Stimulation Combined With Powered Motorized Orthoses for Walking After Stroke

Summary

Participation Requirements

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 da...

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. Screening After signing the informed consent form the subject will undergo screening to determine if an individual is eligible to participate in the study based on the inclusion/exclusion criteria. During screening the investigators will also collect information about stroke demographics (e.g. date of stroke, type of stroke, lesion location, side of impairment), medications, and leg and foot size to ensure the investigators have appropriately sized orthotic components during subsequent sessions. Baseline testing After meeting all inclusion/exclusion criteria and agreeing to participate, initial testing will determine participants' impairment level and walking ability prior to controller development and training with the device. Walking tests will be completed in the laboratory, hospital hallways, and the environment surrounding the hospital. Outcomes will measure impairment in the legs, walking ability, and participants perceptions of the device and the effect on walking. NMGA Fitting and Tuning The study team will work with the participant to fit the device and determine appropriate stimulation patterns to assist walking. Fitting includes choosing orthotic components that fit well on the wearer's leg and allow comfortable walking. Stimulation patterns will be generated for each individual. Electrodes will target movement at the hip, knee, and ankle on the affected side. During stimulation tuning the investigators will adjust surface stimulation electrode locations as well as stimulus timing and intensity during the gait cycle. Orthotic fitting and stimulation pattern creation are expected to take about two sessions. Assessments may be repeated with surface stimulation assistance. Controller Development During several sessions, the participant will complete mobility tasks (i.e. walking, stair climbing, and sit-to-stand transitions) while walking with the NMGA and recording data to characterize walking (Quantitative Motion Analysis data and orthosis mounted sensor data). These data will be used to create and optimize the controller to estimate phases of gaits (e.g. initial contact, pre-swing, mid-swing) based on exoskeleton mounted sensors to coordinate NMGA assistance with walking ability. Participants will complete tasks while different algorithms control transitions between tasks (e.g. walking to stair climbing) and assistance during task completion. Controller parameters will be adjusted during this process to optimize assistance and determine which task transitions the sensors successfully detect and which transitions should be controlled by a separate user input. Contact guard assistance will be provided to prevent falls during controller development. During this process participants will be interviewed in an open discussion format to get feedback about aspects of the device that could be improved. Up to 16 sessions will be used for controller development. Gait Training After determining a control algorithm, there will be six sessions of training to use the device for walking, stair climbing, and sit-to-stand transitions as appropriate. Gait training will be conducted by the study physical therapist in the gait laboratory, hospital hallways, and surrounding outdoor spaces. The physical therapist will provide standby assistance, monitor subjects' vital signs, record their progress and solicit feedback on the use of the NMGA. Training will focus on increasing walking speed while maintaining safety, specifically toe clearance in swing, stability in stance, and situational awareness to the surrounding environment. Participants will provide feedback about when during the process they feel comfortable donning, doffing, and using the device without study staff assistance. The study physical therapist will also provide input about when participants are capable of using the system independently. Post-Training Assessment Following training with the NMGA, the previously described assessments (see Baseline Testing) will be repeated both with and without the device to test the hypothesis that walking with the NMGA compared to without enhances walking speed, endurance, metabolic consumption, and gait symmetry. In addition to the tests at baseline, participants will complete the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), a survey to assess user satisfaction with a device. Additionally, each participant will fill out a worksheet prioritizing different design requirements (e.g. size, weight, ease of use). Up to six sessions will be used for final testing.

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