Human Fall Motion Prediction – A Review

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Published: Sep 30, 2024
DOI: https://doi.org/10.33093/ijoras.2024.6.2.8
Keywords:
Human Fall Motion Prediction, Human Motion, Fall Detection System, IOT Devices, WEARABLE SENSORS

Main Article Content

Raphon Galuh Candraningtyas
Faculty of Informatics, Institut Teknologi Telkom Purwokerto (Indonesia)
Andi Prademon Yunus
Faculty of Informatics, Institut Teknologi Telkom Purwokerto (Indonesia)
Yit Hong Choo
Institute for Intelligent System Research and Innovation, Deakin University (Australia)

Abstract

Abstract – In predicting human fall motion, focused on enhancing safety and quality of life for the elderly and individuals at risk of falls. By highlighting the critical role of Human Pose Estimation, advancements in human motion forecasting, and fall prediction. It explores the continuous efforts to improve fall detection systems using innovative technologies, such as wearable sensors and IoT devices to implement deep learning models and analyze human poses and gestures. Various methods show promise in accurately predicting human fall motion by capturing complex patterns and relationships in the data. For instance, self-attention mechanisms can revolutionize human motion prediction by effectively capturing these intricate patterns, leading to more accurate predictions. Future research directions should focus on enhancing model accuracy, exploring new techniques for capturing complex patterns, and enabling real-time implementation in wearable devices or smart environments. By addressing these areas, fall detection systems can be significantly improved, benefiting individuals and healthcare systems worldwide.


[Manuscript received: 15 Apr 2024 | Accepted: 12 Jun 2024 | Published: : 30 Sep 2024]

Article Details

How to Cite
Candraningtyas, R. G., Yunus, A. P., & Choo, Y. H. (2024). Human Fall Motion Prediction – A Review. International Journal on Robotics, Automation and Sciences, 6(2), 52–58. https://doi.org/10.33093/ijoras.2024.6.2.8
Issue
Vol. 6 No. 2 (2024): International Journal on Robotics, Automation and Sciences
Section
Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Author Biography

Raphon Galuh Candraningtyas, Faculty of Informatics, Institut Teknologi Telkom Purwokerto (Indonesia)

I’m a data science major at Institut Teknologi Telkom Purwokerto, specializing in data engineering. With a strong foundation in Python, machine learning, artificial intelligence (AI), natural language processing (NLP), Database, and Statistics, I am actively expanding my knowledge in the field of data science. I am eager to acquire practical experience in data science through diverse projects. As a self-motivated and detail-oriented individual, I thrive in independent study environments, where I diligently pursue opportunities to expand my knowledge and skills. With a strong commitment to continuous learning and staying up to date with the latest advancements in data science, I am well-prepared to make a valuable contribution to any company or institution I may work with in the future.

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