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  • Open Access

    ARTICLE

    A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

    Kun Fang, Julong Pan*, Lingyi Li, Ruihan Xiang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008

    Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach… More >

  • Open Access

    ARTICLE

    A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1275-1291, 2023, DOI:10.32604/iasc.2023.038584

    Abstract In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features is vital in making RST… More >

  • Open Access

    ARTICLE

    Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

    Hammad Rustam1, Muhammad Muneeb1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara Al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2331-2346, 2023, DOI:10.32604/cmc.2023.028712

    Abstract Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear discrimination analysis feature extraction, and… More >

  • Open Access

    ARTICLE

    Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

    Sakorn Mekruksavanich1, Anuchit Jitpattanakul2,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2111-2128, 2023, DOI:10.32604/iasc.2023.033542

    Abstract The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The… More >

  • Open Access

    ARTICLE

    Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

    Mir Mushhood Afsar1, Shizza Saqib1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Ahmad Jalal1, Jeongmin Park4,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4763-4777, 2022, DOI:10.32604/cmc.2022.028618

    Abstract Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth… More >

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