Innovative research methods in biomechanics

The development and dissemination of analysis tools ranging from basic data processing for undergraduate students to advanced vector field analysis based on statistical parametric mapping techniques.

Advanced statistics

Vector continuum schematic, depicting a mean two-muscle EMG waveform in blue along with inter-muscle dependence (EMG1-EMG2 covariance) and time-dependence (TIME-EMG smoothness). 

Biomechanical data range from discrete 0D scalars to complex 3D vectors (and beyond). Traditional analyses of biomechanical data typically reduces the complexity of the dataset by extracting “key scalars” to analyse with standard statistical analysis techniques. The problem with this approach is that the selection of the “key scalars” is inherently biased unless a hypothesis directly pertained to that key scalar at that instance in time.

Our research involves the analysis of n-D biomechanical data using statistical parametric mapping. We use traditional general linear models, for example t-test, ANOVA, regression to analyse biomechanical data but with the maintenance of time within the analysis fundamentally differentiating it from traditional analyses.

Primary contact

  • Dr Mark Robinson

Artificial neural networks

A set of unique methods to quantify deviation of movement from normality to support clinical decision-making using artificial intelligence. Software tools developed in our project are available as free downloads.

Characteristics of abnormal gait can be captured by quantifying the three dimensional joint angles, moments and powers of the lower limbs but the complexity of the resultant high dimensional data space makes data interpretation prone to bias. Self-organising neural networks can be used to map complex gait data onto a two dimensional topographic map, thereby improving the efficiency of decision-making in clinical gait analysis.

We have developed the Movement Deviation Profile which shows the deviation of an individual’s movement from normality in a single curve and a single number.

Our staff

  • Prof Gabor Barton
  • Dr Malcolm Hawken
  • Dr Mark Robinson
  • Jacob Beesley

Publication spotlights

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Vector field statistics for objective center-of-pressure trajectory analysis, with evidence of scalar sensitivity to small coordinate system rotations

Vector field statistical analysis of kinematic and force trajectories. 

Parametric and non-parametric confidence intervals and hypothesis tests regarding scalar trajectories

Gait kinematics of subjects with chronic ankle instability using a multi-segmented foot model

The effect of running speed on knee mechanical loading in females during side cutting

Pain, fear, and movement: Cognitive-behavioural connections in patellofemoral pain

Patellofemoral pain is associated with complex gait deviations captured by an artificial intelligence-derived gait index

Can the combination of kinematics and electromyography enhance the identification of individuals with patellofemoral pain?

Functional task kinematics in older adults: the role of fear of falling and fall history as measured by the movement deviation profile

Task-specific differentiation of patellofemoral pain in women using a neural network analysis of joint angle subsets

Two sides of the same runner! The association between biomechanical and physiological markers of endurance performance in recreational runners

Patellar tendon shortening surgery restores the knee extensor mechanism in flexed knee gait in children with cerebral palsy

Characterization of movement patterns using unsupervised learning neural networks: exploring a novel approach for monitoring athletes during sidestepping

The movement deviation profile can differentiate faller and non-faller older adults

Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder

Step down tests are the tasks that most differentiate the kinematics of women with patellofemoral pain compared to asymptomatic controls

Leaving hip rotation out of a conventional 3D gait model improves discrimination of pathological gait in cerebral palsy: a novel neural network analysis

A gait index may underestimate changes of gait: a comparison of the Movement Deviation Profile and the Gait Deviation Index

Movement Deviation Profile: a measure of distance from normality using a self-organizing neural network

Gait quality assessment using self-organising artificial neural networks

Visualisation of gait data with Kohonen self organising neural maps

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