Al Kouzbary, Hamza and Al Kouzbary, Mouaz and Tham, Lai Kuan and Liu, Jingjing and Shasmin, Hanie Nadia and Abu Osman, Noor Azuan (2022) Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs. IEEE Transactions on Neural Networks and Learning Systems, 33 (11). pp. 6297-6305. ISSN 2162-237X, DOI https://doi.org/10.1109/TNNLS.2021.3076060.
Full text not available from this repository.Abstract
One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1 degrees +/- 1.7 degrees. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p-value).
| Item Type: | Article |
|---|---|
| Funders: | Platcom HIP-2 [AIM/PlaTCOM/HIP2/CCGF/2017/168] |
| Uncontrolled Keywords: | Legged locomotion; Control systems; Sensors; Prosthetics; Switches; Foot; Mechanical sensors; Artificial neural network (ANN); Hierarchical control system; High-level control system; Nonlinear autoregressive network with an exogenous input (NARX) network; Pattern generator; Powered ankle-foot |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Aug 2023 04:01 |
| Last Modified: | 30 Aug 2023 04:01 |
| URI: | http://eprints.um.edu.my/id/eprint/41028 |
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