Match!

Multi-DoF continuous estimation for wrist torques using stacked autoencoder

Published on Mar 1, 2020in Biomedical Signal Processing and Control2.943
· DOI :10.1016/J.BSPC.2019.101733
Yang Yu1
Estimated H-index: 1
(SJTU: Shanghai Jiao Tong University),
Chen Chen1
Estimated H-index: 1
(SJTU: Shanghai Jiao Tong University)
+ 1 AuthorsXiangyang Zhu26
Estimated H-index: 26
(SJTU: Shanghai Jiao Tong University)
Abstract
Abstract Human machine interface (HMI) based on surface electromyography (sEMG) promises to provide an intuitive and noninvasive way to interact with peripheral equipments, such as prostheses, exoskeletons, and robots. Most recently, advances in machine learning, especially in deep learning algorithms, present the capabilities in constructing complicated mapping functions. In this study, we construct a stacked autoencoder-based deep neural network (SAE-DNN) to continuously estimate multiple degrees-of-freedom (DoFs) kinetics of wrist from sEMG signals. During the experiments, high-density sEMG signals and multi-DoF wrist torques were simultaneously acquired under the guidance of a visual feedback system, with eight healthy subjects and an amputee recruited. Moreover, the estimation performance of SAE-DNN was compared with two of commonly used conventional regressors, linear regression (LR) and support vector regression (SVR). As a consequence, the results demonstrate the feasibility of this scheme and significant superiority of SAE-DNN over LR and SVR with higher R 2 values across all DoFs (SAE-DNN: 0.829 ± 0.050 , LR: 0.757 ± 0.075 , SVR: 0.751 ± 0.079 ). The outcomes of this study provide us with a perspective and a feasible scheme for simultaneous and proportional control.
  • References (38)
  • Citations (1)
📖 Papers frequently viewed together
2015ICIRA: International Conference on Intelligent Robotics and Applications
4 Authors (Wei Yang, ..., Hong Liu)
1 Citations
2017
2 Citations
78% of Scinapse members use related papers. After signing in, all features are FREE.
References38
Newest
#1Tamas Kapelner (GAU: University of Göttingen)H-Index: 5
#2Ivan Vujaklija (Aalto University)H-Index: 13
Last. Dario Farina (Imperial College London)H-Index: 80
view all 7 authors...
Background Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG...
1 CitationsSource
#1Angkoon PhinyomarkH-Index: 23
#2Erik SchemeH-Index: 24
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. N...
10 CitationsSource
#2Syed Omer GilaniH-Index: 6
Last. Ernest Nlandu KamavuakoH-Index: 17
view all 7 authors...
4 CitationsSource
#1Janne M. Hahne (GAU: University of Göttingen)H-Index: 10
#2Meike A. Schweisfurth (Hamburg University of Applied Sciences)H-Index: 7
Last. Dario Farina (Imperial College London)H-Index: 80
view all 4 authors...
Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world cond...
11 CitationsSource
#1Keum-Shik HongH-Index: 50
#2Nida AzizH-Index: 1
Last. Usman GhafoorH-Index: 4
view all 3 authors...
: During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via natural-feeling interfaces. The peripheral nerves provide access to highly processed and segregated neural command signals from the brain that can in principle be used to determine user intent and control muscles. If these signals could be used, they ...
11 CitationsSource
Last. Ander Ramos-Murguialday (University of Tübingen)H-Index: 16
view all 5 authors...
Objective : In light of the shortcomings of current restorative brain–computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. Methods : We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patien...
3 CitationsSource
#1Ivan Vujaklija (Imperial College London)H-Index: 13
#2Vahid Shalchyan (IUST: Iran University of Science and Technology)H-Index: 4
Last. Dario Farina (Imperial College London)H-Index: 80
view all 6 authors...
In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized...
11 CitationsSource
#1Jiangcheng Chen (Xi'an Jiaotong University)H-Index: 1
#2Xiaodong Zhang (Xi'an Jiaotong University)H-Index: 8
Last. NingXi (HKU: University of Hong Kong)H-Index: 30
view all 4 authors...
Abstract Surface electromyography (EMG) signals have been widely used in locomotion studies and human-machine interface applications. In this paper, a regression model which relates the multichannel surface EMG signals to human lower limb flexion/extension (FE) joint angles is constructed. In the experimental paradigm, three dimensional trajectories of 16 external markers on the human lower limbs were recorded by optical motion capture system and surface EMG signals from 10 muscles directly conc...
10 CitationsSource
#1Guang-Zhong Yang (Imperial College London)H-Index: 66
#2James G. Bellingham (WHOI: Woods Hole Oceanographic Institution)H-Index: 31
Last. Robert J. Wood (Wyss Institute for Biologically Inspired Engineering)H-Index: 61
view all 17 authors...
One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts th...
117 CitationsSource
#1Chuang Lin (CAS: Chinese Academy of Sciences)H-Index: 8
#2Binghui Wang (Iowa State University)H-Index: 10
Last. Dario Farina (Imperial College London)H-Index: 80
view all 4 authors...
Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Mai...
13 CitationsSource
Cited By1
Newest
#1Jiayuan He (UW: University of Waterloo)
#1Jiayuan He (UW: University of Waterloo)H-Index: 8
Last. Ning Jiang (UW: University of Waterloo)H-Index: 31
view all 2 authors...
Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG), the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG) and electroencephalogram (EEG), enabling users to customize their own gesture codes. The perfo...
Source