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  • Open Access

    ARTICLE

    Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940

    Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires a wide range of body… More >

  • Open Access

    ARTICLE

    Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

    Sangeeta Yadav1, Preeti Gulia1,*, Nasib Singh Gill1,*, Piyush Kumar Shukla2, Arfat Ahmad Khan3, Sultan Alharby4, Ahmed Alhussen4, Mohd Anul Haq5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869

    Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the model lightweight, the already designed… More >

  • Open Access

    ARTICLE

    Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

    K. Akilandeswari1, Nithya Rekha Sivakumar2,*, Hend Khalid Alkahtani3, Shakila Basheer3, Sara Abdelwahab Ghorashi2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1189-1205, 2024, DOI:10.32604/cmc.2023.034815

    Abstract In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression Feature Extraction model is used… More >

  • Open Access

    ARTICLE

    Research on Human Activity Recognition Algorithm Based on LSTM-1DCNN

    Yuesheng Zhao1, Xiaoling Wang1,*, Yutong Luo2,*, Muhammad Shamrooz Aslam3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3325-3347, 2023, DOI:10.32604/cmc.2023.040528

    Abstract With the rapid advancement of wearable devices, Human Activities Recognition (HAR) based on these devices has emerged as a prominent research field. The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer. This algorithm comprises two branches: one branch consists of a Long and Short-Term Memory Network (LSTM), while the other parallel branch incorporates a one-dimensional Convolutional Neural Network (1DCNN). The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately, which are then concatenated and fed into a fully… More >

  • Open Access

    ARTICLE

    Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

    Jianguo Wang1, Kuan Zhang1,*, Yuesheng Zhao2,*, Xiaoling Wang2, Muhammad Shamrooz Aslam2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506

    Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm,… More >

  • Open Access

    ARTICLE

    Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

    Sakorn Mekruksavanich1, Narit Hnoohom2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549

    Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet and motion information from wearable… More >

  • Open Access

    ARTICLE

    Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

    Mesfer Al Duhayyim*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6587-6603, 2023, DOI:10.32604/cmc.2023.033045

    Abstract Elderly or disabled people can be supported by a human activity recognition (HAR) system that monitors their activity intervenes and patterns in case of changes in their behaviors or critical events have occurred. An automated HAR could assist these persons to have a more independent life. Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system. With the fast development of neural networks, computing, and machine learning algorithms, HAR system based on wearable sensors has gained popularity in several areas, such as medical services, smart homes, improving human communication with computers,… More >

  • Open Access

    ARTICLE

    Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

    Bareen Shamsaldeen Tahir1, Zainab Salih Ageed2, Sheren Sadiq Hasan3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4009-4024, 2023, DOI:10.32604/cmc.2023.037433

    Abstract Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep learning (DL). The study develops… More >

  • Open Access

    ARTICLE

    Adaptive Weighted Flow Net Algorithm for Human Activity Recognition Using Depth Learned Features

    G. Augusta Kani*, P. Geetha

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1447-1469, 2023, DOI:10.32604/csse.2023.035969

    Abstract Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are useful in many tasks that… More >

  • Open Access

    ARTICLE

    Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

    Sarah Alzahrani1, Joud Alderaan1, Dalya Alatawi1, Bandar Alotaibi1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 651-667, 2023, DOI:10.32604/cmc.2023.035173

    Abstract Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric… More >

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