A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

Zhang, Haoxi; Xiao, Zhiwen; Wang, Juan; Li, Fēi; Szczerbicki, Edward

doi:10.1109/jiot.2019.2949715

Cited by 147 publications

(56 citation statements)

References 26 publications

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“…Activity recognition involves the use of movement sensors such as accelerometers, gyroscopes and magnetometers with the aim to help provide the user with feedback on their health in terms of them having enough physical exercise or not, used for sports therapy, fall detection and for monitoring of different diseases such as Parkinson's or other motor degenerative ailments. The most popular sensor for activity recognition are inertial sensors which have been used by [133,134] in a cloud based setting using various deep and machine learning algorithms. In [135], Castro et al include vital sign data in addition to movement information for human activity recognition in a cloud environment, they utilize the DT as their classifier.…”

Section: Smart Healthmentioning

confidence: 99%

“…Moreover, an edge computing system is presented in [152] which utilizes EEG signals to determine seizures in patients. [133] Homogeneous (Accelerometer) CNN [134] RNN (LSTM) [153] Fog Edge Heterogeneous (Accelerometer, Gyroscope, Magnetometer) CNN [137] Fog RF [138] Edge Heterogeneous (Accelerometer and Gyroscope) SVM [136] Patient health monitoring DT [139] Cloud Classification-Recommendation about diet etc.…”

Section: Smart Healthmentioning

confidence: 99%

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IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Healthmentioning

confidence: 99%

Section: Smart Healthmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…The following description shows that fully connected layers are heavily used in the architecture of the transformer encoder and result in a rapid increase in model size. Convolutional neural networks are used widely in computer vision, single processing, and NLP [39], [40]. Convolutional attention is also applied to several studies.…”

Section: B a Novel Lightweight Bert Architecture With Convolutional mentioning

confidence: 99%

Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Zeng

Paik

2021

IEEE Access

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Service clustering is an efficient method for facilitating service discovery and composition. Traditional approaches based on the self-description documents for services usually utilize service signatures. In Web service composition, service clustering can also be performed by the invocation relationship between services. Therefore, based on the successful development of several embedding techniques for words in several contexts, a novel deep learning-based service embedding using invocation sequences is devised for service clustering. Moreover, many microservices are being created because of the rapid development of the Internet of Things (IoT), and edge, and fog computing. Following these developments, Web service composition based on these environments has emerged in abundance.More efficient lightweight approaches to analyze large numbers of services are necessary for service clustering. Consequently, a lightweight deep learning-based approach for the semantic clustering of service composition is presented to address these requirements. In this paper, we first propose the concept of service embedding to capture semantic information from invocation sequences. Second, we suggest using state-of-the-art neural language sequence models for service embedding and develop a corresponding lightweight Bidirectional Encoder Representations of Transformers (BERT)-based model. Next, combined with K-means clustering, the semantic clustering of service composition is evaluated. Finally, the experimental results show that the clustering process can be effectively performed by the lightweight BERT-based model.

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“…The accuracy reached 91.97%, surpassing that of conventional SVM by 9% [25]. Zhang et al proposed a new method using the attention mechanism of CNNs and HAR and concentrating attention into a multihead CNN, thereby facilitating feature extraction and selection and elevating accuracy to 95.4% [26]. Zebin et al proposed a deep CNN model to classify five daily activities: walking, walking upstairs, walking downstairs, sitting for long periods of time and sleeping; the raw data from the accelerometer and the gyroscope of the wearable device served as the input, and the accuracy was 96.4% [27].…”

Section: Introductionmentioning

confidence: 99%

Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

2020

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This study proposed a wearable device capable of recognizing six human daily activities (walking, walking upstairs, walking downstairs, sitting, standing, and lying) through a deep learning algorithm. Existing wearable devices are mainly watches or wristbands, and almost none are to be worn on the waist. Wearable devices in the forms of watches and wristbands are unfriendly to patients who are critically ill, such as patients undergoing dialysis. Patients undergoing dialysis have artificial blood vessels on their arm, and they cannot perform intense exercise. For this type of users, general hand wearable devices cannot correctly identify wearers' activities. Therefore, we proposed a waist wearable device and these types of daily life activities to assess their exercise. The hardware of the wearable device consisted of an inertial sensor, which included a microcontroller, a three-axis accelerometer, and a three-axis gyroscope. The activity recognition algorithm of the software used motion signals acquisition, signal normalization, and a feature learning method. The feature learning method was based on a 1D convolutional neural network that automatically performed feature extraction and classification from raw data. One part of the experimental data was from the dataset of the University of California (UCI), and the other part was recorded by this study. To capture the data recorded, the wearable inertial sensing device was attached to the waists of 21 experimental participants who performed six common movements in a laboratorial environment, and the subsequent records were collected to verify the validity of the proposed deep learning algorithm in relation to the inertial sensor of the wearable device. For the six common activities in the UCI dataset and the data recorded, the recognition rates in the training sample reached 98.93% and 97.19%, respectively, and the recognition rates in the testing sample were 95.99% and 93.77%, respectively.

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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

Cited by 147 publications

References 26 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

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