A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot
Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/19/3986 |
| _version_ | 1818146742161375232 |
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| author | Tao Yang Xueshan Gao Rui Gao Fuquan Dai Jinmin Peng |
| author_facet | Tao Yang Xueshan Gao Rui Gao Fuquan Dai Jinmin Peng |
| author_sort | Tao Yang |
| collection | DOAJ |
| description | Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types. |
| first_indexed | 2024-12-11T12:24:11Z |
| format | Article |
| id | doaj.art-37a472acdc54492ea294f869663305f1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T12:24:11Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-37a472acdc54492ea294f869663305f12022-12-22T01:07:26ZengMDPI AGApplied Sciences2076-34172019-09-01919398610.3390/app9193986app9193986A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation RobotTao Yang0Xueshan Gao1Rui Gao2Fuquan Dai3Jinmin Peng4School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical & Automotive Engineering, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Mechanical & Automotive Engineering, Fujian University of Technology, Fuzhou 350118, ChinaRobot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types.https://www.mdpi.com/2076-3417/9/19/3986rehabilitation robotsmultisensor fusion systemcontrol strategiesactivity recognition |
| spellingShingle | Tao Yang Xueshan Gao Rui Gao Fuquan Dai Jinmin Peng A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot Applied Sciences rehabilitation robots multisensor fusion system control strategies activity recognition |
| title | A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot |
| title_full | A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot |
| title_fullStr | A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot |
| title_full_unstemmed | A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot |
| title_short | A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot |
| title_sort | novel activity recognition system for alternative control strategies of a lower limb rehabilitation robot |
| topic | rehabilitation robots multisensor fusion system control strategies activity recognition |
| url | https://www.mdpi.com/2076-3417/9/19/3986 |
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