Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Alluhaidan, Ala Saleh; Alajmi, Masoud; Al‐Wesabi, Fahd N.; Hilal, Anwer Mustafa; Hamza, Manar Ahmed; Motwakel, Abdelwahed

doi:10.32604/cmc.2022.025202

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The results showed that combining both models is efficient and practical to the developed system, with 92.5% accuracy. To identify fall/non-fall events, the research in [25] [26,27] has been used in this study. This dataset was acquired from 14 healthy individuals by using a wearable smartwatch.…”

Section: Related Workmentioning

confidence: 99%

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Karar¹,

Reyad²,

Shehata³

2023

Computer Systems Science and Engineering

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This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

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Section: Related Workmentioning

confidence: 99%

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Karar¹,

Reyad²,

Shehata³

2023

Computer Systems Science and Engineering

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“…The outcomes reveal that BiLSTM is a good model for handling missing values in wearable falling detection systems due to its ability to maintain long-term connectivity. The proposed study in [23] presented an improved Archimedes optimization method (IAOM) with DL augmented Fall detection model to distinguish between fall and non-fall events. The IAOM significantly enhances the detection of falls functionality by using a superior set of CapsNet hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Reyad,

Ibrahim Shehata,

Esmail Karar

2023

Computers, Materials &Amp; Continua

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Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models-K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

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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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Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Cited by 4 publications

References 20 publications

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

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

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