Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things

Khalid, Asmaa M.; Khafaga, Doaa Sami; Aldakheel, Eman Abdullah; Hosny, Khalid M.

doi:10.3390/s23135862

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…One application of the IoT is human activity recognition (HAR), where smart devices can monitor and recognize human activities for various purposes. HAR is essential in a number of industries, including the sport [3,4], healthcare [5][6][7], and smart environment industries [8][9][10][11]; information about human activities has been collected using smartphones and wearable sensor technologies [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

Wazwaz,

Amin,

Semary

et al. 2024

JSAN

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A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.

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Section: Introductionmentioning

confidence: 99%

“…Smartphone sensors have been widely used in machine learning; they have been used to recognize different categories of daily life activities. In recent research, several papers used smartphone sensors and focused on enhancing prediction accuracy and optimizing algorithms to speed up processing [14,15,32].…”

Section: Introductionmentioning

confidence: 99%

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

Wazwaz,

Amin,

Semary

et al. 2024

JSAN

View full text Add to dashboard Cite

show abstract

“…These devices find widespread usage in the healthcare sector, as well as in certain industries and sports fields [ 15 ]. In recent years, there has been a significant increase in the use of wearable biomedical sensors in healthcare, with a focus on monitoring vital signs in real time to manage chronic diseases [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

Mata-Romero,

Simental-Martínez,

Guerrero-Osuna

et al. 2024

Sensors

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The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

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“…Moreover, even healthy individuals can utilize the systems proactively. Studying the activities of healthy people could aid early disease detection in some cases [4]. The systems could also improve well-being by providing insights into daily activity patterns.…”

Section: Introductionmentioning

confidence: 99%

Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

Mekruksavanich,

Jitpattanakul

2024

Applied Sciences

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Human activity recognition (HAR) identifies people’s motions and actions in daily life. HAR research has grown with the popularity of internet-connected, wearable sensors that capture human movement data to detect activities. Recent deep learning advances have enabled more HAR research and applications using data from wearable devices. However, prior HAR research often focused on a few sensor locations on the body. Recognizing real-world activities poses challenges when device positioning is uncontrolled or initial user training data are unavailable. This research analyzes the feasibility of deep learning models for both position-dependent and position-independent HAR. We introduce an advanced residual deep learning model called Att-ResBiGRU, which excels at accurate position-dependent HAR and delivers excellent performance for position-independent HAR. We evaluate this model using three public HAR datasets: Opportunity, PAMAP2, and REALWORLD16. Comparisons are made to previously published deep learning architectures for addressing HAR challenges. The proposed Att-ResBiGRU model outperforms existing techniques in accuracy, cross-entropy loss, and F1-score across all three datasets. We assess the model using k-fold cross-validation. The Att-ResBiGRU achieves F1-scores of 86.69%, 96.23%, and 96.44% on the PAMAP2, REALWORLD16, and Opportunity datasets, surpassing state-of-the-art models across all datasets. Our experiments and analysis demonstrate the exceptional performance of the Att-ResBiGRU model for HAR applications.

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Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things

Cited by 4 publications

References 48 publications

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

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