September 10, 2025Sensor Review

Gait cycle segmentation and fall detection of healthy individuals using IMU sensors and deep learning

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Authors

NGNeha GaudDevi Ahilya Vishwavidyalaya MRMaya RathoreDevi Ahilya Vishwavidyalaya USUgrasen SumanDevi Ahilya Vishwavidyalaya

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Implication

This analysis demonstrates improved gait periodicity and fall detection in healthy adults using IMU sensors, suggesting enhanced independence for the elderly.

Key Points

The hybrid deep learning model achieved an accuracy of 99.12% in fall detection across various activities.
Average stride duration was found to be 1.06 seconds for 15 healthy subjects, aligning with existing benchmarks.
The method utilizes a unique gait cycle extraction technique based on autocorrelation of time series data.
The study highlights the importance of gait periodicity in assessing fall risk for elderly individuals.

Cite This Study

Gaud et al. (2025) studied this question.

synapsesocial.com/papers/68c23b08b210217d64782831 https://doi.org/10.1108/sr-04-2025-0208

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Implication

This analysis demonstrates improved gait periodicity and fall detection in healthy adults using IMU sensors, suggesting enhanced independence for the elderly.

Gait cycle segmentation and fall detection of healthy individuals using IMU sensors and deep learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Gait cycle segmentation and fall detection of healthy individuals using IMU sensors and deep learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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