Secure Your Steps: A Class-Based Ensemble Framework for Real-Time Fall Detection Using Deep Neural Networks

Kabir, Md. Mohsin; Shin, Jungpil; Mridha, M. F.

doi:10.1109/access.2023.3289402

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Cited by 3 publications

References 52 publications

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A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Gharghan,

Hashim

2024

Measurement

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A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Gharghan,

Hashim

2024

Measurement

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Fall Detection Using HOG Feature Extraction and Adaptive Boosting Technique

Devle,

Jadhav,

Kokate

et al. 2023

2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS)

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A Novel Embedded Deep Learning Wearable Sensor for Fall Detection

Campanella,

Alnasef,

Falaschetti

et al. 2024

IEEE Sensors J.

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Falls and their aftermath pose significant healthcare challenges, impacting individuals across various age groups and occupational backgrounds. These incidents detrimentally affect functional mobility and overall quality of life, necessitating a comprehensive approach to fall detection systems in diverse populations. Therefore, wearable devices are necessary to continuously monitor activities. This work introduces a novel deep-learning model specifically optimized for edge devices capable of detecting falls. The wearable sensor integrates a pressure sensor, a three-axis gyroscope, and a three-axis accelerometer. The developed system works in real-time with the dual objective of identifying the activities carried out and classifying them as falls or daily life activities. We evaluated this approach using both our self-collected dataset and a publicly available one (SisFall). Furthermore, in our dataset, we also introduced the syncope between falls that the sensor must be able to detect. Results demonstrate that while maintaining low-cost, low-complexity of the model, low-power consumption, and high-speed data processing, combining usage of the three sensors and deep learning algorithm allows to obtain an accuracy of 99.38%, and inference time of 25 ms.

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Secure Your Steps: A Class-Based Ensemble Framework for Real-Time Fall Detection Using Deep Neural Networks

Cited by 3 publications

References 52 publications

A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Fall Detection Using HOG Feature Extraction and Adaptive Boosting Technique

A Novel Embedded Deep Learning Wearable Sensor for Fall Detection

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