Literature DB >> 19447724

Computational intelligence in gait research: a perspective on current applications and future challenges.

Daniel T H Lai1, Rezaul K Begg, Marimuthu Palaniswami.   

Abstract

Our mobility is an important daily requirement so much so that any disruption to it severely degrades our perceived quality of life. Studies in gait and human movement sciences, therefore, play a significant role in maintaining the well-being of our mobility. Current gait analysis involves numerous interdependent gait parameters that are difficult to adequately interpret due to the large volume of recorded data and lengthy assessment times in gait laboratories. A proposed solution to these problems is computational intelligence (CI), which is an emerging paradigm in biomedical engineering most notably in pathology detection and prosthesis design. The integration of CI technology in gait systems facilitates studies in disorders caused by lower limb defects, cerebral disorders, and aging effects by learning data relationships through a combination of signal processing and machine learning techniques. Learning paradigms, such as supervised learning, unsupervised learning, and fuzzy and evolutionary algorithms, provide advanced modeling capabilities for biomechanical systems that in the past have relied heavily on statistical analysis. CI offers the ability to investigate nonlinear data relationships, enhance data interpretation, design more efficient diagnostic methods, and extrapolate model functionality. These are envisioned to result in more cost-effective, efficient, and easy-to-use systems, which would address global shortages in medical personnel and rising medical costs. This paper surveys current signal processing and CI methodologies followed by gait applications ranging from normal gait studies and disorder detection to artificial gait simulation. We review recent systems focusing on the existing challenges and issues involved in making them successful. We also examine new research in sensor technologies for gait that could be combined with these intelligent systems to develop more effective healthcare solutions.

Entities:  

Mesh:

Year:  2009        PMID: 19447724     DOI: 10.1109/TITB.2009.2022913

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  8 in total

1.  Automatically Evaluating Balance: A Machine Learning Approach.

Authors:  Tian Bao; Brooke N Klatt; Susan L Whitney; Kathleen H Sienko; Jenna Wiens
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-01-04       Impact factor: 3.802

Review 2.  A comprehensive review of sensors and instrumentation methods in devices for musical expression.

Authors:  Carolina Brum Medeiros; Marcelo M Wanderley
Journal:  Sensors (Basel)       Date:  2014-07-25       Impact factor: 3.576

3.  A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics.

Authors:  Reginaldo K Fukuchi; Claudiane A Fukuchi; Marcos Duarte
Journal:  PeerJ       Date:  2017-05-09       Impact factor: 2.984

4.  Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals.

Authors:  Ning Ji; Hui Zhou; Kaifeng Guo; Oluwarotimi Williams Samuel; Zhen Huang; Lisheng Xu; Guanglin Li
Journal:  Sensors (Basel)       Date:  2019-08-08       Impact factor: 3.576

5.  Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge.

Authors:  Joris De Roeck; J Van Houcke; D Almeida; P Galibarov; L De Roeck; Emmanuel A Audenaert
Journal:  Front Bioeng Biotechnol       Date:  2020-04-02

6.  A Decision Support System to Facilitate Identification of Musculoskeletal Impairments and Propose Recommendations Using Gait Analysis in Children With Cerebral Palsy.

Authors:  Kohleth Chia; Igor Fischer; Pam Thomason; H Kerr Graham; Morgan Sangeux
Journal:  Front Bioeng Biotechnol       Date:  2020-11-27

7.  Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders.

Authors:  Christopher Fricke; Jalal Alizadeh; Nahrin Zakhary; Timo B Woost; Martin Bogdan; Joseph Classen
Journal:  Front Neurol       Date:  2021-05-21       Impact factor: 4.003

Review 8.  Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review.

Authors:  Azadeh Kian; Giwantha Widanapathirana; Anna M Joseph; Daniel T H Lai; Rezaul Begg
Journal:  Sensors (Basel)       Date:  2022-03-14       Impact factor: 3.576

  8 in total

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