scholarly journals Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis

2010 ◽  
Vol 57 (3) ◽  
pp. 542-551 ◽  
Author(s):  
H.A. Varol ◽  
F. Sup ◽  
M. Goldfarb
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Intent Recognition ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Powered Lower Limb Prosthesis

Intent Recognition in a Powered Lower Limb Prosthesis Using Time History Information

2013 ◽  
Vol 42 (3) ◽  
pp. 631-641 ◽  
Author(s):  
Aaron J. Young ◽  
Ann M. Simon ◽  
Nicholas P. Fey ◽  
Levi J. Hargrove
Keyword(s):  
Lower Limb ◽  
Time History ◽  
Intent Recognition ◽  
History Information ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Powered Lower Limb Prosthesis

Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control

2016 ◽  
Vol 24 (2) ◽  
pp. 226-234 ◽  
Author(s):  
John A. Spanias ◽  
Eric J. Perreault ◽  
Levi J. Hargrove
Keyword(s):  
Lower Limb ◽  
Lower Limb Prosthesis ◽  
Prosthesis Control ◽  
Limb Prosthesis ◽  
Powered Lower Limb Prosthesis

scholarly journals Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dongfang Xu ◽  
Qining Wang
Keyword(s):  
Lower Limb ◽  
Control Strategies ◽  
Recognition Task ◽  
Intent Recognition ◽  
Mode Recognition ◽  
Lower Limb Prosthesis ◽  
Gait Phase ◽  
Robotic Prosthesis ◽  
Limb Prosthesis

The lower-limb robotic prostheses can provide assistance for amputees’ daily activities by restoring the biomechanical functions of missing limb(s). To set proper control strategies and develop the corresponding controller for robotic prosthesis, a prosthesis user’s intent must be acquired in time, which is still a major challenge and has attracted intensive attentions. This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective (locomotion mode recognition, gait event detection, and continuous gait phase estimation) and reviews the state-of-the-art intent recognition techniques in a lower-limb prosthesis scope. The current research status, including recognition approach, progress, challenges, and future prospects in the human’s intent recognition, has been reviewed. In particular for the recognition approach, the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition. This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis

2019 ◽  
Vol 27 (5) ◽  
pp. 1032-1042 ◽  
Author(s):  
Ben-Yue Su ◽  
Jie Wang ◽  
Shuang-Qing Liu ◽  
Min Sheng ◽  
Jing Jiang ◽  
...  
Keyword(s):  
Lower Limb ◽  
Inertial Measurement Units ◽  
Intent Recognition ◽  
Inertial Measurement ◽  
Lower Limb Prosthesis ◽  
Measurement Units ◽  
Limb Prosthesis

scholarly journals Real Time Estimation of the Pose of a Lower Limb Prosthesis from a Single Shank Mounted IMU

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2865 ◽  
Author(s):  
Duraffourg ◽  
Bonnet ◽  
Dauriac ◽  
Pillet
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Time Estimation ◽  
Measurement Unit ◽  
Correct Identification ◽  
Accelerometer Data ◽  
Mode Recognition ◽  
Lower Limb Prosthesis ◽  
Real Time Estimation ◽  
Limb Prosthesis

The command of a microprocessor-controlled lower limb prosthesis classically relies on the gait mode recognition. Real time computation of the pose of the prosthesis (i.e., attitude and trajectory) is useful for the correct identification of these modes. In this paper, we present and evaluate an algorithm for the computation of the pose of a lower limb prosthesis, under the constraints of real time applications and limited computing resources. This algorithm uses a nonlinear complementary filter with a variable gain to estimate the attitude of the shank. The trajectory is then computed from the double integration of the accelerometer data corrected from the kinematics of a model of inverted pendulum rolling on a curved arc foot. The results of the proposed algorithm are evaluated against the optoelectronic measurements of walking trials of three people with transfemoral amputation. The root mean square error (RMSE) of the estimated attitude is around 3°, close to the Kalman-based algorithm results reported in similar conditions. The real time correction of the integration of the inertial measurement unit (IMU) acceleration decreases the trajectory error by a factor of 2.5 compared to its direct integration which will result in an improvement of the gait mode recognition.

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