A Machine Learning Approach To Classify The Level Of Cognitive Impairment Among Elderly Population Using Gait Analysis

Registration ID: IJNRD_181302

Published ID: IJNRD2205029

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Christi Thomas , Devu Bhupesh , Saurav Santhosh , Veena Mohan , Jithin Jacob

Cognitive Impairment, Long Short Term Memory Networks, Gait Sequence Analysis, Machine Learning, Temporal Gait Parameters

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.

Published Paper   E-Certificate

Christi Thomas, Devu Bhupesh, Saurav Santhosh, Veena Mohan, & Jithin Jacob (May-2022). A Machine Learning Approach To Classify The Level Of Cognitive Impairment Among Elderly Population Using Gait Analysis. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(5), 274-289. https://ijnrd.org/papers/IJNRD2205029.pdf

Volume 7 Issue 5, May-2022

Pages : 274-289

Paper Reg. ID: IJNRD_181302

Published Paper Id: IJNRD2205029

Downloads: 000121978

Research Area: Computer Engineering 

Country: Kollam, Kerala, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2205029.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2205029

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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