|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
||||||||||||||
Issue: April 2024
Volume 9 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 95
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | A Machine Learning Approach To Classify The Level Of Cognitive Impairment Among Elderly Population Using Gait Analysis |
| Authors Name: | Christi Thomas , Devu Bhupesh , Saurav Santhosh , Veena Mohan , Jithin Jacob |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_181302
|
| Published Paper Id: | IJNRD2205029 |
| Published In: | Volume 7 Issue 5, May-2022 |
| DOI: | |
| Abstract: | Cognitive impairment in the elderly population affects the performance of daily living activities which in turn, reflects the economic burden on society. Diagnosing cognitive impairment can be detained due to the medical assessment requiring considerable time and effort. This model uses a machine learning approach to classify the level of cognitive impairment using sequential gait analysis. The dataset consisting of gait sequence data collected from a group of 106 elderly participants in two sessions and the participants were categorized into three groups based on their scores on the mini-mental state examination. Each participant underwent the usual- and fast-paced walking along a straight line with two measurement units on each foot. These units are equipped with a 3-axis gyroscope and accelerometer. The sagittal angular velocity signals collected from each gait cycle using the measurement units were analyzed to construct gait parameters. A machine learning model called the long short-term memory network was employed to produce the classifiers that used the time- consecutive temporal gait parameters as predictors of cognitive impairment. This proposal helps to detect the CI in the elderly at the earliest stages without the clinical professional help, hence training them improve cognitive skills conveniently. |
| Keywords: | Cognitive Impairment, Long Short Term Memory Networks, Gait Sequence Analysis, Machine Learning, Temporal Gait Parameters |
| Cite Article: | "A Machine Learning Approach To Classify The Level Of Cognitive Impairment Among Elderly Population Using Gait Analysis", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 5, page no.274-289, May-2022, Available :http://www.ijnrd.org/papers/IJNRD2205029.pdf |
| Downloads: | 000118750 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2205029 Registration ID: 181302 Published In: Volume 7 Issue 5, May-2022 DOI (Digital Object Identifier): Page No: 274-289 Country: Kollam, Kerala, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2205029 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2205029 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn