Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization

Sardar, Abdul Wasay; Ullah, Farman; Bacha, Jamshid; Khan, Jebran; Ali, Furqan; Lee, Sungchang

doi:10.1016/j.compbiomed.2022.105662

Cited by 14 publications

(7 citation statements)

References 25 publications

(10 reference statements)

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“…The experimental results indicated that body temperature is lower when a human is involved in dynamic activities (e.g., walking) compared with static activities (e.g., sitting), which may potentially cause misses in fever screening for COVID-19 prevention. Sardar et al ( 2022 ) extended human activity recognition to handwashing, standing, hand sanitizing, nose-eyes touching, handshake, and drinking.…”

Section: Using Big Data To Measure Human Behavior In Covid-19mentioning

confidence: 99%

Human behavior in the time of COVID-19: Learning from big data

et al. 2023

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Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

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Section: Using Big Data To Measure Human Behavior In Covid-19mentioning

confidence: 99%

Human behavior in the time of COVID-19: Learning from big data

et al. 2023

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“…Recently, electronic devices (such as smartphones) are becoming a daily de facto tool. Mobiles have many integrated sensors such as GPS locators, gyroscopes, and accelerometers [ 32 , 33 ]. These sensors provide data both remotely and accurately.…”

Section: Related Workmentioning

confidence: 99%

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children

Ullah

Ali

Ullah

et al. 2023

Sensors

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The last decade’s developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children’s communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children’s gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children’s gestures, and the results showed more than 96% recognition accuracy.

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“…Fan et al [ 20 ] proposed a squeezed convolutional gated attention (SCGA) model to recognize basketball shooting postures fused by various sensors. Sardar et al [ 21 ] proposed a mobile sensor-based human physical activity recognition platform for COVID-19-related physical activity recognition, such as hand washing, hand disinfection, nose-eye contact, and handshake, as well as contact tracing, to minimize the spread of COVID-19.…”

Section: Main Recognition Techniquesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization

Cited by 14 publications

References 25 publications

Human behavior in the time of COVID-19: Learning from big data

Human behavior in the time of COVID-19: Learning from big data

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children

A Survey on Artificial Intelligence in Posture Recognition

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