Automatic spline smoothing of non-stationary kinematic signals using bilayered partitioning and blending with correlation analysis

Shin, Suchul; Han, Soonhung

doi:10.1016/j.dsp.2014.12.014

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…Using a low-frequency cutoff creates a smooth signal that can be used to cluster analyze the curve shape even though it does introduce an under-estimation of the force magnitudes. 20,33 To classify KAM waveforms the initial 15% of the time series of the stance phase was transformed into the sign of lagged differences since we have previously demonstrated that early peaks can be identified within this percentage of stance. 23 The similarity between observations was calculated as the Euclidean distance and then clustered into discrete shapes followed by clustering based on magnitude using the Ward-D2 34,35 method, as previously described.…”

Section: Discussionmentioning

confidence: 99%

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Kinematics observed during ACL injury are associated with large early peak knee abduction moments during a change of direction task in healthy adolescents

Sigurðsson

Karlsson

Snyder‐Mackler

et al. 2020

Journal Orthopaedic Research

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Cluster analysis of knee abduction moment waveforms may be useful to examine biomechanical data. The aim of this study was to analyze if the knee abduction moment waveform of early peaks, consistent with anterior cruciate ligament injury mechanisms, was associated with foot-trunk distance, knee kinematics, and heel strike landing posture, all of which have been observed during anterior cruciate ligament injuries. One hundred and seventy-seven adolescent athletes performed cutting maneuvers, marker-based motion capture collected kinetic and marker data and an 8-segment musculoskeletal model was constructed. Knee abduction moment waveforms were clustered as either a large early peak, or not a large early peak using a two-step process with Euclidean distances and the Ward-d2 cluster method. Mediolateral distance between foot and trunk was associated with the large early peak waveform with an odds ratio (95% confidence interval) of 3.4 (2.7-4.4). Knee flexion angle at initial contact and knee flexion excursion had odds ratios of 1.9 (1.6-2.4) and 1.6 (1.3-2.0). Knee abduction excursions had an odds ratio of 1.8 (1.1-2.4) and 1.8 (1.4-2.4), respectively. Heel strike landings and anteroposterior distance between foot and trunk were not associated with the large early peak waveform with odds ratios of 1.2 (0.9-1.7) and 1.1 (0.8-1.3), respectively. The knee abduction moment waveform is associated with several kinematic variables observed during ACL injury. The results support intervention programs that can modify these kinematics and thus reduce early stance phase knee abduction moments.

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Section: Discussionmentioning

confidence: 99%

“…Using a low-frequency cut-off creates a smooth signal that can be used to cluster analyze the curve shape even though it does introduce an under-estimation of the force magnitudes. 20 , 33 …”

Section: Methodsmentioning

confidence: 99%

Kinematics observed during ACL injury are associated with large early peak knee abduction moments during a change of direction task in healthy adolescents

Sigurðsson

Karlsson

Snyder‐Mackler

et al. 2020

Journal Orthopaedic Research

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“…Several recently proposed methods have aimed to address the processing of nonstationary kinematic signals with varying levels of accuracy, automation, and sensitivity. [1][2][3] Determining an approach suitable for processing multiple biomechanical signals can add an additional difficulty when those signals are nonstationary. In gait analysis, for example, it is fairly common to filter all noisy displacement signals at a given cutoff frequency and, perhaps, kinetic signals at another or even filter both at the same frequency to avoid artifacts in computed resultant joint moments.…”

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Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data

Davis

Challis

2021

Journal of Applied Biomechanics

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Time-differentiating kinematic signals from optical motion capture amplifies the inherent noise content of those signals. Commonly, biomechanists address this problem by applying a Butterworth filter with the same cutoff frequency to all noisy displacement signals prior to differentiation. Nonstationary signals, those with time-varying frequency content, are widespread in biomechanics (eg, those containing an impact) and may necessitate a different filtering approach. A recently introduced signal filtering approach wherein signals are divided into sections based on their energy content and then Butterworth filtered with section-specific cutoff frequencies improved second derivative estimates in a nonstationary kinematic signal. Utilizing this signal-section filtering approach for estimating running vertical ground reaction forces saw more of the signal’s high-frequency content surrounding heel strike maintained without allowing inappropriate amounts of noise contamination in the remainder of the signal. Thus, this signal-section filtering approach resulted in superior estimates of vertical ground reaction forces compared with approaches that either used the same filter cutoff frequency across the entirety of each signal or across the entirety of all signals. Filtering kinematic signals using this signal-section filtering approach is useful in processing data from tasks containing an impact when accurate signal second derivative estimation is of interest.

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Automatic spline smoothing of non-stationary kinematic signals using bilayered partitioning and blending with correlation analysis

Cited by 2 publications

References 23 publications

Kinematics observed during ACL injury are associated with large early peak knee abduction moments during a change of direction task in healthy adolescents

Kinematics observed during ACL injury are associated with large early peak knee abduction moments during a change of direction task in healthy adolescents

Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data

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