Gait Stability and Aging

Mochizuki, Luís; Aliberti, Sandra

doi:10.1007/978-3-319-48980-3_4

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…(2000) and Dingwell & Cusumano, (2000). This algorithm, based on Lyapunov exponents used in chaos theory (Dingwell, 2006; Mochizuki & Aliberti, 2017), has been recommended to assess gait stability and fall risk (Bruijn et al, 2013). LDS requires the construction of an attractor in the phase space by means of time delay embedding of continuous signals, such as body accelerations (Takens, 1981; Rosenstein, Collins & De Luca, 1993; Terrier & Dériaz, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Terrier

2019

PeerJ

View full text Add to dashboard Cite

Background During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA, while being computed from continuous signals without preliminary discretization. Its use would facilitate complexity analysis from a larger variety of gait measures, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods Thirty-six healthy adults walked 30 min on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUC). Results DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.86). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUC = 1.00 and 0.97, respectively). Conclusion Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals. However, future studies are needed to investigate the theoretical relationship between DFA and ACI.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Terrier

2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…The use of LDS to characterize gait stability and assess fall risk has gained popularity over recent years (Mochizuki & Aliberti, 2017;Bizovska et al, 2018;Mehdizadeh, 2018). Computing ACI in addition to LDS is straightforward and using the measures together could be fruitful, as information about gait automaticity and cautiousness would complement information about gait stability.…”

Section: Discussionmentioning

confidence: 99%

“…ACI is a new term for long-term local dynamic stability (LDS)-also referred to as divergence exponent or lambda ()-which was introduced by Dingwell et al in 2000 . This algorithm, based on Lyapunov exponents used in chaos theory (Dingwell, 2006;Mochizuki & Aliberti, 2017), has been recommended to assess gait stability and fall risk (Bruijn et al, 2013). LDS requires the construction of an attractor in the phase space by means of time delay embedding of continuous signals, such as body accelerations (Takens, 1981;Rosenstein, Collins & De Luca, 1993;Terrier & Dériaz, 2013).…”

Section: Introductionmentioning

confidence: 99%

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Terrier

2019

Preprint

View full text Add to dashboard Cite

Background. During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA. But in contrast to DFA, ACI can be applied to continuous signals, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods. Thirty-six healthy adults walked 30 minutes on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUROC). Results. DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.78). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUROC = 1.0 and 0.96, respectively). Conclusion. Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals.

show abstract

“…ACI is a new term for long-term local dynamic stability (LDS)-also referred to as divergence exponent or lambda ()-which was introduced by . This algorithm, based on Lyapunov exponents used in chaos theory (Dingwell, 2006;Mochizuki & Aliberti, 2017), has been recommended to assess gait stability and fall risk (Bruijn et al, 2013). LDS requires the construction of an attractor in the phase space by means of time delay embedding of continuous signals, such as body accelerations (Takens, 1981;Rosenstein, Collins & De Luca, 1993;Terrier & Dériaz, 2013).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues (v0.2)"

2019

View full text Add to dashboard Cite

Background. During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (antipersistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA, while being computed from continuous signals without preliminary discretization. Its use would facilitate complexity analysis from a larger variety of gait measures, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods. Thirty-six healthy adults walked 30 minutes on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUC). Results. DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson's r = 0.86). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUC = 1.00 and 0.97, respectively). Conclusion. Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the longrange complexity of continuous gait signals. However, future studies are needed to investigate the theoretical relationship between DFA and ACI.

show abstract

Gait Stability and Aging

Cited by 4 publications

References 22 publications

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Peer Review #2 of "Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues (v0.2)"

Contact Info

Product

Resources

About