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.

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.

scholarly journals Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Qiulin Wang
Keyword(s):  
Deep Learning ◽  
Lower Limb ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Limb Amputation ◽  
Deep Learning Algorithm ◽  
The Disabled ◽  
Lower Limb Prosthesis ◽  
Movement Intention ◽  
Limb Prosthesis

Objective. In order to study the motion recognition intention of lower limb prosthesis based on the CNN deep learning algorithm. Methods. A convolutional neural network (CNN) model was established to reconstruct the motion pattern. Before the movement mode of the affected side was converted, the sensor was bound to the healthy side. The classifier was employed to extract and classify the features, so as to realize the accurate description of the movement intention of the disabled. Results. The method proposed in this research can achieve 98.2% recognition rate of the movement intention of patients with lower limb amputation under different terrains, and the recognition rate can reach 97% after the pattern converted between the five modes was added. Conclusion. The deep learning algorithm that automatically recognized and extracted features can effectively improve the control performance on the intelligent lower limb prosthesis and realize the natural and seamless conversion of the intelligent prosthesis in a variety of motion modes.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Min Sheng ◽  
Wan-Jun Wang ◽  
Ting-Ting Tong ◽  
Yuan-Yuan Yang ◽  
Hui-Lin Chen ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Lower Limb ◽  
Steady States ◽  
Statistical Features ◽  
Short Term ◽  
Intent Recognition ◽  
One Dimensional ◽  
Lower Limb Prosthesis ◽  
The One ◽  
Limb Prosthesis

The motion intent recognition via lower limb prosthesis can be regarded as a kind of short-term action recognition, where the major issue is to explore the gait instantaneous conversion (known as transitional pattern) between each two adjacent different steady states of gait mode. Traditional intent recognition methods usually employ a set of statistical features to classify the transitional patterns. However, the statistical features of the short-term signals via the instantaneous conversion are empirically unstable, which may degrade the classification accuracy. Bearing this in mind, we introduce the one-dimensional dual-tree complex wavelet transform (1D-DTCWT) to address the motion intent recognition via lower limb prosthesis. On the one hand, the local analysis ability of the wavelet transform can amplify the instantaneous variation characteristics of gait information, making the extracted features of instantaneous pattern between two adjacent different steady states more stable. On the other hand, the translation invariance and direction selectivity of 1D-DTCWT can help to explore the continuous features of patterns, which better reflects the inherent continuity of human lower limb movements. In the experiments, we have recruited ten able-bodied subjects and one amputee subject and collected data by performing five steady states and eight transitional states. The experimental results show that the recognition accuracy of the able-bodied subjects has reached 98.91%, 98.92%, and 97.27% for the steady states, transitional states, and total motion states, respectively. Furthermore, the accuracy of the amputee has reached 100%, 91.16%, and 90.27% for the steady states, transitional states, and total motion states, respectively. The above evidence finally indicates that the proposed method can better explore the gait instantaneous conversion (better expressed as motion intent) between each two adjacent different steady states compared with the state-of-the-art.

Nonlinear Passive Cam-Based Springs for Powered Ankle Prostheses

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Jonathan Realmuto ◽  
Glenn Klute ◽  
Santosh Devasia
Keyword(s):  
Lower Limb ◽  
Nonlinear Response ◽  
Experimental Results ◽  
Passive Element ◽  
Ankle Torque ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Elastic Elements

This article studies the design of passive elastic elements to reduce the actuator requirements for powered ankle prostheses. The challenge is to achieve most of the typically nonlinear ankle response with the passive element so that the active ankle-torque from the actuator can be small. The main contribution of this article is the design of a cam-based lower-limb prosthesis to achieve such a nonlinear ankle response. Results are presented to show that the addition of the cam-based passive element can reduce the peak actuator torque requirement substantially, by ∼74%. Moreover, experimental results are presented to demonstrate that the cam-based design can achieve a desired nonlinear response to within 10%.

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