1
|
Li Z, Gao L, Lu W, Wang D, Cao H, Zhang G. Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR. SENSORS 2022; 22:s22124651. [PMID: 35746432 PMCID: PMC9231143 DOI: 10.3390/s22124651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023]
Abstract
During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.
Collapse
Affiliation(s)
- Zebin Li
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
- Department of Science Island, University of Science and Technology of China, Hefei 230026, China
- School of Electrical and Photoelectric Engineering, West Anhui University, Lu’an 237012, China;
- Correspondence: (Z.L.); (W.L.)
| | - Lifu Gao
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
- Department of Science Island, University of Science and Technology of China, Hefei 230026, China
| | - Wei Lu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
- Department of Science Island, University of Science and Technology of China, Hefei 230026, China
- Correspondence: (Z.L.); (W.L.)
| | - Daqing Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
| | - Huibin Cao
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (L.G.); (D.W.); (H.C.)
| | - Gang Zhang
- School of Electrical and Photoelectric Engineering, West Anhui University, Lu’an 237012, China;
| |
Collapse
|
2
|
Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN-SVM. SENSORS 2022; 22:s22093439. [PMID: 35591128 PMCID: PMC9102926 DOI: 10.3390/s22093439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
Abstract
With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle-robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.
Collapse
|
3
|
Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.01.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
4
|
Xiao Y, Feng J, Liu B. A new transductive learning method with universum data. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02113-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
5
|
Tariq Jamal A, Ben Ishak A, Abdel-Khalek S. Tumor edge detection in mammography images using quantum and machine learning approaches. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05518-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
6
|
|
7
|
Zhao X, Zhang Z, Bi X, Sun Y. A new point-of-interest group recommendation method in location-based social networks. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04979-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
8
|
Ding S, Sun Y, An Y, Jia W. Multiple birth support vector machine based on recurrent neural networks. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01655-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
9
|
Li G, Han D, Wang C, Hu W, Calhoun VD, Wang YP. Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105073. [PMID: 31525548 DOI: 10.1016/j.cmpb.2019.105073] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/27/2019] [Accepted: 09/06/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. METHODS In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. RESULTS The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. CONCLUSIONS Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.
Collapse
Affiliation(s)
- Gang Li
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, China.
| | - Depeng Han
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
| | - Chao Wang
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
| | - Wenxing Hu
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.
| | - Vince D Calhoun
- Mind Research Network and Department of ECE, University of New Mexico, Albuquerque, NM 87106, USA.
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.
| |
Collapse
|
10
|
|
11
|
Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
Collapse
Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| |
Collapse
|
12
|
Tang L, Tian Y, Pardalos PM. A novel perspective on multiclass classification: Regular simplex support vector machine. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.12.026] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
13
|
de Lima MD, Costa NL, Barbosa R. Improvements on least squares twin multi-class classification support vector machine. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.040] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
14
|
|
15
|
Zhang C, Zheng X, Ni H, Li P, Li HJ. Discovery of quality control markers from traditional Chinese medicines by fingerprint-efficacy modeling: Current status and future perspectives. J Pharm Biomed Anal 2018; 159:296-304. [DOI: 10.1016/j.jpba.2018.07.006] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 07/05/2018] [Accepted: 07/07/2018] [Indexed: 01/11/2023]
|
16
|
|
17
|
A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting. ENERGIES 2018. [DOI: 10.3390/en11041009] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
18
|
Chen C, Li Y, Yan C, Guo J, Liu G. Least absolute deviation-based robust support vector regression. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.06.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
19
|
Rastogi R, Sharma S, Chandra S. Robust Parametric Twin Support Vector Machine for Pattern Classification. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9633-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|