Translating walking parameters into meaningful biomarkers for benchmarking pathological movement behaviour

Walking is regulated and coordinated through complex control mechanisms within the human sensory motor system, allowing individuals to adapt to both internal and external challenges and perturbations (Full et al., 2002). During this regulation to achieve stable walking, natural fluctuations (i.e. movement variability) are present between strides in both the temporal (e.g. stride interval) and spatial (e.g. step width) walking characteristics. Movement variability resulting from a repetitive (continuous) task, such as walking, comprises of context-rich information on an individual’s intrinsic functional status, but also on how the individual might interact with external perturbations and adapt their motor performance (Stergiou et al., 2011). As such, the quality of their movement output (signature) can therefore be characterised through the assessment of movement variability.

Recent evidence shows that elderly adults produce repetitive movements such as walking with high levels of variability (Callisaya et al., 2010), possibly due to the loss in strength and deficits in neuromuscular control. Interestingly, movement variability is even greater in individuals that are at a high risk of falling, or suffer from movement disorders such as Parkinson’s disease, Multiple Sclerosis, or Huntington’s disease (Hausdorff et al., 1998, Hausdorff et al., 2001). This suggests that movement variability (in combination with other parameters to evaluate cognitive impairment) might hold the key for early identification of these neurological disorders. Despite this evidence, there has been limited uptake of movement variability as a biomarker in clinical settings, primarily because of a lack of clear definitions for healthy vs. pathological variability during walking.

Human walking patterns are inherently repetitive, but also variable!

To better quantify the optimal thresholds to effectively discriminate pathological from healthy asymptomatic variability in walking patterns, we performed a systematic review and meta-analysis. We believe that by providing optimum thresholds, our investigation will allow rapid and standardised monitoring of movement disorders in clinical settings.


Our meta-analysis included 85 studies comprising 2409 patients and 2523 healthy asymptomatic controls. The results showed an overall medium to large effect of pathology on stride time variability – the most commonly reported walking parameter in the literature. More importantly, our study identified an optimal threshold of 2.34% [1.92%, 2.76%] for stride time variability (a promising signature for assessing movement rhythmicity), discriminating pathological from healthy asymptomatic performance with an overall accuracy of 75%.

The study also provided evidence on optimal boundaries for variability of six other commonly identified walking spatio-temporal parameters: stride length, step length, swing time, step time, step width, dual limb support time.


A comprehensive knowledge of movement variability with clear definitions for healthy vs. pathological movement performance allows us to associate (in a scalable and unbiased manner) an individual’s quality of movement with their underlying functional neural status. Consequently, we also envisage prioritising and formalising potentially useful movement-based biomarkers to characterise complex distinctive walking behaviours in both healthy asymptomatic and pathological individuals.


Ravi DK, Gwerder M, Ignasiak NK, Baumann CR, Uhl M, van Dieën JH, Taylor WR, Singh NB. Revealing the optimal thresholds for movement performance: A systematic review and meta-analysis to benchmark pathological walking behavior. Neurosci Biobehav Rev, 2019. DOI:



Callisaya ML, Blizzard L, Schmidt MD, McGinley JL, Srikanth VK. Ageing and gait variability—a population-based study of older people. Age and Ageing, 39(2):191-197, 2010.

Full RJ, Kubow T, Schmitt J, Holmes P, and Koditschek D. Quantifying dynamic stability and maneuverability in legged locomotion. Integr Comp Biol, 42:149-57, 2002.

Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov Disord, 13(3):428-37, 1998.

Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil, 82(8):1050-6, 2001.

Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci, 30(5):869-88, 2011.


Deepak Ravi is a PhD student at the Laboratory of Movement Biomechanics ( at ETH Zurich Switzerland. His research group is interested in characterising motor-related adaptation due to aging and neuro-motor pathologies, but also with external perturbations.


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