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.

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

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 The Role of Earlier Receipt of a Lower Limb Prosthesis on Emergency Department Utilization

PM&R ◽  
2020 ◽  
Author(s):  
Taavy A. Miller ◽  
Rajib Paul ◽  
Melinda Forthofer ◽  
Shane R. Wurdeman
Keyword(s):  
Emergency Department ◽  
Lower Limb ◽  
Emergency Department Utilization ◽  
Lower Limb Prosthesis ◽  
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.

An Echo-Based Gait Phase Determination Method of Lower Limb Prosthesis

2013 ◽  
Vol 706-708 ◽  
pp. 629-634 ◽  
Author(s):  
Wen Jun Wang ◽  
Juan Li ◽  
Wei Da Li ◽  
Li Ning Sun
Keyword(s):  
Test Data ◽  
Lower Limb ◽  
Healthy Subjects ◽  
Motion Information ◽  
Determination Method ◽  
Phase Determination ◽  
Lower Limb Prosthesis ◽  
Gait Phase ◽  
Motion Parameters ◽  
Limb Prosthesis

The gait phase determination is the basis of the control of intelligent prosthesis. The prosthetic usually determines the gait phase according to its own motion parameters, which will cause the asymmetry gait of the prosthesis wearer during level walking due to the lack of contralateral motion information. By using of a variety of sensors installed in the healthy leg and prosthesis, the gait phase determination method based on echo control can achieve good symmetry, but at the same time also made calculation difficult. Based on the existing echo method, a gait phase determination method by utilize of the contralateral motion parameters is proposed. According to gait test data collected from healthy subjects, motion parameters and their relation can be obtained. Then the proposed method can determine its gait phase and contralateral phase. Preliminary experiment is conducted and verified the effective of the method.

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

scholarly journals Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms

2021 ◽  
Author(s):  
Min Sheng ◽  
Wanjun Wang ◽  
Tingting Tong ◽  
Yuanyuan Yang ◽  
Huilin Chen ◽  
...  
Keyword(s):  
Lower Limb ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Intent Recognition ◽  
Instantaneous Change ◽  
Motion Modes ◽  
Lower Limb Prosthesis ◽  
Complex Wavelet ◽  
Limb Prosthesis ◽  
Motion Behavior

Abstract Background: Most traditional intent recognition methods are to recognizing the movement of lower limb prosthesis through statistical features, which are unstable in short-term signals. The another key problem with recognition of lower limb prosthesis motion intent is to explore the instantaneous change between the two difffferent steady modes. Based on the above considerations, the one-dimensional dual-tree complex wavelet transform(1D-DTCWT) is introduced for motion intent recognition in intelligent prosthesis. Methods: 1D-DTCWT adopts two-way complex wavelet transforms with a binary tree structure as functional data analysis (FDA) method that preserves the time-frequency local analysis capabilities of wavelet transforms while maintaining translation invariance and direction selection. Therefore, the 1D-DTCWT can amplify the instantaneous change information hidden in the data while retaining the continuity of the motion behavior, so as to better recognize the motion intention. Furthermore, the feature vector composed of low-frequency wavelet coeffiffifficients decomposed by 1D-DTCWT is classifified and recognized by support vector machine (SVM), which can effffectively classify and recognize the motion intent of the unilateral lower limb amputees. Results: The data of the experiment comes from ten able-bodied subjects and one amputee subject to analyze 5 steady modes, 8 transitional modes, and 13 total motion modes adopting user-dependent and user-independent methods. The experimental results from the user-dependent methods show that the recognition rate for able-bodied subjects reached 98.91%, 98.92%, and 97.27% for the movement modes of steady modes, transitional modes, and total motion modes, respectively. The recognition rate of the amputee subject reached 100%, 91.16%, and 89.27%, respectively, for the three modes. Conclusions: The method in this paper can effffectively solve the problem of short-term signal instability reflflected by traditional statistical feature recognition of motion intent and explore the instantaneous change information of transitional modes while retaining the continuity of the motion behavior.

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