Parametric studies enable control of prosthesis mechanical properties and generation of mappings between physics-based definitions of mechanical function and clinically-relevant user outcomes. The resolution and range of this mapping is dependent on the modular adjustments of the experimental devices, but exceeds that obtained by testing commercial devices. Specific examples of this mapping for in-vivo and in-silico studies include defining relationships between: ankle rotational stiffness and lower-limb joint range-of-motion (ROM) during fast level walking 47 ( ); ankle rotational stiffness and bilateral swing time symmetry across different mobility scenarios 48 ( ); overall foot stiffness on foot and muscle function 58 ( ); and overall foot damping on intact knee abduction moments during level walking at different speeds 69 . These results help populate the vast map relating prosthesis properties and user outcomes for different scenarios (e.g., level ground and slope walking at different speeds), and inform on how fine adjustments in prosthesis properties can maximize rehabilitation outcomes. However, these data also emphasize that much of the experimental mapping remains incomplete, and concerted research efforts are required to fill the gaps in knowledge.
To liberate from traditional study constraints and further define relationships between user outcomes and device function, investigators began to pursue experimental designs incorporating systematic adjustments in mechanical properties. These parametric studies can be separated into: human subject in-vivo testing that employ experimental prostheses 34 , 47 – 57 ( ), and numerical in-silico simulations incorporating physics-based prosthesis models 58 – 64 ( ). To facilitate in-vivo testing, standardized methods for reliably characterizing the mechanical properties of commercial and experimental prostheses, including combinations of stiffness, damping, and roll-over geometry, have been developed 41 , 65 – 67 . There are benefits and limitations to both types of studies. For example, numerical simulations allow for rapid and extensive systematic exploration as they are not directly reliant on human subjects, but require validation of sometimes complex musculoskeletal models to lend confidence in the results 58 , 59 . Conversely, although human subject testing accounts for biological variability, they are practically limited in terms of possible test conditions, sample size and hence, statistical power and external validity 47 , 48 . Consequently, both forms of studies are needed to inform understanding of how device properties influence user outcomes 41 .
The traditional model of human subject comparative studies involves testing multiple prostheses, often defined by commercial trade names and/or type such as conventional non-articulated, dynamic, ESAR, and articulated ( ) 17 , 20 , 40 . This approach is limited as outcomes are only attributable to arbitrary device classifications, which historically resulted from subjective evaluations on topology, modular components, and material composition. Without knowledge of the device’s user-independent mechanical properties, it is impossible to establish relationships between objective device characteristics and user outcomes 41 . Although there are notable exceptions when commercial prostheses characterization is included to correlate mechanical function with user outcomes 42 – 44 , these studies are constrained within the range of prosthesis properties available through commercial devices. Recently, standardized bench tests were proposed to classify prosthetic feet based on mechanical properties such as stiffness and energy loss 38 , 45 , which could enhance objectivity of device classification.
Passive below-knee prostheses have become more advanced and numerous over the past several decades partly resulting from developments in materials science to enhance their mechanical function 18 , 20 , 35 . For example, carbon fiber reinforced polymers now incorporated in many energy storage-and-return (ESAR) and dynamic prosthetic feet (e.g., Flex-Foot series, Össur), can demonstrate decreased net energy loss relative to other feet constructed of conventional composites and polymers (e.g., Solid Ankle Cushion Heel (SACH) series, Otto Bock, or Seattle Lightfoot, Seattle Systems, USA) 35 – 39 . The hypothesis was that modifications in mechanical function would be reflected in user performance 18 , 20 , such as reduced metabolic cost of gait 35 , 36 . Addressing such hypotheses generates evidence to inform the clinical goal of selecting the prosthesis design(s) that yields the best patient outcomes, and this is accomplished through exploring effects of prosthesis characteristics on user outcomes.
The utility of human-prosthesis interaction mapping is that generalized models can be created to predict user performance outcomes from prosthesis properties. When considering multiple objectives, a system of models would include clinically-relevant performance outcomes, such as minimizing metabolic cost or kinematic deviations, or maximizing residual limb health/comfort or locomotor stability, as the dependent (outcome) variables and prosthesis mechanical parameters as the independent variables. This modelling process represents a form of optimization where the prosthesis is designed to yield the best outcomes for a given patient state. We recognize the possibility of relationships between prosthesis properties and user outcomes that are more complex than a U-shape with a single min/maximum, thereby permitting the possibility of multiple designs with equally-valid function.
The definitive below-knee prosthesis is composed of modular components, including the foot-ankle mechanism, pylon, suspension system (often including a residuum socket and liner), and footwear 4, each contributing to overall prosthesis mechanical function 41. For example, footwear may substantially affect regional and overall stiffness and damping of prosthetic feet, in some instances normalizing the mechanical function across designs 35, 37, 39, 70. Additionally, the choice of suspension system and constituent components71 will play a role in determining the residuum-prosthesis interface properties and behavior 72, 73, and this is especially relevant with the advent of bone-anchored prostheses (osseointegration) 74. Therefore, a first application of the optimization process relates to evidence-based practice. Human-prosthesis maps inform clinicians to make educated selections of commercial components with which to build the definitive prosthesis while accounting for patient-specific factors. The second application of the optimization process is the identification of a set of prosthesis mechanical parameters that produces a desired performance outcome as a means to drive research and development (R&D) ( ) 41. Following identification of the optimized prosthesis properties, exploration of different topologies, modular component designs, and material composition can be conducted through simulation (e.g., finite element analysis and/or forward dynamics simulation) and physical prototyping to yield tuned components for maximizing the desired performance outcome(s).
Applying to both clinical prescription and R&D, the decision to select one or more objectives is a matter of prioritizing performance outcomes, and these outcomes may interact positively or negatively. For example, optimizing for metabolic efficiency may also affect locomotor stability as physical fatigue has associations with fall risk 75–77. When selecting to optimize for multiple objectives, prioritizing through a weighting scheme may be appropriate. Simulations provide a powerful and expedient platform of optimization for multiple objectives concurrently, such as identifying prosthesis stiffness distributions that minimize metabolic cost and intact knee joint loading 59 ( ) to identify trade-offs in performance.
When considering clinical applicability, prosthesis fitting plays a complementary role to design optimization from R&D contributions and is an important step after device selection. The definitive prosthesis delivered to the patient incorporates tuning from both processes as prosthetists select, assemble, fit, and align the device. Alignment relies on subjective evaluations from clinician observation and patient feedback to satisfy relevant clinical goals 4, 78, broadly defined as “maximum comfort, efficient function, and cosmesis” 4. Clinical objectives then reasonably include minimization of leg length discrepancy and limb discomfort, smooth and controlled stance-phase shank progression, bilateral symmetry of temporal-spatial parameters, and prosthetic structural stability 4, 78, 79. As evidence suggests that lower-limb prosthesis users can tolerate a range of alignments 78, the R&D optimization process may also need to consider designing for a range of alignments.
We would be remiss to not emphasize that prosthesis optimization may be less than straightforward due to some key features of generalized models. In-vivo parametric study results are derived from using experimental prostheses and isolated adjustments in specific properties, such as dorsiflexion rotational stiffness while holding plantarflexion stiffness constant 47 or overall foot damping while holding stiffness constant 69. Furthermore, experimental mappings are generated for isolated mobility scenarios, most commonly walking at a self-selected speed over level ground 34, 47–52, 58, 59, 69. These experimental designs are needed for practical, easy-to-interpret, and valid experimentation. However, there may be important interaction effects between mechanical parameters (e.g., stiffness and damping), regional structures (e.g., heel and keel), and mobility scenarios (e.g., level and slope walking) that can be included in the inferential statistical approach 48, 69. For example, the effects of keel stiffness on metabolic cost may be different for different values of heel stiffness, or as illustrated in , the effects of ankle rotational stiffness on gait symmetry may be different for different mobility scenarios (although not significant in the referenced study 48). Not accounting for interactions means the optimization process operates on an incomplete model and may result in suboptimal outcomes in the broader context of daily ambulation. Of course, consideration of multiple independent variables increases analytical and interpretation complexity.
Importantly, optimizing a set of passive properties for unlimited community ambulation involving different mobility scenarios of walking speed and terrain characteristics 80 is challenging. Prior parametric studies are useful when designing for a specific mobility scenario, but each scenario may require different prosthesis properties to achieve the same level of performance. This concept has been supported through studies that have characterized the quasi-static stiffness and roll-over geometry of the physiological foot-ankle complex across various mobility scenarios to inform biomimetic prosthetic foot designs 81–87. Similar to optimizing for multiple performance-based cost objectives, the process should consider tuning for multiple mobility scenarios which may require reconciliation of parameters to achieve the best balance of real-world outcomes. Alternatively, the design space is open to passive devices that can modify properties to adapt to a given scenario, such as different walking gradients 88, 89.
Despite their challenges, current parametric study designs account for some of the most important variables defining the human-prosthesis interaction, but a limitation of generalized models is that they are based on ‘average user’ performance. Consequently, they neglect patient-specific motor performance which impacts the user’s ability to exploit the prosthetic intervention as they self-integrate with the device. This limitation is partially an artifact of common experimental designs that rely on data from convenience samples of willing and able participatants90. These data may then be derived from a sample of relatively homogenous motor performance, thereby not accounting for a range of motor control strategies. Although a prosthesis user theoretically has the luxury of selecting from multiple solutions to achieve ambulation due to motor abundancy/redundancy, the range of solutions are likely limited by their neuromotor capacity as affected by neural and musculoskeletal structural properties (e.g., levels of joint ROM, muscle strength, reaction time, and sensory feedback) 31, 91–95. The optimal prosthesis design to maximize a given performance outcome may reasonably be different for patients of dissimilar motor performance, such as young individuals of traumatic etiology versus older individuals of dysvascular etiology 22, 23.
Although generalized models can account for the majority of prosthesis users via targeted and diverse subject recruitment strategies, the ultimate goal is to provide personalized interventions through inclusion of patient-specific variables. We propose that aspects of motor performance can and likely should be considered in the optimization process to account for neuromuscular constraints underlying movement. In a sense, this is already included in clinics in the USA and other countries that classify patient mobility level using the Medicare Functional Classification Level (MFCL) system 30, where component designs are reserved for each level as rationalized through medical necessity. However, we emphasize that considerations on motor performance should be included in R&D and clinical optimization processes in a standard way to accurately and reliably define the intended user. A logical next step is to incorporate the motor performance variable in parametric studies to expand the experimental mapping landscape. As this discussion is focused on optimization for a given outcome, we are operating on the position that users are ‘self-organizing’ to converge on a selection of motor strategies that stabilize performance for a given ambulatory task in ways that leverage their own neuromotor capacity 93, 95.
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