Stroke survivors often experience impaired neuromechanical control that limits walking speed and quality, particularly due to deficits in paretic propulsion. This study aims to identify patient-specific neuromechanical locomotor control strategies, link them to biomechanical gait impairments, and investigate how these strategies influence responses to soft robotic exosuit assistance of paretic propulsion and ground clearance during walking. The study focuses on adults who are more than six months post-stroke and have observable gait deficits. The main questions are: 1. How do neuromechanical control patterns (i.e., electromyography-measured muscle coordination) affect walking speed, quality, and gait biomechanics after stroke? 2. Do individuals with distinct neuromechanical patterns respond differently to robotic exosuit-assisted gait rehabilitation? Researchers will compare walking performance without and with robotic exosuit assistance to determine whether tailoring exosuit-assisted gait intervention to patient-specific neuromechanical profiles can lead to greater improvements in walking function. Participants will complete treadmill and overground walking assessments instrumented with motion capture, EMG, and force plates, performing one trial without assistance and two trials with robotic exosuit assistance delivered at different assistance onset timings, from which a preferred assistance setting will be identified. The walking trial associated with the preferred assistance setting will be used for primary analyses.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
22
Subjects will complete two trials of 3-minute treadmill walking with active robotic exosuit assistance, from which a preferred assistance profile will be identified. The treadmill walk associated with the preferred profile will be used for primary analyses.
Boston University Neuromotor Recovery Laboratory
Boston, Massachusetts, United States
Dynamic Motor Control Index (DMCI)
Difference in neuromuscular control quality compared to normative data with and without exosuit
Time frame: Assisted - Baseline
Correlation Between Propulsion and Weight-Acceptance Motor Modules (Temporal)
Difference in the merging of motor module structures quantified by correlation coefficient between the propulsion temporal module and weight-acceptance temporal module, computed from EMG data by non-negative matrix factorization, with and without exosuit
Time frame: Assisted - Baseline
Correlation Between Weight-Acceptance and Swing-Limb Deceleration Motor Modules (Temporal)
Difference in the merging of motor module structures quantified by correlation coefficient between the weight-acceptance temporal module and swing-limb deceleration temporal module, computed from EMG data by non-negative matrix factorization, with and without exosuit
Time frame: Assisted - Baseline
Variance Accounted For (VAF) by Four Muscle Synergies
Difference in the variance in muscle activation accounted for by the 4-synergy model, measuring the quantitative shift in muscle coordination complexity with and without exosuit (%)
Time frame: Assisted - Baseline
Paretic Propulsion
Difference in anterior-posterior ground reaction force with and without exosuit (N)
Time frame: Assisted - Baseline
Joint Angle
Difference in joint angles computed using inverse dynamics, including ankle, knee, and hip angles, with and without exosuit (degrees)
Time frame: Assisted - Baseline
Joint Torque
Difference in joint torques computed using inverse dynamics, including ankle, knee, and hip torques, with and without exosuit (Nm/kg)
Time frame: Assisted - Baseline
Joint Power
Difference in joint power computed using inverse dynamics, including ankle, knee, and hip power, with and without exosuit (W/kg)
Time frame: Assisted - Baseline
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