ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 7; p. 2389
• Main Authors: Vu, Huong Thi Thu, Gomez, Felipe, Cherelle, Pierre, Lefeber, Dirk, Nowé, Ann, Vanderborght, Bram
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.07.2018; MDPI
• Subjects: Accuracy; Algorithms; Amputation; Artificial intelligence; Deep learning; Electromyography; exoskeleton; Exoskeletons; Gait; gait event detection; gait phase prediction; gait recognition; Inertial platforms; International conferences; lower limb prosthesis; Neural networks; Phase transitions; Prostheses; Sensors; Skin; Wearable technology; United States > US; Germany; exoskeleton; gait recognition; gait event detection; lower limb prosthesis; gait phase prediction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18072389

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection 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 percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects' signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.