The overall goal of this project is to establish a novel design and control paradigm for modular, partial-assist powered orthoses (exoskeletons) to enhance voluntary lower-limb motion and manage pain in broad patient populations. Building upon a previous study period that addressed weakness from advanced age or muscle fatigue, this current period extends the technology to novel powered unloader orthoses designed to manage knee osteoarthritis (OA) pain. The investigators hypothesize that by providing 15-30% of biological joint torque, these motorized devices can reduce muscular contributions to painful loads on the joint's surfaces during activities of daily living (ADLs). The project aims to develop a task-agnostic, neural network-based controller and establish the feasibility of reducing knee pain and muscle effort in individuals with multi-compartment knee osteoarthritis.
The overall goal of this project is to develop modular, lower-limb, powered orthoses that fit to user-specific joints and control torque in a manner that enhances voluntary motion and mitigates musculoskeletal pain. Commercial exoskeletons typically use actuation and control methods that force the human user to follow specific, rigid gait patterns. This has prevented emerging wearable robotics from effectively addressing the weakness and pain associated with mild to moderate impairments, such as knee osteoarthritis (OA). These populations require partial, task-agnostic assistance that works harmoniously with their voluntary motion rather than constraining it. To bridge this gap, this project utilizes a quasi-direct drive actuation paradigm, a high-torque motor combined with a low-ratio transmission, integrated into conventional knee stabilizer and unloader braces. This hardware is uniquely capable of producing large output torques without causing perceptible resistance when backdriven by the human joint. To control the device across various activities of daily living (ADLs) without requiring pre-programmed trajectories, the investigators are developing a neural network-based formulation of "energy shaping" (effectively combining virtual springs, dampers, and gravity/inertia compensation) trained on multi-activity human data. The specific objectives of this study period are to: 1. Integrate the modular quasi-direct drive actuator and miniaturized electronics into both a modified knee stabilizer brace and a modified unloader knee brace. 2. Implement and validate the neural network-based energy shaping controller to provide task-agnostic, biomimetic torque assistance with able-bodied subjects. 3. Establish the clinical feasibility of this technology in individuals with multi-compartment knee OA. The investigators hypothesize that the biomimetic torque assistance provided by these motorized braces will significantly reduce quadriceps effort, knee joint moment loads, and subjective pain across ADLs.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
65
This study will investigate modular, lower-limb, powered orthoses that fit to user-specific weakened joints and control force/torque in a manner that enhances voluntary motion in broad patient populations. The central hypothesis is that high-torque, low-inertia motor systems controlled with energetic objectives will enable modular powered orthoses to partially assist the joints. High-torque electric motors combined with minimal transmissions can be freely rotated (i.e., backdriven) by human joints, allowing the use of an emerging torque control method called energy shaping to reduce the perceived weight/inertia of the body during any motion. By mounting these modular actuators to commercial orthoses, this technology will be easily prescribed/configured by clinicians.
Rehab Lab
Ann Arbor, Michigan, United States
RECRUITINGMuscle effort
Muscle activity will be measured by surface EMG electrodes, and EMG sites will be averaged by flexors and extensors. For each condition (no orthosis vs. powered orthosis), signals will be normalized by their peaks during the no-orthosis condition, and then averaged over cycle, repetitions, and tasks to obtain overall muscle effort (focusing on knee extension for the powered knee orthosis, hip extension and flexion for the powered hip orthosis, and ankle plantarflexion for the powered ankle orthosis).
Time frame: Baseline and through study completion, an average of 2 months
Joint moment load
Biological torques will be estimated by subtracting the assistive torques from the total joint torques computed via inverse dynamics (using OpenSim). Biological torques will be divided into flexion and extension. For each condition (no orthosis vs. powered orthosis), signals will be normalized by their peaks during the no-orthosis condition, and then averaged over cycle, repetitions, and tasks to obtain overall joint moment load (focusing on knee extension for the powered knee orthosis, hip extension and flexion for the powered hip orthosis, and ankle plantarflexion for the powered ankle orthosis).
Time frame: Baseline and through study completion, an average of 2 months
Pain score
The Knee Injury and Osteoarthritis Outcome Score (KOOS) will be used to assess perceived pain for a multi-activity circuit test and isolated activity tests. The KOOS evaluates both short-term and long-term consequences of knee injury. It holds 42 items in 5 separately scored subscales; Pain, other Symptoms, Function in daily living (ADL), Function in Sport and Recreation (Sport/Rec), and knee-related Quality of Life (QOL). It is an extension of the WOMAC Osteoarthritis Index. A Likert scale is used and all items have five possible answer options scored from 0 (No Problems) to 4 (Extreme Problems) and each of the five scores is calculated as the sum of the items included. Scores are transformed to a 0-100 scale, with zero representing extreme knee problems and 100 representing no knee problems as common in orthopaedic assessment scales and generic measures.
Time frame: Baseline and through study completion, an average of 2 months
10-meter walk test
A 10-meter walk test on an overground walkway will assess self-selected walking speed across three conditions: no orthosis, conventional orthosis, and powered orthosis at their preferred assistance level.
Time frame: Baseline and through study completion, an average of 2 months
Joint range of motion
Joint range of motion for each task will be recorded using optical motion capture and averaged over cycle and repetitions. Motion capture data will be recorded during study visits and used to calculate joint angle, which will then be averaged over gait cycles and activity repetitions to get a range of motion value.
Time frame: Baseline and through study completion, an average of 2 months
Joint compressive forces
In Aim 3, joint compressive forces for each task will be modeled using OpenSim / inverse dynamics and
Time frame: Baseline and through study completion, an average of 2 months
Difficulty score
The Knee Injury and Osteoarthritis Outcome Score (KOOS) will be used to assess perceived difficulty for a multi-activity circuit test and isolated activity tests. The KOOS evaluates both short-term and long-term consequences of knee injury. It holds 42 items in 5 separately scored subscales; Pain, other Symptoms, Function in daily living (ADL), Function in Sport and Recreation (Sport/Rec), and knee-related Quality of Life (QOL). It is an extension of the WOMAC Osteoarthritis Index. A Likert scale is used and all items have five possible answer options scored from 0 (No Problems) to 4 (Extreme Problems) and each of the five scores is calculated as the sum of the items included. Scores are transformed to a 0-100 scale, with zero representing extreme knee problems and 100 representing no knee problems as common in orthopaedic assessment scales and generic measures.
Time frame: Baseline and through study completion, an average of 2 months
QUEST
We will administer the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) survey after each orthosis condition to assess comfort and satisfaction with the device. The QUEST 2.0 is a highly-validated, 12-item self-report questionnaire designed to measure how satisfied a person is with a wide range of assistive technology. It contains 12 items divided into 2 primary categories: device subscale and services subscale. Participants rate each item using a 5-point Likert scale: 1 = Not satisfied at all, 2 = Not very satisfied, 3 = More or less satisfied, 4 = Quite satisfied, 5 = Very satisfied. To calculate the final metrics, the scores of the items are summed and divided by the total number of valid responses. This yields three separate scores ranging from 1.0 to 5.0: a Device Score, a Services Score, and a Total QUEST Score. Higher scores indicate a better outcome (greater satisfaction with the technology).
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Time frame: Baseline and through study completion, an average of 2 months