Ankle-Foot Orthosis Stiffness: Biomechanical Effects, Measurement and Emulation

Ankle-foot orthoses (AFOs) are braces worn by individuals with gait impairments to provide support about the ankle. AFOs come in a variety of designs for clinicians to choose from. However, as the effects of different design parameters on AFO properties and AFO users have not been adequately quantified, it is not clear which design choices are most likely to improve patient outcomes. Recent advances in manufacturing have further expanded the design space, adding urgency and complexity to the challenge of selecting optimal designs. A key AFO property affected by design decisions is sagittal-plane rotational stiffness. To evaluate the effectiveness of different AFO designs, we need: 1) a better understanding of the biomechanical effects of AFO stiffness and 2) more precise and repeatable stiffness measurement methods. This dissertation addresses these needs by accomplishing four aims. First, we conducted a systematic literature review on the influence of AFO stiffness on gait biomechanics. We found that ankle and knee kinematics are affected by increasing stiffness, with minimal effects on hip kinematics and kinetics. However, the lack of effective stiffness measurement techniques made it difficult to determine which specific values or ranges of stiffness influence biomechanics. Therefore, in Aim2, we developed an AFO stiffness measurement apparatus (SMApp). The SMApp is an automated device that non-destructively flexes an AFO to acquire operator- and trial-independent measurements of its torque-angle dynamics. The SMApp was designed to test a variety of AFO types and sizes across a wide range of flexion angles and speeds exceeding current alternatives. Common models of AFO torque-angle dynamics in literature have simplified the relationship to a linear fit whose slope represents stiffness. This linear approximation ignores damping parameters. However, as previous studies were unable to precisely control AFO flexion speed, the presence of speed effects has not been adequately investigated. Thus, in Aim3, we used the SMApp to test whether AFOs exhibit viscoelastic behaviors over the range of speeds typically achieved during walking. This study revealed small but statistically significant effects of flexion speed on AFO stiffness for samples of both traditional AFOs and novel 3-D printed AFOs, suggesting that more complex models that include damping parameters could be more suitable for modeling AFO dynamics. Finally, in Aim 4, we investigated the use of an active exoskeleton, that can haptically-emulate different AFOs, as a potential test bed for studying the effects of AFO parameters on human movement. Prior work has used emulation for rapid prototyping of candidate assistive devices. While emulators can mimic a physical device's torque-angle profile, the physical and emulated devices may have other differences that influence user biomechanics. Current studies have not investigated these differences, which limits translation of findings from emulated to physical devices. To evaluate the efficacy of AFO emulation as a research tool, we conducted a single-subject pilot study with a custom-built AFO emulator device. We compared user kinematics while walking with a physical AFO against those with an emulated AFO and found they elicited similar ankle trajectories. This dissertation resulted in the successful development and evaluation of a framework consisting of two test beds, one to assess AFO mechanical properties and another to assess the effects of these properties on the AFO user. These tools enable innovations in AFO design that can translate to measurable improvements in patient outcomes.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163219/1/deema_1.pd

Comparison of five different methodologies for evaluating ankle–foot orthosis stiffness

Abstract Background The mechanical properties of an ankle–foot orthosis (AFO) play an important role in the gait mechanics of the end user. However, testing methodologies for evaluating these mechanical properties are not standardized. The purpose of this study was to compare five different evaluation frameworks to assess AFO stiffness. Method The same 13 carbon composite AFOs were tested with five different methods. Four previously reported custom test fixtures (the BRUCE, KST, SMApp, and EMPIRE) rotated an AFO into dorsiflexion about a defined axis in the sagittal plane. The fifth method involved quasi-static deflection of AFOs into dorsiflexion by hanging weights (HW) from the footplate. AFO rotational stiffness was calculated as the linear fit of the AFO resistive torque and angular deflection. Differences between methods were assessed using descriptive statistics and a repeated measures Friedman with post-hoc Bonferroni–Holm adjusted Wilcoxon signed-rank tests. Results There were significant differences in measured AFO stiffnesses between test methods. Specifically, the BRUCE and HW methods measured lower stiffness than both the EMPIRE and the KST. Stiffnesses measured by the SMApp were not significantly different than any test method. Stiffnesses were lowest in the HW method, where motion was not constrained to a single plane. The median difference in absolute AFO stiffness across methods was 1.03 Nm/deg with a range of [0.40 to 2.35] Nm/deg. The median relative percent difference, measured as the range of measured stiffness from the five methods over the average measured stiffness was 62% [range 13% to 156%]. When the HW method was excluded, the four previously reported test fixtures produced a median difference in absolute AFO stiffness of 0.52 [range 0.38 to 2.17] Nm/deg with a relative percent difference between the methods of 27% [range 13% to 89%]. Conclusions This study demonstrates the importance of developing mechanical testing standards, similar to those that exist for lower limb prosthetics. Lacking standardization, differences in methodology can result in large differences in measured stiffness, particularly for different constraints on motion. Non-uniform measurement practices may limit the clinical utility of AFO stiffness as a metric in AFO prescription and future research

Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks

<div><p>Objective</p><p>Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.</p><p>Methods</p><p>Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.</p><p>Results</p><p>Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69–92%.</p><p>Conclusion</p><p>These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.</p><p>Significance</p><p>Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.</p></div

Average muscle activity for different loading conditions.

<p>The average muscle activity of the two left EMG channels for all nine subjects normalized to each subject’s maximum voluntary contraction (MVC) baseline. Time zero is the time of load-onset. The shaded regions show the region of ±1 standard deviation around the average. There is a clear spike in average activity around the load-onset time point that is more prominent with increasing lifted load values. The average of the right EMG channels showed similar activity patterns.</p

Average classification recall for each weight class.

<p>The average testing recall for each weight class at each time window for all nine test subjects. Time zero indicates the time of load-onset.</p

The experimental setup.

<p>(a) Schematic showing a subject lifting a weight from a table by following a posture sequence shown on the screen. The table height was adjusted such that trunk flexion was near 30° for each subject. (b) The sequence of posture images shown to the subject on the screen accompanied by a timed sound cue. At the start of each lift, the screen displayed a prompt informing the subject which weight to lift—no-weight, 10-lbs, or 24-lbs, then it displayed the posture sequence with a 1 second delay between each of the numbered (1)-(8) posture images. (c) Low-back muscle activity was measured from four surface EMG bipolar electrodes placed at L4/L5 vertebrae. (d) A subject performing a 24-lbs lift with a close up of the weights on the table. The no-weight case consisted of two sticks wrapped in foil and positioned to close a circuit between two charged foil railings. The force plate under the table can also be seen, flush with the floor, in the larger image.</p

Data analysis steps.

<p>Flowcharts showing the steps involved in (a) data processing, segmentation and feature extraction, and (b) dimensionality reduction, feature selection, cross-validation and, finally, testing.</p

Classification recall was affected by subject and weight class.

<p>Classification recall for each subject during the optimal time windows before (Pre) and after (Post) load-onset. The interaction between subject and weight class had statistically significant affects on recall percentage.</p

Selected features and their selection frequency.

<p>The percentage of times (out of 711) that each feature from each channel was selected for the optimal feature set as input by the greedy feedforward algorithm during cross-validation of the MLR classifier. The mean feature was selected much more frequently than the others.</p

A summary of relevant response times of muscles, classifiers, controllers and actuators commonly used in assistive device applications.

<p>A summary of relevant response times of muscles, classifiers, controllers and actuators commonly used in assistive device applications.</p