Student’s Health Exercise Recognition Tool for E-Learning Education

Shloul, Tamara al; Gochoo, Munkhjargal; Alsuhibany, Suliman A.; Ghadi, Yazeed Yasin; Jalal, Ahmad; Park, Jeong-Min

doi:10.32604/iasc.2023.026051

Cited by 15 publications

(12 citation statements)

References 31 publications

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“…active pattern. Shloul et al [35] introduced a student's health exercise recognition framework to recognize students' indoor activities for physical education. The system used a modified Quaternion-based filter, data fusion, segmentation, static-kinematic patterns identification, features extraction along with optimization, and classification via extended Kalman filter-based neural networks.…”

Section: A Imu Sensors For Indoor-environmentsmentioning

confidence: 99%

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Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

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Section: A Imu Sensors For Indoor-environmentsmentioning

confidence: 99%

“…In order to take care of the shortcomings mentioned in previous section, the suggested method applies a variety of algorithms combination based on experiments conducted [4,8,26,35]. We proposed to use two publicly available datasets for this study, namely, REALDISP [44] and wearable computing [45].…”

Section: Proposed Smart Healthcare Learningmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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“…Today, after almost a year and a half of exclusive and mandatory distance learning imposed by the pandemic, the learning processes of higher education have been tested in terms of applicability, feasibility and long-term sustainability. Although the health situation is not yet fully stabilized, the educational models deployed during the most critical period must be evaluated [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

et al. 2022

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With the immersion of a plethora of technological tools in the early post-COVID-19 era in university education, instructors around the world have been at the forefront of implementing hybrid learning spaces for knowledge delivery. The purpose of this experimental study is not only to divert the primary use of a YouTube channel into a tool to support asynchronous teaching; it also aims to provide feedback to instructors and suggest steps and actions to implement in their teaching modules to ensure students’ access to new knowledge while promoting their engagement and satisfaction, regardless of the learning environment, i.e., face-to-face, distance and hybrid. Learners’ viewing habits were analyzed in depth from the channel’s 37 instructional videos, all of which were related to the completion of a computer-aided mechanical design course. By analyzing and interpreting data directly from YouTube channel reports, six variables were identified and tested to quantify the lack of statistically significant changes in learners’ viewing habits. Two time periods were specifically studied: 2020–2021, when instruction was delivered exclusively via distance education, and 2021–2022, in a hybrid learning mode. The results of both parametric and non-parametric statistical tests showed that “Number of views” and “Number of unique viewers” are the two variables that behave the same regardless of the two time periods studied, demonstrating the relevance of the proposed concept for asynchronous instructional support regardless of the learning environment. Finally, a forthcoming instructor’s manual for learning CAD has been developed, integrating the proposed methodology into a sustainable academic educational process.

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“…Against the background of the COVID-19 pandemic, online learning has become a vital alternative learning mode worldwide [1,2]. As learners and instructors have been separated physically, the convenience and flexibility of online environments have guaranteed basic educational needs [3,4]. However, the socially isolated features of online learning settings have also caused challenges for instructors in monitoring their learners' progress and in identifying learners who are at risk [5].…”

Section: Introductionmentioning

confidence: 99%

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Wang

Zhang

2022

Sustainability

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Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors to identify underachieving students in school-based online learning, which may obstruct timely guidance and impede learning performance. Exploring small-sample-supported learning performance prediction and personalized feedback methods is an urgent need to mitigate these shortcomings. Consequently, considering the problem of insufficient data, this study proposes a machine learning model for learning performance prediction with additional pre-training and fine-tuning phases, and constructs a personalized feedback generation method to improve the online learning effect. With a quasi-experiment involving 62 participants (33 in experimental group and 29 in control group), the validity of the prediction model and personalized feedback generation, and the impact of the personalized feedback on learning performance and cognitive load, were evaluated. The results revealed that the proposed model reached a relatively high level of accuracy compared to the baseline models. Additionally, the students who learned with personalized feedback performed significantly better in terms of learning performance and showed a lower cognitive load.

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Student’s Health Exercise Recognition Tool for E-Learning Education

Cited by 15 publications

References 31 publications

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

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