed-loop controlled knee power assist as needed to control foot clearance during swing and stability of the knee during stance. Based on data attained during controller development and evaluation, a new prototype NMGA will be design, constructed and evaluated on pilot subjects to test the hypothesis that a NMGA device improves safety and stability, increases walking speed and distance and minimizes user effort.
Clinical Significance: The anticipated outcome is improved gait stability with improved swing knee flexion, thus, increasing the safety and preventing injurious falls of ambulatory individuals with hemiplegia due to stroke found in large and ever-increasing numbers in the aging Veteran population. Correcting gait should lead to improved quality of life and participation.
Detailed Description
This study includes controller development and feasibility testing for a hybrid neuromuscular gait assist (NMGA) system to enhance walking after stroke. The study consists of baseline testing, fitting the device on participants, tuning assistance parameters to enhance walking, collecting movement data with and without the device, modifying controller designs to optimize walking, sit-to-stand transitions, and stair climbing, gait training, and evaluating movement capability with and without device assistance. Heart rate and blood pressure will be monitored during each session.
Device Description The NMGA is comprised of a motorized knee brace and surface electrical stimulation applied to muscles acting across the hip, knee, and ankle. The device is worn on the impaired side of the body with an orthotic interface attaching it to the leg. The goal of combining the powered knee with stimulation is to improve leg movement and coordination for safer walking at reduced user effort. The powered exoskeletal knee ensures adequate toe clearance during the swing phase of gait by generating knee flexion and then prevents knee buckling during the stance phase of gait by maintaining extension for support. Surface muscle stimulation assists with user volitional effort. Stimulation applied to ankle dorsiflexors assists with toe clearance during swing while quadriceps stimulation assists with stance. Gastrocnemius and rectus femoris stimulation assist with push-off and swing to improve walking speed.
This study focuses on developing and testing control methods that integrate assistance of stimulation and the powered knee with volitional activity in a manner that maximizes the user's own muscle contribution. Orthosis mounted sensors measure motion, joint angles, interaction forces and foot-floor contact to determine the current phase of gait and assistance required. A Gait Event Detector (GED) determines the phase of gait and appropriate control state based on sensor data and then a Finite State Controller (FSC) optimizes stimulation and motor assistance in coordination with volitional effort. The controller design incorporates feedforward control of stimulation with stimulation triggered by detection of different gait events. Feedback control is applied for motor assistance only as needed. This study will evaluate different algorithms to detect different phases of gait. Controller refinement is an iterative process of testing different algorithms, adjusting detection parameters, and adjusting assistance parameters.
In addition to detecting phases of gait during walking, this study will also develop algorithms to detect user intent for mobility task transitions. Beyond overground walking, mobility includes transitions between sitting and standing as well as stair climbing. Depending on command signal robustness, these transitions could be achieved through separate inputs (e.g. a smart phone app or orthosis mounted buttons) to inform the device when to change task states or may be detectable based on the user's motions. As part of this study the investigators will test different approaches to determine the safest effective option and user preferences.
The following describes the participation involved in this development process.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Other
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to 75 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •More than 6 months post stroke.
- •Stiff-legged gait defined as a gait pattern manifesting as "dragging" or "catching" of the affected toes during swing phase of gait or use of compensatory strategies such as circumducting the affected limb, vaulting with the unaffected limb or hiking the affected hip.
- •Sufficient endurance and motor ability to ambulate at least 10ft continuously with standby assist.
- •Weakness at the hip, knee and ankle.
- •Poor lower extremity coordination due to weakness or tone.
- •Hip extension range to neutral.
- •Hip flexion range greater or equal to 90 degrees.
- •Passive range of ankle dorsiflexion to neutral with knee extended.
- •Sufficient upper extremity function to use a cane.
Exclusion Criteria
- •Severe knee extensor tone requiring >25Nm of torque to flex the knee.
- •Ankle contractures of more than 0 degrees of plantar flexion and hip contractures of greater than 0 degrees of hip flexion.
- •Inability to grasp with both hands.
- •History of potentially fatal cardiac arrhythmias such as ventricular tachycardia, supra-ventricular tachycardia, and rapid ventricular response atrial fibrillation with hemodynamic instability.
- •Presence of a demand pacemaker.
- •Parkinson's Disease.
- •Edema of the affected limb.
- •Active pressure ulcers or wounds in lower extremities.
- •Sepsis or active infection.
- •Severe osteoporosis.
- •Uncontrolled seizures.
- •Presence of substance abuse.
- •Severely impaired cognition and communication.
- •Uncompensated hemineglect.
- •Pregnancy.
Arms & Interventions
Neuromechanical Gait Assist
All participants will participate in developing controllers to coordinate device assistance with walking ability. Walking will be compared before gait training and after gait training. Walking will be evaluated both with and without device assistance.
Intervention: Neuromechanical Gait Assist (Device)
Outcomes
Primary Outcomes
Controller accuracy
Time Frame: up to one year
The accuracy of the controller (True/False positives and negatives) in detecting gait events and gait transitions.
Secondary Outcomes
- Modified Ashworth scale(up to one year after baseline)
- Instrumented impairment measures - strength(up to one year after baseline)
- Quantitative motion analysis - electromyograms(up to one year after baseline)
- Quantitative motion analysis - kinematics(up to one year after baseline)
- Oxygen consumption(up to one year after baseline)
- Fugl-Meyer Motor Assessment(up to one year after baseline)
- Instrumented impairment measures - joint stiffness(up to one year after baseline)
- Quantitative motion analysis - kinetics(up to one year after baseline)
- 6 minute timed walk(up to one year after baseline)
- 10m walk test(up to one year after baseline)
- Timed up and go test(up to one year after baseline)
- Manual Muscle Test(up to one year after baseline)
- 10m walk test(At baseline)
- Quantitative motion analysis - kinematics(At baseline)
- 6 minute timed walk(At baseline)
- Oxygen consumption(At baseline)
- Timed up and go test(At baseline)
- Manual Muscle Test(At baseline)
- Modified Ashworth scale(At baseline)
- Fugl-Meyer Motor Assessment(At baseline)
- Instrumented impairment measures - joint stiffness(At baseline)
- Instrumented impairment measures - strength(At baseline)
- Quantitative motion analysis - kinetics(At baseline)
- Quantitative motion analysis - electromyograms(At baseline)