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

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 detec...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 18; H. 7; S. 2389
Hauptverfasser: Vu, Huong Thi Thu, Gomez, Felipe, Cherelle, Pierre, Lefeber, Dirk, Nowé, Ann, Vanderborght, Bram
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 23.07.2018
MDPI
Schlagworte:
Accuracy
Algorithms
Amputation
Artificial intelligence
Electromyography
exoskeleton
Gait
gait event detection
gait phase prediction
gait recognition
International conferences
lower limb prosthesis
Neural networks
Phase transitions
Prostheses
Sensors
Skin
United States > US
Germany
exoskeleton
gait recognition
gait event detection
lower limb prosthesis
gait phase prediction
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18072389