{"title":"Topological Gait Analysis: A New Framework and Its Application to the Study of Human Gait","authors":"Shreyam Mishra;Debasish Chatterjee;Neeta Kanekar","doi":"10.1109/JBHI.2024.3427700","DOIUrl":null,"url":null,"abstract":"<italic>Objective:</i>\n This study introduces a physiologically driven topological gait analysis (TGA) framework to gain insights into pathological gait. \n<italic>Methods:</i>\n A publicly available gait dataset consisting of four groups: healthy adults, people with Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) was used. The topological properties of the configuration space of three gait parameters were studied by approximating the underlying distribution through a Gaussian kernel-based density estimation technique. Thereafter, sublevel sets of the density estimate were analyzed using cubical persistence homology. \n<italic>Results:</i>\n Three new features were constructed: 1. Probability density estimates (PDEs) that characterize the distribution of gait parameters over their configuration space. Healthy adults exhibited a unimodal distribution, while people with neurodegenerative disorders displayed a multi-modal distribution. 2. Persistence entropy plots that summarize changes in the PDEs and characterize the uncertainty in the underlying distribution. Gait of healthy adults was concentrated at higher entropy values as opposed to neurodegenerative gait. 3. A number \n<inline-formula><tex-math>$\\alpha _{s}$</tex-math></inline-formula>\n that captures disease severity trends. \n<italic>Conclusions:</i>\n Topological features in PD and HD indicate a ‘bias’ to a certain set of gait configurations. This lack of exploration may reflect poor planning of the underlying topology, resulting in outward manifestations of impaired gait. The lower variegations in PDEs in ALS compared to PD and HD suggest that the planning of the topology of gait may occur at higher levels of the neural architecture. \n<italic>Significance:</i>\n TGA offers characterization of gait at a hitherto uncharted level, potentially serving neuromotor markers for early diagnosis and personalized rehabilitation protocols.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"28 12","pages":"7040-7053"},"PeriodicalIF":6.7000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Biomedical and Health Informatics","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10779507/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective:
This study introduces a physiologically driven topological gait analysis (TGA) framework to gain insights into pathological gait.
Methods:
A publicly available gait dataset consisting of four groups: healthy adults, people with Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) was used. The topological properties of the configuration space of three gait parameters were studied by approximating the underlying distribution through a Gaussian kernel-based density estimation technique. Thereafter, sublevel sets of the density estimate were analyzed using cubical persistence homology.
Results:
Three new features were constructed: 1. Probability density estimates (PDEs) that characterize the distribution of gait parameters over their configuration space. Healthy adults exhibited a unimodal distribution, while people with neurodegenerative disorders displayed a multi-modal distribution. 2. Persistence entropy plots that summarize changes in the PDEs and characterize the uncertainty in the underlying distribution. Gait of healthy adults was concentrated at higher entropy values as opposed to neurodegenerative gait. 3. A number
$\alpha _{s}$
that captures disease severity trends.
Conclusions:
Topological features in PD and HD indicate a ‘bias’ to a certain set of gait configurations. This lack of exploration may reflect poor planning of the underlying topology, resulting in outward manifestations of impaired gait. The lower variegations in PDEs in ALS compared to PD and HD suggest that the planning of the topology of gait may occur at higher levels of the neural architecture.
Significance:
TGA offers characterization of gait at a hitherto uncharted level, potentially serving neuromotor markers for early diagnosis and personalized rehabilitation protocols.
期刊介绍:
IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.