Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.
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http://dx.doi.org/10.1007/s11042-022-13698-y | DOI Listing |
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
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January 2025
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.
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January 2025
Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations.
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January 2025
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.
Pedestrian detection is widely used in real-time surveillance, urban traffic, and other fields. As a crucial direction in pedestrian detection, dense pedestrian detection still faces many unresolved challenges. Existing methods suffer from low detection accuracy, high miss rates, large model parameters, and poor robustness.
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January 2025
Department of Artifcial Intelligence, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea.
Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.
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