A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods
- PMID: 11166554
- DOI: 10.1016/s0966-6362(00)00094-1
A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods
Abstract
In recent years, several new approaches to gait data analysis have been explored, including fuzzy systems, multivariate statistical techniques and fractal dynamics. Through a critical survey of recent gait studies, this paper reviews the potential of these methods to strengthen the gait laboratory's analytical arsenal. It is found that time-honoured multivariate statistical methods are the most widely applied and understood. Although initially promising, fuzzy and fractal analyses of gait data remain largely unknown and their full potential is yet to be realized. The trend towards fusing multiple techniques in a given analysis means that additional research into the application of these two methods will benefit gait data analysis.
Similar articles
-
A review of analytical techniques for gait data. Part 2: neural network and wavelet methods.Gait Posture. 2001 Apr;13(2):102-20. doi: 10.1016/s0966-6362(00)00095-3. Gait Posture. 2001. PMID: 11240358 Review.
-
Application of fractal theory and fuzzy enhancement in ultrasound image segmentation.Med Biol Eng Comput. 2019 Mar;57(3):623-632. doi: 10.1007/s11517-018-1907-z. Epub 2018 Oct 9. Med Biol Eng Comput. 2019. PMID: 30302667
-
Characterization of medical time series using fuzzy similarity-based fractal dimensions.Artif Intell Med. 2003 Feb;27(2):201-22. doi: 10.1016/s0933-3657(02)00114-8. Artif Intell Med. 2003. PMID: 12636979
-
Local fuzzy fractal dimension and its application in medical image processing.Artif Intell Med. 2004 Sep;32(1):29-36. doi: 10.1016/j.artmed.2004.01.016. Artif Intell Med. 2004. PMID: 15350622
-
Fractals in the neurosciences, Part II: clinical applications and future perspectives.Neuroscientist. 2015 Feb;21(1):30-43. doi: 10.1177/1073858413513928. Epub 2013 Dec 20. Neuroscientist. 2015. PMID: 24362814 Review.
Cited by
-
Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders.Front Neurol. 2021 May 21;12:666458. doi: 10.3389/fneur.2021.666458. eCollection 2021. Front Neurol. 2021. PMID: 34093413 Free PMC article.
-
Fractal analysis of concurrently prepared latex rubber casts of the bronchial and vascular systems of the human lung.Open Biol. 2020 Jul;10(7):190249. doi: 10.1098/rsob.190249. Epub 2020 Jul 8. Open Biol. 2020. PMID: 32634372 Free PMC article.
-
Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology.Sensors (Basel). 2022 Apr 2;22(7):2728. doi: 10.3390/s22072728. Sensors (Basel). 2022. PMID: 35408342 Free PMC article.
-
Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.Elife. 2024 Apr 30;13:e86132. doi: 10.7554/eLife.86132. Elife. 2024. PMID: 38686919 Free PMC article.
-
Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.PLoS One. 2025 Jan 7;20(1):e0312415. doi: 10.1371/journal.pone.0312415. eCollection 2025. PLoS One. 2025. PMID: 39774494 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources