Prediction Algorithm of Parameters of Toe Clearance in the Swing Phase. (14th August 2019)
- Record Type:
- Journal Article
- Title:
- Prediction Algorithm of Parameters of Toe Clearance in the Swing Phase. (14th August 2019)
- Main Title:
- Prediction Algorithm of Parameters of Toe Clearance in the Swing Phase
- Authors:
- Miyake, Tamon
Fujie, Masakatsu G.
Sugano, Shigeki - Other Names:
- Zollo Loredana Academic Editor.
- Abstract:
- Abstract : The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum andAbstract : The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase. … (more)
- Is Part Of:
- Applied bionics and biomechanics. Volume 2019(2019)
- Journal:
- Applied bionics and biomechanics
- Issue:
- Volume 2019(2019)
- Issue Display:
- Volume 2019, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 2019
- Issue:
- 2019
- Issue Sort Value:
- 2019-2019-2019-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-08-14
- Subjects:
- Bionics -- Periodicals
Biomechanics -- Periodicals
Biomedical engineering -- Periodicals
003.505 - Journal URLs:
- http://www.tandfonline.com/loi/tbob20 ↗
https://www.hindawi.com/journals/abb/ ↗
http://www.atypon-link.com/WHP/loi/abib ↗
http://www.informaworld.com/smpp/title~content=t778164488~db=all ↗ - DOI:
- 10.1155/2019/4502719 ↗
- Languages:
- English
- ISSNs:
- 1176-2322
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1571.911000
British Library HMNTS - ELD Digital store - Ingest File:
- 15896.xml