ReviewAnkle and foot power in gait analysis: Implications for science, technology and clinical assessment
Introduction
Muscles and tendons about the ankle, knee and hip are typically considered the main mechanical power producers during human gait. Using inverse dynamics to estimate net power generated about these joints has become ubiquitous in human gait analysis studies (Robertson et al., 2013, Winter, 2009, Winter, 1991). Substantial effort has gone into characterizing how ankle, knee and hip kinetics are adapted during different locomotor tasks and varying task intensities (e.g., Farris and Sawicki, 2012a, Winter, 1984, Winter, 1983, Zelik and Kuo, 2010), and understanding how power about each of these three joints contributes functionally to movement biomechanics (e.g., Mann and Hagy, 1980, Inman et al., 1981, Perry, 1992, Levine et al., 2012, Zelik and Adamczyk, 2016). However, in gait analysis studies, far less attention has been given to power contributions from the foot.
Foot power, the rate of mechanical work performed collectively by active and passive structures of the foot (sometimes including the shoe), is not typically estimated in gait analysis studies (Zelik et al., 2015). The standard convention in the gait analysis field is to model the entire foot as a single rigid-body segment, which neither absorbs nor generates mechanical power. This convention is found throughout biomechanics textbooks (Baker, 2013, Inman et al., 1981, Rancho Los Amigos National Rehabilitation Center, 2001, Robertson et al., 2013, Whittle, 2014, Winter, 2009), and is reflected in commonly-used motion capture marker sets. However, there is compelling evidence that foot power contributes meaningfully to walking (Bruening et al., 2012a, MacWilliams et al., 2003, Siegel et al., 1996, Takahashi et al., 2012, Takahashi and Stanhope, 2013, Zelik et al., 2015) and running (Kelly et al., 2015, McDonald et al., 2016, Riddick and Kuo, 2016, Stearne et al., 2016, Stefanyshyn and Nigg, 1997), due to a complex biomechanical interplay between muscles and passive structures (Kelly et al., 2014, Ker et al., 1987, Venkadesan et al., 2017, Zelik et al., 2014).
Currently there remains a lack of clarity in the scientific literature regarding if, when and how foot power should be calculated in the study of gait biomechanics. A critical question looms: is modeling the entire foot as one rigid-body segment, which neither absorbs nor generates mechanical power, adequate for addressing the types of the scientific questions that are commonly investigated in gait analysis studies, or adequate for obtaining biomechanical estimates that properly inform the design, prescription and evaluation of clinical interventions (e.g., foot prostheses)? Here we present experimental evidence and analytical arguments suggesting that, in many cases, neglecting foot power is inadequate for scientific studies and may be inappropriate (misleading) for clinical gait analysis or informing technology development.
The purpose of this article is two-fold: (i) to use case study examples in conjunction with analytical arguments and prior literature to highlight why foot power should be estimated within the context of whole-body or lower-limb gait analysis studies, and then (ii) to discuss how to experimentally estimate (and interpret) foot and ankle power. This article is principally intended for individuals who employ conventional gait analysis methods (e.g., 3 degree-of-freedom (3DOF) rigid-body inverse dynamics) to understand bio- or neuro-mechanical aspects of human locomotion, to inform device design, or to evaluate clinical interventions. Some of the observations contained within this article may be banal or obvious to foot experts and enthusiasts. But if so, this is all the more reason to resolve the discontinuity between scientists, engineers and clinicians focused specifically on the foot, and those who use gait analysis methods such as inverse dynamics to more broadly investigate how constituents of the body (e.g., individual joints, segments, muscles or tendons) contribute to whole-body movement.
Section snippets
Methods
We performed two gait analysis case studies that exemplify how and why to compute foot power, and implications on ankle power. The first case study was on a healthy individual during treadmill walking at fixed speed. We used an extended marker set to compute and contrast various estimates of ankle power, foot power, and combined ankle plus foot (termed anklefoot) power. The second case study involved a person with unilateral transtibial amputation walking sequentially on eight different
Case study 1
Ankle power estimates were similar with both 3DOF and 6DOF methods in terms of peak power and positive work (Fig. 3). These findings are consistent with prior studies, each on 10 subjects (Buczek et al., 1994, Zelik et al., 2015). Anklefoot power estimates were also similar to each other (Fig. 3). This result is consistent with Takahashi et al. (2012), who previously demonstrated strong similarity between Distal Shank power and Ankle+Distal Foot power. However, Ankle power and positive work
Discussion
These case studies exemplify problems that can arise when the entire foot is treated as a single rigid-body segment. Below we discuss scientific, clinical and technological implications, which highlight why it is important to include foot power in gait analysis studies; either explicitly by computing it, or implicitly by taking the (non-rigid) anatomy of the foot into account when estimating power about the ankle. Based on these empirical examples, analytical arguments and corroborating
Conclusion
Treating the entire foot as a single rigid-body segment can result in obscuring (or even completely missing) important dynamics, re-affirming conclusions from prior multi-segment foot modeling studies. Here we overview why this is important to the gait analysis community, and how to better estimate anklefoot dynamics experimentally. Specifically, we highlight how neglecting foot power can hinder our scientific understanding of movement, confound our ability to make robust clinical comparisons
Conflict of interest
The authors have no conflict of interest to declare.
Acknowledgements
This work was supported by funding from the National Institutes of Health (K12HD073945) and from the National Science Foundation (CBET – 1605200). We gratefully acknowledge New Balance for donating footwear. We would like to thank Erik Lamers for his help with data collection and processing. And we would like to thank a host of colleagues – notably, Matthew Yandell, Kota Takahashi, Luke Kelly, Thomas Kepple, Maura Eveld and Harrison Bartlett – for their thought-provoking discussions and helpful
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