| Autor: |
Ismael Espinoza Jaramillo, Jin Gyun Jeong, Patricio Rivera Lopez, Choong-Ho Lee, Do-Yeon Kang, Tae-Jun Ha, Ji-Heon Oh, Hwanseok Jung, Jin Hyuk Lee, Won Hee Lee, Tae-Seong Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 22, Iss 24, p 9690 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s22249690 |
| Popis: |
Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot’s control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer’s activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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