ETRO VUB
About ETRO  |  News  |  Events  |  Vacancies  |  Contact  
Home Research Education Industry Publications About ETRO

ETRO Publications

Full Details

Journal Publication

ED-FNN: A new deep learning algorithm to detect percentage of the gait cycle for powered prostheses

This publication appears in: Sensors

Authors: V. Thi Thu Huong, F. Gomez Marulanda, P. Cherelle, D. Lefeber, A. Nowé and B. Vanderborght

Volume: 18

Number of Pages: 19

Publication Date: Jul. 2018


Abstract:

Throughout the last decade a whole new generation of powered transtibial prosthesis and exoskeletons have been developed. However, these technologies are constraint by a gait detection phase which controls the wearable device in function of the activities of the wearer. Consequently, the gait detection phase is considered to be of great importance as achieving high detection accuracy will produce a more precise, stable and safe rehabilitation device. In this paper we propose a novel gait phase detection algorithm that can predict 100 percent of the gait cycle by implementing an Exponential Delayed Fully Connected Neural Network (ED-FNN) algorithm. Furthermore, due to the forecasting capabilities of the ED-FNN, the system shows no-delayed in detection for real-time applications. The signals fed into the model were taken from only one Initial Measurement Unit (IMU) attached to the lower shank. A large dataset was obtained from 7 healthy subjects performing a daily walking on the flat ground and a 15-degree slope. The results from training individual subjects showed an average of 0.003 MSE (Mean Square Error) in the validation set. MSE values were almost the same both training and validation off-line. When training the dataset from all subjects, the result showed a slight increase in the average with 0.01 MSE obtained from validation set. The method obtained high detection performance compared to the existing algorithms and we propose a concept of gait detection implemented on the prostheses.

Other Reference Styles
Other Publications

• Journal publications

IRIS • LAMI • AVSP

• Conference publications

IRIS • LAMI • AVSP

• Book publications

IRIS • LAMI • AVSP

• Reports

IRIS • LAMI • AVSP

• Laymen publications

IRIS • LAMI • AVSP

• PhD Theses

Search ETRO Publications

Author:

Keyword:  

Type:








- Contact person

- IRIS

- AVSP

- LAMI

- Contact person

- Thesis proposals

- ETRO Courses

- Contact person

- Spin-offs

- Know How

- Journals

- Conferences

- Books

- Vacancies

- News

- Events

- Press

Contact

ETRO Department

info@etro.vub.ac.be

Tel: +32 2 629 29 30

©2019 • Vrije Universiteit Brussel • ETRO Dept. • Pleinlaan 2 • 1050 Brussels • Tel: +32 2 629 2930 (secretariat) • Fax: +32 2 629 2883 • WebmasterDisclaimer