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Mrs. Huong Vu
Vrije Universiteit Brussel

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Research Keywords & Expertise

0 wearable sensors
0 Gait phase detection
0 event detection
0 lower limb prosthesis
0 gait phase classification

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Review
Published: 17 July 2020 in Sensors
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Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

ACS Style

Huong Thi Thu Vu; Dianbiao Dong; Hoang-Long Cao; Tom Verstraten; Dirk Lefeber; Bram VanderBorght; Joost Geeroms. A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. Sensors 2020, 20, 3972 .

AMA Style

Huong Thi Thu Vu, Dianbiao Dong, Hoang-Long Cao, Tom Verstraten, Dirk Lefeber, Bram VanderBorght, Joost Geeroms. A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. Sensors. 2020; 20 (14):3972.

Chicago/Turabian Style

Huong Thi Thu Vu; Dianbiao Dong; Hoang-Long Cao; Tom Verstraten; Dirk Lefeber; Bram VanderBorght; Joost Geeroms. 2020. "A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses." Sensors 20, no. 14: 3972.

Original research paper
Published: 12 October 2018 in Intelligent Service Robotics
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In this paper, we present a novel dual-robot testbed called DualKeepon for carrying out pairwise comparisons of linguistic features of speech in human–robot interactions. Our solution, using a modified version of the MyKeepon robotic toy developed by Beatbots, is a portable open-source system for researchers to set up experiments quickly, and in an intuitive way. We provide an online tutorial with all required materials to replicate the system. We present two human–robot interaction studies to demonstrate the testbed. The first study investigates the perception of robots using filled pauses. The second study investigates how social roles, realized by different prosodic and lexical speaking profiles, affect trust. Results show that the proposed testbed is a helpful tool for linguistic studies. In addition to the basic setup, advanced users of the system have the ability to connect the system to different robot platforms, i.e., NAO, Pepper.

ACS Style

Hoang-Long Cao; Lars Christian Jensen; Xuan Nhan Nghiem; Huong Vu; Albert De Beir; Pablo Gomez Esteban; Greet Van De Perre; Dirk Lefeber; Bram VanderBorght. DualKeepon: a human–robot interaction testbed to study linguistic features of speech. Intelligent Service Robotics 2018, 12, 45 -54.

AMA Style

Hoang-Long Cao, Lars Christian Jensen, Xuan Nhan Nghiem, Huong Vu, Albert De Beir, Pablo Gomez Esteban, Greet Van De Perre, Dirk Lefeber, Bram VanderBorght. DualKeepon: a human–robot interaction testbed to study linguistic features of speech. Intelligent Service Robotics. 2018; 12 (1):45-54.

Chicago/Turabian Style

Hoang-Long Cao; Lars Christian Jensen; Xuan Nhan Nghiem; Huong Vu; Albert De Beir; Pablo Gomez Esteban; Greet Van De Perre; Dirk Lefeber; Bram VanderBorght. 2018. "DualKeepon: a human–robot interaction testbed to study linguistic features of speech." Intelligent Service Robotics 12, no. 1: 45-54.

Journal article
Published: 23 July 2018 in Sensors
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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.

ACS Style

Huong Thi Thu Vu; Felipe Gomez; Pierre Cherelle; Dirk Lefeber; Ann Nowé; Bram VanderBorght. ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses. Sensors 2018, 18, 2389 .

AMA Style

Huong Thi Thu Vu, Felipe Gomez, Pierre Cherelle, Dirk Lefeber, Ann Nowé, Bram VanderBorght. ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses. Sensors. 2018; 18 (7):2389.

Chicago/Turabian Style

Huong Thi Thu Vu; Felipe Gomez; Pierre Cherelle; Dirk Lefeber; Ann Nowé; Bram VanderBorght. 2018. "ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses." Sensors 18, no. 7: 2389.