Patient Preference in the Selection of Prosthetic Joint Stiffness

Clites, Tyler R.; Shepherd, Max K.; Ingraham, Kimberly A.; Rouse, Elliott J.

doi:10.1109/biorob49111.2020.9224405

Cited by 6 publications

(13 citation statements)

References 22 publications

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“…The stark lack of correlation we observed between preferred stiffness and body mass reinforces results from our previous studies, showing that body mass is a poor predictor of preference [39,40]. Because user weight plays Fig.…”

Section: Discussionsupporting

confidence: 90%

“…While it is known that metabolic cost is not highly sensitive to prosthesis mechanics during level walking [41,[60][61][62][63][64][65][66], these previous studies have assessed metabolic cost either as a function of weight-normalized prosthetic joint stiffness, or of categorical stiffness [64]. Given that we have consistently shown that there is no clear linear relationship between weight and preferred stiffness, it is possible that weight-normalization may have obscured any underlying effects, by not adequately aligning the minima of individual subjects' energy landscapes [40]. As such, we posited that preference normalization might reveal an energetic minimum at each subject's preferred stiffness, as the vertex of an underlying quadratic relationship between preference-normalized stiffness and metabolic cost.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, preference is inherently specific to the population from which it is measured, which increases its applicability as an efficacy criterion across pathologies, and drives device development toward solutions that meet patient needs. In light of these benefits, preference has emerged in the research setting as a potential indicator of device efficacy [39][40][41], and has recently been used in combination with humanin-the-loop optimization to tune the behavior of complex wearable mechatronic systems [42,43].…”

Section: Introductionmentioning

confidence: 99%

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Understanding patient preference in prosthetic ankle stiffness

Clites

Shepherd

Ingraham

et al. 2021

J NeuroEngineering Rehabil

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Background User preference has the potential to facilitate the design, control, and prescription of prostheses, but we do not yet understand which physiological factors drive preference, or if preference is associated with clinical benefits. Methods Subjects with unilateral below-knee amputation walked on a custom variable-stiffness prosthetic ankle and manipulated a dial to determine their preferred prosthetic ankle stiffness at three walking speeds. We evaluated anthropomorphic, metabolic, biomechanical, and performance-based descriptors at stiffness levels surrounding each subject’s preferred stiffness. Results Subjects preferred lower stiffness values at their self-selected treadmill walking speed, and elected to walk faster overground with ankle stiffness at or above their preferred stiffness. Preferred stiffness maximized the kinematic symmetry between prosthetic and unaffected joints, but was not significantly correlated with body mass or metabolic rate. Conclusion These results imply that some physiological factors are weighted more heavily when determining preferred stiffness, and that preference may be associated with clinically relevant improvements in gait.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding patient preference in prosthetic ankle stiffness

Clites

Shepherd

Ingraham

et al. 2021

J NeuroEngineering Rehabil

Self Cite

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“…To use preference as an optimization criterion for lower limb exoskeletons, it must be robustly measured, and we need to understand how it changes as individuals adapt to the assistance. Recent studies have investigated users' preferences in assistance provided by lower limb prostheses (24)(25)(26)(27)(28)(29)(30) and exoskeletons (11,(31)(32)(33)(34). To measure preference, some studies used a forced choice paradigm, in which participants were presented with pairwise comparisons (A-B testing) (29,(31)(32)(33) or asked to compare a condition to their own internalized preference (24,25).…”

Section: Introductionmentioning

confidence: 99%

“…Such "self-tuning" methods are advantageous because they can quickly yield an individual's preference and are intuitive to the user-it is easy to imagine someone using a smart phone or watch to adjust their settings during activities of daily living. Previous studies have demonstrated success using selftuning to identify individual preferences in one dimension (26,27), yet it is an open question how to extend these methods to multiple dimensions. One option is to perform one-dimensional sweeps through each parameter sequentially (28), but this method is time consuming and does not allow participants to assess how potential interactions between parameters affect their preference.…”

Section: Introductionmentioning

confidence: 99%

The role of user preference in the customized control of robotic exoskeletons

2022

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User preference is a promising objective for the control of robotic exoskeletons because it may capture the multifactorial nature of exoskeleton use. However, to use it, we must first understand its characteristics in the context of exoskeleton control. Here, we systematically measured the control preferences of individuals wearing bilateral ankle exoskeletons during walking. We investigated users’ repeatability identifying their preferences and how preference changes with walking speed, device exposure, and between individuals with different technical backgrounds. Twelve naive and 12 knowledgeable nondisabled participants identified their preferred assistance in repeated trials by simultaneously self-tuning the magnitude and timing of peak torque. They were blinded to the control parameters and relied solely on their perception of the assistance to guide their tuning. We found that participants’ preferences ranged from 7.9 to 19.4 newton-meters and 54.1 to 59.2 percent of the gait cycle. Across trials, participants repeatably identified their preferences with a mean standard deviation of 1.7 newton-meters and 1.5 percent of the gait cycle. Within a trial, participants converged on their preference in 105 seconds. As the experiment progressed, naive users preferred higher torque magnitude. At faster walking speeds, these individuals were more precise at identifying the magnitude of their preferred assistance. Knowledgeable users preferred higher torque than naive users. These results highlight that although preference is a dynamic quantity, individuals can reliably identify their preferences. This work motivates strategies for the control of lower limb exoskeletons in which individuals customize assistance according to their unique preferences and provides meaningful insight into how users interact with exoskeletons.

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The development of rating scales to evaluate experiential prosthetic foot preference for people with lower limb amputation

Ruxin

Halsne

Hafner

et al. 2023

PM&R

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BackgroundSelection of a foot is an important aspect of prosthetic prescription and vital to maximizing mobility and functional goals after lower limb amputation. Development of a standardized approach to soliciting user experiential preferences is needed to improve evaluation and comparison of prosthetic feet.ObjectiveTo develop rating scales to assess prosthetic foot preference and to evaluate use of these scales in people with transtibial amputation after trialing different prosthetic feet.DesignParticipant‐blinded, repeated measures crossover trial.SettingVeterans Affairs and Department of Defense Medical Centers, laboratory setting.ParticipantsSeventy‐two male prosthesis users with unilateral transtibial amputation started, and 68 participants completed this study.InterventionsParticipants trialed three mobility‐level appropriate commercial prosthetic feet briefly in the laboratory.Main Outcome Measures“Activity‐specific” rating scales were developed to assess participants' ability with a given prosthetic foot to perform typical mobility activities (eg, walking at different speeds, on inclines, and stairs) and “global” scales to rate overall perceived energy required to walk, satisfaction, and willingness to regularly use the prosthetic foot. Foot preference was determined by comparing the rating scale scores, after laboratory testing.ResultsThe greatest within‐participant differences in scores among feet were observed in the “incline” activity, where 57% ± 6% of participants reported 2+ point differences. There was a significant association (p < .05) between all “activity‐specific” rating scores (except standing) and each “global” rating score.ConclusionsThe standardized rating scales developed in this study could be used to assess prosthetic foot preference in both the research and clinical settings to guide prosthetic foot prescription for people with lower limb amputation capable of a range of mobility levels.

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Patient Preference in the Selection of Prosthetic Joint Stiffness

Cited by 6 publications

References 22 publications

Understanding patient preference in prosthetic ankle stiffness

Understanding patient preference in prosthetic ankle stiffness

The role of user preference in the customized control of robotic exoskeletons

The development of rating scales to evaluate experiential prosthetic foot preference for people with lower limb amputation

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