Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder
Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased sh...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025004992 |
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| author | Sina Saadati Abdolah Sepahvand Mohammadreza Razzazi |
| author_facet | Sina Saadati Abdolah Sepahvand Mohammadreza Razzazi |
| author_sort | Sina Saadati |
| collection | DOAJ |
| description | Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and computational requirements. |
| format | Article |
| id | doaj-art-4fdf6804d7fb49baa638d4b554f7e096 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-4fdf6804d7fb49baa638d4b554f7e0962025-02-02T05:29:00ZengElsevierHeliyon2405-84402025-01-01112e42119Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorderSina Saadati0Abdolah Sepahvand1Mohammadreza Razzazi2Corresponding author.; Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranDepartment of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranDepartment of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranMotion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and computational requirements.http://www.sciencedirect.com/science/article/pii/S2405844025004992Agent-based modeling and simulationCloud and IoT based simulationHuman gaitAgent-driven simulationMuscles disorder |
| spellingShingle | Sina Saadati Abdolah Sepahvand Mohammadreza Razzazi Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder Heliyon Agent-based modeling and simulation Cloud and IoT based simulation Human gait Agent-driven simulation Muscles disorder |
| title | Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder |
| title_full | Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder |
| title_fullStr | Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder |
| title_full_unstemmed | Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder |
| title_short | Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder |
| title_sort | cloud and iot based smart agent driven simulation of human gait for detecting muscles disorder |
| topic | Agent-based modeling and simulation Cloud and IoT based simulation Human gait Agent-driven simulation Muscles disorder |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025004992 |
| work_keys_str_mv | AT sinasaadati cloudandiotbasedsmartagentdrivensimulationofhumangaitfordetectingmusclesdisorder AT abdolahsepahvand cloudandiotbasedsmartagentdrivensimulationofhumangaitfordetectingmusclesdisorder AT mohammadrezarazzazi cloudandiotbasedsmartagentdrivensimulationofhumangaitfordetectingmusclesdisorder |