Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients
Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increas...
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MDPI AG
2024-09-01
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| author | Jong-Chen Chen Hao-Ming Cheng |
| author_facet | Jong-Chen Chen Hao-Ming Cheng |
| author_sort | Jong-Chen Chen |
| collection | DOAJ |
| description | Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch). |
| format | Article |
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| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-09-01 |
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| series | Actuators |
| spelling | doaj-art-93518d1512b0495897e9165dd83bf4e12025-08-20T01:56:06ZengMDPI AGActuators2076-08252024-09-0113936210.3390/act13090362Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke PatientsJong-Chen Chen0Hao-Ming Cheng1Information Management, National Yunlin University of Science and Technology, Douliu 64002, TaiwanInformation Management, National Yunlin University of Science and Technology, Douliu 64002, TaiwanFreedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch).https://www.mdpi.com/2076-0825/13/9/362strokerehabilitationartificial neuromolecular systemself-adaptive learningrobotic arm |
| spellingShingle | Jong-Chen Chen Hao-Ming Cheng Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients Actuators stroke rehabilitation artificial neuromolecular system self-adaptive learning robotic arm |
| title | Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients |
| title_full | Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients |
| title_fullStr | Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients |
| title_full_unstemmed | Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients |
| title_short | Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients |
| title_sort | application of artificial neuromolecular system in robotic arm control to assist progressive rehabilitation for upper extremity stroke patients |
| topic | stroke rehabilitation artificial neuromolecular system self-adaptive learning robotic arm |
| url | https://www.mdpi.com/2076-0825/13/9/362 |
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