The relationship between the anteroposterior and mediolateral margins of stability in able-bodied human walking

Buurke, T J W; Otter, Rob den

doi:10.1016/j.gaitpost.2021.08.008

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…This limitation can be mitigated by fusing the outcome of DI with the position of the equivalent GRF application point, called Centre of Pressure (CoP): while DI can provide instantaneous rate of change of CoM evolution, CoP supplies a reference to prevent drift phenomenon [9,11,14]. Clearly, this strategy allows to enhance the estimation of CoM position only with regards to its horizontal coordinates, which correspond to its projection on the ground (CoMG).…”

Section: Introductionmentioning

confidence: 99%

“…Clearly, this strategy allows to enhance the estimation of CoM position only with regards to its horizontal coordinates, which correspond to its projection on the ground (CoMG). Such information, although incomplete, holds considerable interest in the biomechanical analysis field, in particular it allows the study of balance and dynamic stability during locomotion through the concepts of extrapolated centre of mass position and margin of stability [11,[14][15][16], with particular application to neurological disorders [10,17,18]. DI and CoP fusion is usually carried out by employing a Complementary Linear Filter (CLF): not considering any statistical description for the noise corrupting the signals, the filter is obtained by a simple analysis in the frequency domain [19].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Observer Design for Ambulatory Estimation of Body Centre of Mass Position

Maddalena

Saadat

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Complementary Linear Filter (CLF) is a common techinque employed for estimating the ground projection of body Centre of Mass starting from ground reaction forces. This method fuses centre of pressure position and double integration of horizontal forces, selecting best cut-off frequencies for low-pass and high-pass filters. Classical Kalman filter is a substantially equivalent approach, as both methods rely on an overall quantification of error/noise and don't analyze its origin and time-dependence. In order to overcome such limitations, a Time-Varying Kalman Filter (TVKF) is proposed in this paper: the effect of unknown variables is directly taken into account by employing a statistical description which is obtained from experimental data. To this end, in this paper we have employed a dataset of 8 walking healthy subjects: beside supplying gait cycles at different speeds, it deals with subjects in age of development and provides a wide range of body sizes, allowing therefore to assess the observers' behaviour under different conditions. The comparison carried out between CLF and TVKF appears to highlight several advantages of the latter method in terms of better average performance and smaller variability. Results presented in this paper suggest that a strategy which incorporates a statistical description of unknown variables and a time-varying structure can yield a more reliable observer. The demonstrated methodology sets a tool that can undergo a broader investigation to be carried out including more subjects and different walking styles.

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Section: Introductionmentioning

confidence: 99%