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Updated: Nov 27, 2025
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
Published on: March 4, 2018
Characterizing Normal and Pathological Gait through Permutation Entropy
Massimiliano Zanin1,2, David Gómez-Andrés3,4, Irene Pulido-Valdeolivas3,5
1Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain.
Insights
Researchers analyzed joint kinematics in children with cerebral palsy (CP) using permutation entropy. CP children showed more complex, erratic joint control, offering insights for personalized medicine and disability mitigation strategies.
Area of Science:
- Neurology
- Biomedical Engineering
- Information Theory
Background:
- Cerebral palsy (CP) is a leading cause of childhood disability, characterized by gait abnormalities due to perinatal brain lesions.
- Understanding the neural dynamics underlying CP gait is crucial for developing effective interventions.
- Current knowledge on brain adaptation to CP and its impact on gait remains limited.
Purpose of the Study:
- To investigate joint kinematics in children with CP compared to typically developing controls using permutation entropy.
- To explore the relationship between permutation entropy, gait speed, and neural control in CP.
- To assess the potential of permutation entropy for clinical applications and personalized medicine.
Main Methods:
- Analysis of joint kinematics data from children with cerebral palsy and matched control subjects.
- Application of permutation entropy, an information theory measure, to quantify complexity in joint control.
- Development of a data mining model utilizing permutation entropy for condition forecasting.
Main Results:
- Children with cerebral palsy exhibited a significant increase in permutation entropy compared to controls.
- This finding indicates more complex and erratic neural control of joints in CP.
- A non-trivial relationship was observed between permutation entropy and gait speed.
Conclusions:
- Permutation entropy serves as a valuable measure for assessing neural control complexity in CP gait.
- The findings support the use of permutation entropy in data mining models for CP condition forecasting.
- Results highlight the potential for personalized medicine interventions in managing CP-related disabilities.
Abstract:
Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last few years, for being the complex result of the interactions between different brain motor areas and thus a proxy in the understanding of the underlying neural dynamics. Yet, and in spite of its importance, little is still known about how the brain adapts to cerebral palsy and to its impaired gait and, consequently, about the best strategies for mitigating the disability. In this contribution, we present the hitherto first analysis of joint kinematics data using permutation entropy, comparing cerebral palsy children with a set of matched control subjects. We find a significant increase in the permutation entropy for the former group, thus indicating a more complex and erratic neural control of joints and a non-trivial relationship between the permutation entropy and the gait speed. We further show how this information theory measure can be used to train a data mining model able to forecast the child's condition. We finally discuss the relevance of these results in clinical applications and specifically in the design of personalized medicine interventions.
