Prediction of Passive Torque on Human Shoulder Joint Based on BPANN
In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed a...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2020/8839791 |
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| _version_ | 1832547035216084992 |
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| author | Shuyang Li Paolo Dario Zhibin Song |
| author_facet | Shuyang Li Paolo Dario Zhibin Song |
| author_sort | Shuyang Li |
| collection | DOAJ |
| description | In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient’s joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists. |
| format | Article |
| id | doaj-art-bb7811c13f0b44aa90d1b766ff3fba51 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-bb7811c13f0b44aa90d1b766ff3fba512025-02-03T06:46:18ZengWileyApplied Bionics and Biomechanics1176-23221754-21032020-01-01202010.1155/2020/88397918839791Prediction of Passive Torque on Human Shoulder Joint Based on BPANNShuyang Li0Paolo Dario1Zhibin Song2Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaIn upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient’s joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists.http://dx.doi.org/10.1155/2020/8839791 |
| spellingShingle | Shuyang Li Paolo Dario Zhibin Song Prediction of Passive Torque on Human Shoulder Joint Based on BPANN Applied Bionics and Biomechanics |
| title | Prediction of Passive Torque on Human Shoulder Joint Based on BPANN |
| title_full | Prediction of Passive Torque on Human Shoulder Joint Based on BPANN |
| title_fullStr | Prediction of Passive Torque on Human Shoulder Joint Based on BPANN |
| title_full_unstemmed | Prediction of Passive Torque on Human Shoulder Joint Based on BPANN |
| title_short | Prediction of Passive Torque on Human Shoulder Joint Based on BPANN |
| title_sort | prediction of passive torque on human shoulder joint based on bpann |
| url | http://dx.doi.org/10.1155/2020/8839791 |
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