Gait symmetry measures: A review of current and prospective methods

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

“…The strength of the autocorrelation procedure may stem from analysing continuous successive steps. Complex measures such as gait asymmetry are not simply portrayed within a single discreet gait cycle; this concept has been highlighted before, whereby continuous measures have been described to highlight different asymmetry causes, symptoms, and gait strategies such as particular compensatory techniques [17]. Data from our study indicate that participants with high asymmetry produced poor forward propulsion from the affected limb, instead of relying on the more dominant limb to achieve progression at the end of each stride.…”

“…However, this strategy will likely vary among a broader range of participants and throughout recovery. Other methods may better reflect true levels of asymmetry at different stages of recovery from acute, early subacute, late subacute, and chronic stroke, meaning that they should still be considered as potential variables [17,20].…”

“…Numerous asymmetry variables exist that have been obtained from cyclical acceleration signals during gait such as variables derived from the frequency domain [15,16]. These variables vary according to the complexity of the sensor, the number of sensors used, their location, and the population on which they were tested [17][18][19]. Relative to the discreet spatiotemporal movement of the feet equivalents, variables quantifying asymmetry from the cyclical signals of the lower back better classified post-stroke gait from controls [16,18,20].…”

“…Relative to the discreet spatiotemporal movement of the feet equivalents, variables quantifying asymmetry from the cyclical signals of the lower back better classified post-stroke gait from controls [16,18,20]. Their advantage stems from considering the acceleration as a complete waveform, not neglecting temporal information outside of the time domain, which may enable a more complete description of the signal and a better characterisation of gait post-stroke [17].Previously, studies quantifying asymmetry from acceleration signals of the trunk during post-stroke gait typically focus on differences from a control group, adopt a minimal data set of variables, and to our knowledge do not report the concurrent validity or reliability to reference standards. Knowledge of the most robust asymmetry variables that are capable of quantifying similar information to reference standards using clinically feasible tools is important to further the field.…”

“…Relative to the discreet spatiotemporal movement of the feet equivalents, variables quantifying asymmetry from the cyclical signals of the lower back better classified post-stroke gait from controls [16,18,20]. Their advantage stems from considering the acceleration as a complete waveform, not neglecting temporal information outside of the time domain, which may enable a more complete description of the signal and a better characterisation of gait post-stroke [17].…”