Handling of impact forces in inverse dynamics

Bisseling, Rob W; Hof, At L.

doi:10.1016/j.jbiomech.2005.07.021

Cited by 280 publications

(183 citation statements)

References 14 publications

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“…It has been suggested that during activities with high impact forces, filtering the kinetic and kinematic input data using different cut-off frequencies could lead to the computation of artificial peaks in the determined RJMs [11][12][13] . It is thus possible that previously observed excessive fluctuations in the knee RJM during early stance in sprinting are influenced by the digital filtering procedures.…”

Section: Introductionmentioning

confidence: 99%

Excessive fluctuations in knee joint moments during early stance in sprinting are caused by digital filtering procedures

2013

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Abstract:Inverse dynamics analyses are commonly used to understand movement patterns in all forms of gait. The aim of this study was to determine the effect of digital filtering procedures on the knee joint moments calculated during sprinting as an example of the possible influence of data analysis processes on interpretation of movement patterns. Data were obtained from three highly trained sprinters who completed a series of 30 m sprints.Ten different combinations of cut-off frequency were applied to the two-dimensional kinematic and kinetic input data with the kinetic cut-off frequency set equal to or higher than the kinematic cut-off frequency. When using the commonly adopted practice of filtering the kinetic data with a higher cut-off frequency than the kinematic data, exaggerated fluctuations in the knee joint moment existed soon after contact. In extreme cases, the knee moved between flexor, extensor and flexor dominance in less than 33 ms and through ranges exceeding 500 Nm. During an inverse dynamics analysis of locomotion, mismatched cut-off frequencies will likely affect the calculated joint moments if the cut-off frequency applied to the kinematic data is less than the true frequency content, particularly during impact phases. In the example of sprinting, exaggerated fluctuations in the knee joint moment appear to be data processing artefact rather than genuine characteristics of the joint kinetics. When the cut-off frequencies, and thus the frequency content of all input data, are matched, the fluctuations after contact are minimal, and such a procedure is suggested for inverse dynamics analyses of gait.

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Section: Introductionmentioning

confidence: 99%

Excessive fluctuations in knee joint moments during early stance in sprinting are caused by digital filtering procedures

2013

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“…The kinematic and ground reaction force data were filtered using a fourth-order, zero lag, recursive Butterworth filter with a cutoff frequency of 20 Hz. 35 Segment coordinate systems followed the right-hand convention and were anatomically based. The x-axis, y-axis, and z-axis pointed in the medial-lateral, anterior-posterior, and vertical direction, respectively.…”

Section: Data Reductionmentioning

confidence: 99%

Acute Responses of Strength and Running Mechanics to Increasing and Decreasing Pain in Patients With Patellofemoral Pain

Bazett-Jones

Huddleston

Cobb

et al. 2017

Journal of Athletic Training

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; †University of Wisconsin-MilwaukeeContext: Patellofemoral pain (PFP) is typically exacerbated by repetitive activities that load the patellofemoral joint, such as running. Understanding the mediating effects of changes in pain in individuals with PFP might inform injury progression, rehabilitation, or both.Objective: To investigate the effects of changing pain on muscular strength and running biomechanics in those with PFP.Design: Crossover study. Setting: University research laboratory.Patients or Other Participants: Seventeen participants (10 men, 7 women) with PFP.Intervention(s): Each participant completed knee painreducing and pain-inducing protocols in random order. The pain-reducing protocol consisted of 15 minutes of transcutaneous electric nerve stimulation (TENS) around the patella. The pain-inducing protocol was sets of 20 repeated single-legged squats (RSLS). Participants completed RSLS sets until either their pain was within at least 1 cm of their pain during an exhaustive run or they reached 10 sets.Main Outcome Measure(s): Pain, isometric hip and trunk strength, and running mechanics were assessed before and after the protocols. Dependent variables were pain, normalized strength (abduction, extension, external rotation, lateral trunk flexion), and peak lower extremity kinematics and kinetics in all planes. Pain scores were analyzed using a Friedman test. Strength and mechanical variables were analyzed using repeated-measures analyses of variance. The a level was set at P , .05.Results: Pain was decreased after the TENS (pretest: 3.10 6 1.95, posttest: 1.89 6 2.33) and increased after the RSLS (baseline: 3.10 6 1.95, posttest: 4.38 6 2.40) protocols (each P , .05). The RSLS protocol resulted in a decrease in hipextension strength (baseline: 0.355 6 0.08 kg/kg, posttest: 0.309 6 0.09 kg/kg; P , .001). Peak plantar-flexion angle was decreased after RSLS (baseline: À13.978 6 6.418, posttest: À12.848 6 6.458; P ¼ .003). Peak hip-extension (pretest: À2.31 6 0.46) and hip-abduction (pretest: À2.02 6 0.35) moments decreased after both the TENS (extension: À2.15 6 0.48 Nm/ kg, P ¼ .015; abduction: À1.91 6 0.33 Nm/kg, P ¼ .015) and RSLS (extension: À2.18 6 0.52 Nm/kg, P ¼ .003; abduction: À1.87 6 0.36 Nm/kg, P ¼ .039) protocols.Conclusions: This study presents a novel and effective method of increasing pain in persons with PFP. Functionally increased pain after RSLS coincides with reduced hip-extensor muscle strength and decreased plantar-flexion angle during running. The TENS treatment decreased pain during running in those with PFP but failed to influence strength. Hip moments were reduced by both protocols, which may demonstrate that acute increases or decreases in pain cause runners to change their mechanics.

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“…Solid state accelerometers have had some use in biomechanics experiments for estimating peak forces in jumping [3], analyzing gait patterns [4] and walking stability in the aged [5]. MEMS sensors are more flexible and are suitable in arrays for more complex motion capture [6] providing accurate orientation and accelerometer data.…”

Section: Human Motion Capture Datamentioning

confidence: 99%