1
|
Guo J, Wang X, Bai R, Zhang Z, Chen H, Xue K, Ma C, Zang D, Yin E, Gao K, Ji B. A Cost-Effective and Easy-to-Fabricate Conductive Velcro Dry Electrode for Durable and High-Performance Biopotential Acquisition. BIOSENSORS 2024; 14:432. [PMID: 39329808 PMCID: PMC11430566 DOI: 10.3390/bios14090432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Revised: 09/02/2024] [Accepted: 09/04/2024] [Indexed: 09/28/2024]
Abstract
Compared with the traditional gel electrode, the dry electrode is being taken more seriously in bioelectrical recording because of its easy preparation, long-lasting ability, and reusability. However, the commonly used dry AgCl electrodes and silver cloth electrodes are generally hard to record through hair due to their flat contact surface. Claw electrodes can contact skin through hair on the head and body, but the internal claw structure is relatively hard and causes discomfort after being worn for a few hours. Here, we report a conductive Velcro electrode (CVE) with an elastic hook hair structure, which can collect biopotential through body hair. The elastic hooks greatly reduce discomfort after long-time wearing and can even be worn all day. The CVE electrode is fabricated by one-step immersion in conductive silver paste based on the cost-effective commercial Velcro, forming a uniform and durable conductive coating on a cluster of hook microstructures. The electrode shows excellent properties, including low impedance (15.88 kΩ @ 10 Hz), high signal-to-noise ratio (16.0 dB), strong water resistance, and mechanical resistance. After washing in laundry detergent, the impedance of CVE is still 16% lower than the commercial AgCl electrodes. To verify the mechanical strength and recovery capability, we conducted cyclic compression experiments. The results show that the displacement change of the electrode hook hair after 50 compression cycles was still less than 1%. This electrode provides a universal acquisition scheme, including effective acquisition of different parts of the body with or without hair. Finally, the gesture recognition from electromyography (EMG) by the CVE electrode was applied with accuracy above 90%. The CVE proposed in this study has great potential and promise in various human-machine interface (HMI) applications that employ surface biopotential signals on the body or head with hair.
Collapse
Affiliation(s)
- Jun Guo
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xuanqi Wang
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ruiyu Bai
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zimo Zhang
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Huazhen Chen
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Kai Xue
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Chuang Ma
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Intelligent Game and Decision Laboratory, Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Dawei Zang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Intelligent Game and Decision Laboratory, Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Kunpeng Gao
- College of Information Science and Technology, Donghua University, Shanghai 201620, China
| | - Bowen Ji
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| |
Collapse
|
2
|
Yu S, Sun X, Liu J, Li S. OECT - Inspired electrical detection. Talanta 2024; 275:126180. [PMID: 38703480 DOI: 10.1016/j.talanta.2024.126180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 04/16/2024] [Accepted: 04/27/2024] [Indexed: 05/06/2024]
Abstract
Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.
Collapse
Affiliation(s)
- Shixin Yu
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Xiaojun Sun
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Jingjing Liu
- School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China.
| | - Shuang Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
| |
Collapse
|
3
|
Ekinci E, Garip Z, Serbest K. Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms. Comput Biol Med 2024; 178:108812. [PMID: 38943945 DOI: 10.1016/j.compbiomed.2024.108812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.
Collapse
Affiliation(s)
- Ekin Ekinci
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Zeynep Garip
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Kasim Serbest
- Department of Mechatronics Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| |
Collapse
|
4
|
Romano F, Formenti D, Cardone D, Russo EF, Castiglioni P, Merati G, Merla A, Perpetuini D. Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics. ENTROPY (BASEL, SWITZERLAND) 2024; 26:578. [PMID: 39056940 PMCID: PMC11276346 DOI: 10.3390/e26070578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/25/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024]
Abstract
A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients.
Collapse
Affiliation(s)
- Francesco Romano
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (F.R.); (D.C.); (A.M.)
| | - Damiano Formenti
- Department of Biotechnology and Life Sciences, University of Insubria, 21100 Varese, Italy; (D.F.); (P.C.); (G.M.)
| | - Daniela Cardone
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (F.R.); (D.C.); (A.M.)
| | | | - Paolo Castiglioni
- Department of Biotechnology and Life Sciences, University of Insubria, 21100 Varese, Italy; (D.F.); (P.C.); (G.M.)
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | - Giampiero Merati
- Department of Biotechnology and Life Sciences, University of Insubria, 21100 Varese, Italy; (D.F.); (P.C.); (G.M.)
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (F.R.); (D.C.); (A.M.)
- UdA-TechLab, University G. D’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - David Perpetuini
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (F.R.); (D.C.); (A.M.)
| |
Collapse
|
5
|
Engdahl SM, Acuña SA, Kaliki RR, Sikdar S. Sonomyography for Control of Upper-Limb Prostheses: Current State and Future Directions. JOURNAL OF PROSTHETICS AND ORTHOTICS : JPO 2024; 36:174-184. [PMID: 38983244 PMCID: PMC11230649 DOI: 10.1097/jpo.0000000000000482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
ABSTRACT
Problem Statement
Despite the recent advancements in technology, many individuals with upper-limb loss struggle to achieve stable control over multiple degrees of freedom in a prosthesis. There is an ongoing need to develop noninvasive prosthesis control modalities that could improve functional patient outcomes.
Proposed Solution
Ultrasound-based sensing of muscle deformation, known as sonomyography, is an emerging sensing modality for upper-limb prosthesis control with the potential to significantly improve functionality. Sonomyography enables spatiotemporal characterization of both superficial and deep muscle activity, making it possible to distinguish the contributions of individual muscles during functional movements and derive a large set of independent prosthesis control signals. Using sonomyography to control a prosthesis has shown great promise in the research literature but has not yet been fully adapted for clinical use. This article describes the implementation of sonomyography for upper-limb prosthesis control, ongoing technological development, considerations for deploying this technology in clinical settings, and recommendations for future study.
Clinical Relevance
Sonomyography may soon become a clinically viable modality for upper-limb prosthesis control that could offer prosthetists an additional solution when selecting optimal treatment plans for their patients.
Collapse
Affiliation(s)
- Susannah M Engdahl
- Department of Bioengineering, George Mason University, Fairfax, VA
- Center for Adaptive Systems of Brain-Body Interactions, Fairfax, VA
| | - Samuel A Acuña
- Department of Bioengineering, George Mason University, Fairfax, VA
- Center for Adaptive Systems of Brain-Body Interactions, Fairfax, VA
| | | | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA
- Center for Adaptive Systems of Brain-Body Interactions, Fairfax, VA
| |
Collapse
|
6
|
Choo YJ, Lee GW, Moon JS, Chang MC. Application of non-contact sensors for health monitoring in hospitals: a narrative review. Front Med (Lausanne) 2024; 11:1421901. [PMID: 38933102 PMCID: PMC11199382 DOI: 10.3389/fmed.2024.1421901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The continuous monitoring of the health status of patients is essential for the effective monitoring of disease progression and the management of symptoms. Recently, health monitoring using non-contact sensors has gained interest. Therefore, this study aimed to investigate the use of non-contact sensors for health monitoring in hospital settings and evaluate their potential clinical applications. A comprehensive literature search was conducted using PubMed to identify relevant studies published up to February 26, 2024. The search terms included "hospital," "monitoring," "sensor," and "non-contact." Studies that used non-contact sensors to monitor health status in hospital settings were included in this review. Of the 38 search results, five studies met the inclusion criteria. The non-contact sensors described in the studies were radar, infrared, and microwave sensors. These non-contact sensors were used to obtain vital signs, such as respiratory rate, heart rate, and body temperature, and were then compared with the results from conventional measurement methods (polysomnography, nursing records, and electrocardiography). In all the included studies, non-contact sensors demonstrated a performance similar to that of conventional health-related parameter measurement methods. Non-contact sensors are expected to be a promising solution for health monitoring in hospital settings.
Collapse
Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Gun Woo Lee
- Department of Orthopaedic Surgery, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Jun Sung Moon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| |
Collapse
|
7
|
Hu Z, Wang S, Ou C, Ge A, Li X. Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals. SENSORS (BASEL, SWITZERLAND) 2024; 24:2702. [PMID: 38732808 PMCID: PMC11085498 DOI: 10.3390/s24092702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024]
Abstract
Currently, surface EMG signals have a wide range of applications in human-computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory results. Considering the strong nonlinear generalization ability of neural networks, this paper proposes a two-stream residual network model with an attention mechanism for gesture recognition. One branch processes surface EMG signals, while the other processes hand acceleration signals. Segmented networks are utilized to fully extract the physiological and kinematic features of the hand. To enhance the model's capacity to learn crucial information, we introduce an attention mechanism after global average pooling. This mechanism strengthens relevant features and weakens irrelevant ones. Finally, the deep features obtained from the two branches of learning are fused to further improve the accuracy of multi-gesture recognition. The experiments conducted on the NinaPro DB2 public dataset resulted in a recognition accuracy of 88.25% for 49 gestures. This demonstrates that our network model can effectively capture gesture features, enhancing accuracy and robustness across various gestures. This approach to multi-source information fusion is expected to provide more accurate and real-time commands for exoskeleton robots and myoelectric prosthetic control systems, thereby enhancing the user experience and the naturalness of robot operation.
Collapse
Affiliation(s)
- Zhigang Hu
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China; (Z.H.); (C.O.); (A.G.)
| | - Shen Wang
- School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471003, China;
| | - Cuisi Ou
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China; (Z.H.); (C.O.); (A.G.)
| | - Aoru Ge
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China; (Z.H.); (C.O.); (A.G.)
| | - Xiangpan Li
- School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471003, China;
| |
Collapse
|
8
|
Freitas M, Pinho F, Pinho L, Silva S, Figueira V, Vilas-Boas JP, Silva A. Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:2338. [PMID: 38610549 PMCID: PMC11014015 DOI: 10.3390/s24072338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024]
Abstract
Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.
Collapse
Affiliation(s)
- Marta Freitas
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - Francisco Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
| | - Liliana Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - Sandra Silva
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
- School of Health Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Vânia Figueira
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - João Paulo Vilas-Boas
- School of Health Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
- Centre for Research, Training, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Augusta Silva
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Department of Physiotherapy, School of Health, Polytechnic of Porto, 4200-072 Porto, Portugal
| |
Collapse
|
9
|
Park J, Lee Y, Cho S, Choe A, Yeom J, Ro YG, Kim J, Kang DH, Lee S, Ko H. Soft Sensors and Actuators for Wearable Human-Machine Interfaces. Chem Rev 2024; 124:1464-1534. [PMID: 38314694 DOI: 10.1021/acs.chemrev.3c00356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.
Collapse
Affiliation(s)
- Jonghwa Park
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Youngoh Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Seungse Cho
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Ayoung Choe
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Jeonghee Yeom
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Yun Goo Ro
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Jinyoung Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Dong-Hee Kang
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Seungjae Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| | - Hyunhyub Ko
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 44919, Republic of Korea
| |
Collapse
|
10
|
Moslhi AM, Aly HH, ElMessiery M. The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals. SENSORS (BASEL, SWITZERLAND) 2024; 24:1259. [PMID: 38400416 PMCID: PMC10893156 DOI: 10.3390/s24041259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel Signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced Signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the Signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the Signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation.
Collapse
Affiliation(s)
- Aly Medhat Moslhi
- Faculty of Engineering, The Arab Academy for Science, Technology & Maritime Transport, Smart Village Campus, Giza P.O. Box 2033, Egypt;
| | - Hesham H. Aly
- Faculty of Engineering, The Arab Academy for Science, Technology & Maritime Transport, Smart Village Campus, Giza P.O. Box 2033, Egypt;
| | - Medhat ElMessiery
- Faculty of Engineering, Cairo University, Giza P.O. Box 2033, Egypt;
| |
Collapse
|
11
|
Martinez-Velasco JD, Filomena-Ambrosio A, Garzón-Castro CL. Technological tools for the measurement of sensory characteristics in food: A review. F1000Res 2024; 12:340. [PMID: 38322308 PMCID: PMC10844804 DOI: 10.12688/f1000research.131914.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 02/08/2024] Open
Abstract
The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.
Collapse
Affiliation(s)
- José D Martinez-Velasco
- Engineering Faculty - Research Group CAPSAB, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia
| | - Annamaria Filomena-Ambrosio
- International School of Economics and Administrative Science - Research Group Alimentación, Gestión de Procesos y Servicio de la Universidad de La Sabana Research Group, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chía, Cundinamarca, 250001, Colombia
| | - Claudia L Garzón-Castro
- Engineering Faculty - Research Group CAPSAB, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia
| |
Collapse
|
12
|
Sparling T, Iyer L, Pasquina P, Petrus E. Cortical Reorganization after Limb Loss: Bridging the Gap between Basic Science and Clinical Recovery. J Neurosci 2024; 44:e1051232024. [PMID: 38171645 PMCID: PMC10851691 DOI: 10.1523/jneurosci.1051-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/28/2023] [Accepted: 09/29/2023] [Indexed: 01/05/2024] Open
Abstract
Despite the increasing incidence and prevalence of amputation across the globe, individuals with acquired limb loss continue to struggle with functional recovery and chronic pain. A more complete understanding of the motor and sensory remodeling of the peripheral and central nervous system that occurs postamputation may help advance clinical interventions to improve the quality of life for individuals with acquired limb loss. The purpose of this article is to first provide background clinical context on individuals with acquired limb loss and then to provide a comprehensive review of the known motor and sensory neural adaptations from both animal models and human clinical trials. Finally, the article bridges the gap between basic science researchers and clinicians that treat individuals with limb loss by explaining how current clinical treatments may restore function and modulate phantom limb pain using the underlying neural adaptations described above. This review should encourage the further development of novel treatments with known neurological targets to improve the recovery of individuals postamputation.Significance Statement In the United States, 1.6 million people live with limb loss; this number is expected to more than double by 2050. Improved surgical procedures enhance recovery, and new prosthetics and neural interfaces can replace missing limbs with those that communicate bidirectionally with the brain. These advances have been fairly successful, but still most patients experience persistent problems like phantom limb pain, and others discontinue prostheses instead of learning to use them daily. These problematic patient outcomes may be due in part to the lack of consensus among basic and clinical researchers regarding the plasticity mechanisms that occur in the brain after amputation injuries. Here we review results from clinical and animal model studies to bridge this clinical-basic science gap.
Collapse
Affiliation(s)
- Tawnee Sparling
- Department of Physical Medicine and Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, Maryland 20814
| | - Laxmi Iyer
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland 20817
| | - Paul Pasquina
- Department of Physical Medicine and Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, Maryland 20814
| | - Emily Petrus
- Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, Maryland 20814
| |
Collapse
|
13
|
Anastasiev A, Kadone H, Marushima A, Watanabe H, Zaboronok A, Watanabe S, Matsumura A, Suzuki K, Matsumaru Y, Ishikawa E. Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding. Bioengineering (Basel) 2023; 10:866. [PMID: 37508895 PMCID: PMC10376258 DOI: 10.3390/bioengineering10070866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10-17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing.
Collapse
Affiliation(s)
- Alexey Anastasiev
- Department of Neurosurgery, Graduate School of Comprehensive Human Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Hideki Kadone
- Center for Cybernics Research, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Aiki Marushima
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| | - Hiroki Watanabe
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| | - Alexander Zaboronok
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| | - Shinya Watanabe
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| | - Akira Matsumura
- Ibaraki Prefectural University of Health Sciences, 4669-2 Amicho, Inashiki 300-0394, Ibaraki, Japan
| | - Kenji Suzuki
- Center for Cybernics Research, Artificial Intelligence Laboratory, Faculty of Engineering Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
| | - Yuji Matsumaru
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| | - Eiichi Ishikawa
- Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba 305-8575, Ibaraki, Japan
| |
Collapse
|
14
|
Lopez-Castellanos JM, Ramon JL, Pomares J, Garcia GJ, Ubeda A. Multisensory Evaluation of Muscle Activity and Human Manipulability during Upper Limb Motor Tasks. BIOSENSORS 2023; 13:697. [PMID: 37504097 PMCID: PMC10377320 DOI: 10.3390/bios13070697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/29/2023]
Abstract
In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.
Collapse
Affiliation(s)
- Jose M Lopez-Castellanos
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
- Department of Systems Engineering, National Autonomous University of Honduras, Tegucigalpa 11101, Honduras
| | - Jose L Ramon
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Jorge Pomares
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Gabriel J Garcia
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Andres Ubeda
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| |
Collapse
|
15
|
Liang Z, Wang X, Guo J, Ye Y, Zhang H, Xie L, Tao K, Zeng W, Yin E, Ji B. A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection. MICROMACHINES 2023; 14:mi14051085. [PMID: 37241708 DOI: 10.3390/mi14051085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023]
Abstract
The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human-computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human-computer interaction and physiological state monitoring.
Collapse
Affiliation(s)
- Zekai Liang
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Innovation Center NPU Chongqing, Northwestern Polytechnical University, Chongqing 400000, China
| | - Xuanqi Wang
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Innovation Center NPU Chongqing, Northwestern Polytechnical University, Chongqing 400000, China
| | - Jun Guo
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Innovation Center NPU Chongqing, Northwestern Polytechnical University, Chongqing 400000, China
| | - Yuanming Ye
- Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi'an 710072, China
| | - Haoyang Zhang
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Liang Xie
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Kai Tao
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wen Zeng
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Bowen Ji
- Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Innovation Center NPU Chongqing, Northwestern Polytechnical University, Chongqing 400000, China
| |
Collapse
|
16
|
Gomez-Correa M, Ballesteros M, Salgado I, Cruz-Ortiz D. Forearm sEMG data from young healthy humans during the execution of hand movements. Sci Data 2023; 10:310. [PMID: 37210582 DOI: 10.1038/s41597-023-02223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 05/10/2023] [Indexed: 05/22/2023] Open
Abstract
This work provides a complete dataset containing surface electromyography (sEMG) signals acquired from the forearm with a sampling frequency of 1000 Hz. The dataset is named WyoFlex sEMG Hand Gesture and recorded the data of 28 participants between 18 and 37 years old without neuromuscular diseases or cardiovascular problems. The test protocol consisted of sEMG signals acquisition corresponding to ten wrist and grasping movements (extension, flexion, ulnar deviation, radial deviation, hook grip, power grip, spherical grip, precision grip, lateral grip, and pinch grip), considering three repetitions for each gesture. Also, the dataset contains general information such as anthropometric measures of the upper limb, gender, age, laterally of the person, and physical condition. Likewise, the implemented acquisition system consists of a portable armband with four sEMG channels distributed equidistantly for each forearm. The database could be used for the recognition of hand gestures, evaluation of the evolution of patients in rehabilitation processes, control of upper limb orthoses or prostheses, and biomechanical analysis of the forearm.
Collapse
Affiliation(s)
- Manuela Gomez-Correa
- Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Z.C, 07700, Mexico City, Mexico
| | - Mariana Ballesteros
- Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Z.C, 07700, Mexico City, Mexico
- Medical Robotics and Biosignals Laboratory, Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Z.C, 07340, Mexico City, Mexico
| | - Ivan Salgado
- Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Z.C, 07700, Mexico City, Mexico
| | - David Cruz-Ortiz
- Medical Robotics and Biosignals Laboratory, Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Z.C, 07340, Mexico City, Mexico.
| |
Collapse
|
17
|
Stawiarska E, Stawiarski M. Assessment of Patient Treatment and Rehabilitation Processes Using Electromyography Signals and Selected Industry 4.0 Solutions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3754. [PMID: 36834449 PMCID: PMC9965708 DOI: 10.3390/ijerph20043754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
Funding treatment and rehabilitation processes for patients with musculoskeletal conditions is an important part of public health insurance in European Union countries. By 2030, these processes will be planned in national health strategies (sequential process activities will be identified, care packages will be defined, service standards will be described, roles in the implementation of activities will be distinguished). Today, in many countries of the world (including the EU countries), these processes tend not to be very effective and to be expensive for both patients and insurance companies. This article aims to raise awareness of the need for process re-engineering and describes possible tools for assessing patient treatment and rehabilitation processes (using electromyographic signals-EMG and selected Industry 4.0 solutions). This article presents the research methodology prepared for the purpose of process evaluation. The use of this methodology will confirm the hypothesis that the use of EMG signals and selected Industry 4.0 solutions will improve the effectiveness and efficiency of treatment and rehabilitation processes for patients with musculoskeletal injuries.
Collapse
Affiliation(s)
- Ewa Stawiarska
- Faculty of Organisation and Management, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Maciej Stawiarski
- Faculty of Medical Biotechnology, Medical University of Lodz, 90-419 Łódz, Poland
| |
Collapse
|
18
|
Nazari V, Zheng YP. Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:1885. [PMID: 36850483 PMCID: PMC9959820 DOI: 10.3390/s23041885] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence", and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.
Collapse
Affiliation(s)
- Vaheh Nazari
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yong-Ping Zheng
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| |
Collapse
|
19
|
Asghar A, Khan SJ, Azim F, Shakeel CS, Hussain A, Niazi IK. Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method. Proc Inst Mech Eng H 2023; 237:74-90. [PMID: 36458327 DOI: 10.1177/09544119221139593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.
Collapse
Affiliation(s)
- Ali Asghar
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.,Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Saad Jawaid Khan
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Fahad Azim
- Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Choudhary Sobhan Shakeel
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Amatullah Hussain
- College of Rehabilitation Sciences, Ziauddin University, Karachi, Pakistan
| | - Imran Khan Niazi
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand.,Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland, New Zealand.,Centre for Sensory-Motor Interactions, Department of Health, Science and Technology, Aalborg University, Aalborg, Denmark
| |
Collapse
|
20
|
Aviles M, Sánchez-Reyes LM, Fuentes-Aguilar RQ, Toledo-Pérez DC, Rodríguez-Reséndiz J. A Novel Methodology for Classifying EMG Movements Based on SVM and Genetic Algorithms. MICROMACHINES 2022; 13:mi13122108. [PMID: 36557408 PMCID: PMC9781991 DOI: 10.3390/mi13122108] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 05/28/2023]
Abstract
Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non-stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.
Collapse
Affiliation(s)
- Marcos Aviles
- Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
| | | | - Rita Q. Fuentes-Aguilar
- Tecnológico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Guadalajara 45201, Mexico
| | | | | |
Collapse
|
21
|
Wang J, Cao D, Li Y, Wang J, Wu Y. Multi-user motion recognition using sEMG via discriminative canonical correlation analysis and adaptive dimensionality reduction. Front Neurorobot 2022; 16:997134. [PMID: 36386392 PMCID: PMC9650084 DOI: 10.3389/fnbot.2022.997134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/06/2022] [Indexed: 03/23/2024] Open
Abstract
The inability of new users to adapt quickly to the surface electromyography (sEMG) interface has greatly hindered the development of sEMG in the field of rehabilitation. This is due mainly to the large differences in sEMG signals produced by muscles when different people perform the same motion. To address this issue, a multi-user sEMG framework is proposed, using discriminative canonical correlation analysis and adaptive dimensionality reduction (ADR). The interface projects the feature sets for training users and new users into a low-dimensional uniform style space, overcoming the problem of individual differences in sEMG. The ADR method removes the redundant information in sEMG features and improves the accuracy of system motion recognition. The presented framework was validated on eight subjects with intact limbs, with an average recognition accuracy of 92.23% in 12 categories of upper-limb movements. In rehabilitation laboratory experiments, the average recognition rate reached 90.52%. The experimental results suggest that the framework offers a good solution to enable new rehabilitation users to adapt quickly to the sEMG interface.
Collapse
Affiliation(s)
| | - Dianguo Cao
- School of Engineering, Qufu Normal University, Rizhao, China
| | | | | | | |
Collapse
|
22
|
Caillet AH, Phillips ATM, Farina D, Modenese L. Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling. PLoS Comput Biol 2022; 18:e1010556. [PMID: 36174126 PMCID: PMC9553065 DOI: 10.1371/journal.pcbi.1010556] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 10/11/2022] [Accepted: 09/08/2022] [Indexed: 11/18/2022] Open
Abstract
Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.
Collapse
Affiliation(s)
- Arnault H. Caillet
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
| | - Andrew T. M. Phillips
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
| | - Dario Farina
- Department of Bioengineering, Imperial College London, United Kingdom
| | - Luca Modenese
- Department of Civil and Environmental Engineering, Imperial College London, United Kingdom
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| |
Collapse
|
23
|
Cao J, Yang X, Rao J, Mitriashkin A, Fan X, Chen R, Cheng H, Wang X, Goh J, Leo HL, Ouyang J. Stretchable and Self-Adhesive PEDOT:PSS Blend with High Sweat Tolerance as Conformal Biopotential Dry Electrodes. ACS APPLIED MATERIALS & INTERFACES 2022; 14:39159-39171. [PMID: 35973944 DOI: 10.1021/acsami.2c11921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dry epidermal electrodes that can always form conformal contact with skin can be used for continuous long-term biopotential monitoring, which can provide vital information for disease diagnosis and rehabilitation. But, this application has been limited by the poor contact of dry electrodes on wet skin. Herein, we report a biocompatible fully organic dry electrode that can form conformal contact with both dry and wet skin even during physical movement. The dry electrodes are prepared by drop casting an aqueous solution consisting of poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS), poly(vinyl alcohol) (PVA), tannic acid (TA), and ethylene glycol (EG). The electrodes can exhibit a conductivity of 122 S cm-1 and a mechanical stretchability of 54%. Moreover, they are self-adhesive to not only dry skin but also wet skin. As a result, they can exhibit a lower contact impedance to skin than commercial Ag/AgCl gel electrodes on both dry and sweat skins. They can be used as dry epidermal electrodes to accurately detect biopotential signals including electrocardiogram (ECG) and electromyogram (EMG) on both dry and wet skins for the users at rest or during physical movement. This is the first time to demonstrate dry epidermal electrodes self-adhesive to wet skin for accurate biopotential detection.
Collapse
Affiliation(s)
- Jian Cao
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
| | - Xingyi Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117574
| | - Jiancheng Rao
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
| | - Aleksandr Mitriashkin
- Biomedical Engineering Department, College of Design and Engineering, National University of Singapore, Singapore 117574
| | - Xing Fan
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
| | - Rui Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
| | - Hanlin Cheng
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
| | - Xinchao Wang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117574
| | - James Goh
- Biomedical Engineering Department, College of Design and Engineering, National University of Singapore, Singapore 117574
| | - Hwa Liang Leo
- Biomedical Engineering Department, College of Design and Engineering, National University of Singapore, Singapore 117574
| | - Jianyong Ouyang
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117574
- NUS Research Institute, No. 16 South Huashan Road, Liangjiang New Area, Chongqing 119077, China
| |
Collapse
|
24
|
Dwivedi A, Groll H, Beckerle P. A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding. SENSORS (BASEL, SWITZERLAND) 2022; 22:6319. [PMID: 36080778 PMCID: PMC9460678 DOI: 10.3390/s22176319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/02/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle-machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user's intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.
Collapse
Affiliation(s)
- Anany Dwivedi
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Helen Groll
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Philipp Beckerle
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| |
Collapse
|
25
|
Sun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of Surface Electromyography in Exercise Fatigue: A Review. Front Syst Neurosci 2022; 16:893275. [PMID: 36032326 PMCID: PMC9406287 DOI: 10.3389/fnsys.2022.893275] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.
Collapse
|
26
|
Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion. MICROMACHINES 2022; 13:mi13081205. [PMID: 36014127 PMCID: PMC9416603 DOI: 10.3390/mi13081205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/24/2022] [Accepted: 07/26/2022] [Indexed: 11/17/2022]
Abstract
An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors’ data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton’s ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.
Collapse
|
27
|
Vijayvargiya A, Singh B, Kumar R, Tavares JMRS. Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview. Biomed Eng Lett 2022; 12:343-358. [DOI: 10.1007/s13534-022-00236-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 12/16/2022] Open
|
28
|
Panio A, Cava C, D’Antona S, Bertoli G, Porro D. Diagnostic Circulating miRNAs in Sporadic Amyotrophic Lateral Sclerosis. Front Med (Lausanne) 2022; 9:861960. [PMID: 35602517 PMCID: PMC9121628 DOI: 10.3389/fmed.2022.861960] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the neurodegeneration of motoneurons. About 10% of ALS is hereditary and involves mutation in 25 different genes, while 90% of the cases are sporadic forms of ALS (sALS). The diagnosis of ALS includes the detection of early symptoms and, as disease progresses, muscle twitching and then atrophy spreads from hands to other parts of the body. The disease causes high disability and has a high mortality rate; moreover, the therapeutic approaches for the pathology are not effective. miRNAs are small non-coding RNAs, whose activity has a major impact on the expression levels of coding mRNA. The literature identifies several miRNAs with diagnostic abilities on sALS, but a unique diagnostic profile is not defined. As miRNAs could be secreted, the identification of specific blood miRNAs with diagnostic ability for sALS could be helpful in the identification of the patients. In the view of personalized medicine, we performed a meta-analysis of the literature in order to select specific circulating miRNAs with diagnostic properties and, by bioinformatics approaches, we identified a panel of 10 miRNAs (miR-193b, miR-3911, miR-139-5p, miR-193b-1, miR-338-5p, miR-3911-1, miR-455-3p, miR-4687-5p, miR-4745-5p, and miR-4763-3p) able to classify sALS patients by blood analysis. Among them, the analysis of expression levels of the couple of blood miR-193b/miR-4745-5p could be translated in clinical practice for the diagnosis of sALS.
Collapse
|
29
|
Engdahl SM, Acuña SA, King EL, Bashatah A, Sikdar S. First Demonstration of Functional Task Performance Using a Sonomyographic Prosthesis: A Case Study. Front Bioeng Biotechnol 2022; 10:876836. [PMID: 35600893 PMCID: PMC9114778 DOI: 10.3389/fbioe.2022.876836] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/29/2022] [Indexed: 11/28/2022] Open
Abstract
Ultrasound-based sensing of muscle deformation, known as sonomyography, has shown promise for accurately classifying the intended hand grasps of individuals with upper limb loss in offline settings. Building upon this previous work, we present the first demonstration of real-time prosthetic hand control using sonomyography to perform functional tasks. An individual with congenital bilateral limb absence was fitted with sockets containing a low-profile ultrasound transducer placed over forearm muscle tissue in the residual limbs. A classifier was trained using linear discriminant analysis to recognize ultrasound images of muscle contractions for three discrete hand configurations (rest, tripod grasp, index finger point) under a variety of arm positions designed to cover the reachable workspace. A prosthetic hand mounted to the socket was then controlled using this classifier. Using this real-time sonomyographic control, the participant was able to complete three functional tasks that required selecting different hand grasps in order to grasp and move one-inch wooden blocks over a broad range of arm positions. Additionally, these tests were successfully repeated without retraining the classifier across 3 hours of prosthesis use and following simulated donning and doffing of the socket. This study supports the feasibility of using sonomyography to control upper limb prostheses in real-world applications.
Collapse
Affiliation(s)
- Susannah M. Engdahl
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, United States
| | - Samuel A. Acuña
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, United States
| | - Erica L. King
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, United States
| | - Ahmed Bashatah
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, United States
| | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA, United States
- *Correspondence: Siddhartha Sikdar,
| |
Collapse
|
30
|
Cho S, Chang T, Yu T, Lee CH. Smart Electronic Textiles for Wearable Sensing and Display. BIOSENSORS 2022; 12:bios12040222. [PMID: 35448282 PMCID: PMC9029731 DOI: 10.3390/bios12040222] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/04/2022] [Accepted: 04/06/2022] [Indexed: 05/13/2023]
Abstract
Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice.
Collapse
Affiliation(s)
- Seungse Cho
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Taehoo Chang
- School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Tianhao Yu
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;
| | - Chi Hwan Lee
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA;
- School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA;
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;
- Center for Implantable Devices, Purdue University, West Lafayette, IN 47907, USA
- Correspondence:
| |
Collapse
|
31
|
Kiper P, Rimini D, Falla D, Baba A, Rutkowski S, Maistrello L, Turolla A. Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors? SENSORS 2021; 21:s21248175. [PMID: 34960269 PMCID: PMC8708806 DOI: 10.3390/s21248175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/03/2021] [Accepted: 12/05/2021] [Indexed: 11/16/2022]
Abstract
It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.
Collapse
Affiliation(s)
- Pawel Kiper
- Physical Medicine and Rehabilitation Unit, Azienda ULSS 3 Serenissima, 30126 Venice, Italy
- Correspondence: (P.K.); (A.T.)
| | - Daniele Rimini
- Medical Physics Department-Clinical Engineering, Salford Care Organisation, Salford M6 8HD, UK;
| | - Deborah Falla
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Alfonc Baba
- Rehabilitation Unit, Azienda Ospedale Università Padova, 35128 Padua, Italy;
| | - Sebastian Rutkowski
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758 Opole, Poland;
| | - Lorenza Maistrello
- Laboratory of Neurorehabilitation Technologies, San Camillo IRCCS, 30126 Venice, Italy;
| | - Andrea Turolla
- Laboratory of Neurorehabilitation Technologies, San Camillo IRCCS, 30126 Venice, Italy;
- Correspondence: (P.K.); (A.T.)
| |
Collapse
|
32
|
The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction. SENSORS 2021; 21:s21186291. [PMID: 34577498 PMCID: PMC8473357 DOI: 10.3390/s21186291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/17/2022]
Abstract
Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.
Collapse
|
33
|
Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals. SENSORS 2021; 21:s21186064. [PMID: 34577270 PMCID: PMC8469046 DOI: 10.3390/s21186064] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
Collapse
|
34
|
Kowalski E, Catelli DS, Lamontagne M. Comparing the Accuracy of Visual and Computerized Onset Detection Methods on Simulated Electromyography Signals with Varying Signal-to-Noise Ratios. J Funct Morphol Kinesiol 2021; 6:jfmk6030070. [PMID: 34449669 PMCID: PMC8395734 DOI: 10.3390/jfmk6030070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/09/2021] [Accepted: 08/18/2021] [Indexed: 11/16/2022] Open
Abstract
Electromyography (EMG) onsets determined by computerized detection methods have been compared against the onsets selected by experts through visual inspection. However, with this type of approach, the true onset remains unknown, making it impossible to determine if computerized detection methods are better than visual detection (VD) as they can only be as good as what the experts select. The use of simulated signals allows for all aspects of the signal to be precisely controlled, including the onset and the signal-to-noise ratio (SNR). This study compared three onset detection methods: approximated generalized likelihood ratio, double threshold (DT), and VD determined by eight trained individuals. The selected onset was compared against the true onset in simulated signals which varied in the SNR from 5 to 40 dB. For signals with 5 dB SNR, the VD method was significantly better, but for SNRs of 20 dB or greater, no differences existed between the VD and DT methods. The DT method is recommended as it can improve objectivity and reduce time of analysis when determining EMG onsets. Even for the best-quality signals (SNR of 40 dB), all the detection methods were off by 15-30 ms from the true onset and became progressively more inaccurate as the SNR decreased. Therefore, although all the detection methods provided similar results, they can be off by 50-80 ms from the true onset as the SNR decreases to 10 dB. Caution must be used when interpreting EMG onsets, especially on signals where the SNR is low or not reported at all.
Collapse
Affiliation(s)
- Erik Kowalski
- Human Movement Biomechanics Laboratory, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (E.K.); (D.S.C.)
| | - Danilo S. Catelli
- Human Movement Biomechanics Laboratory, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (E.K.); (D.S.C.)
| | - Mario Lamontagne
- Human Movement Biomechanics Laboratory, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (E.K.); (D.S.C.)
- Division of Orthopeadic Surgery, The Ottawa Hospital, Ottawa, ON K1H 8L6, Canada
- Correspondence:
| |
Collapse
|
35
|
Fang Z, Mahapatra RP, Selvaraj P. Dynamic data processing system for sports training system using internet of things. Technol Health Care 2021; 29:1305-1318. [PMID: 34092678 DOI: 10.3233/thc-213008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The Internet of Things (IoT) has recently become a prevalent technological culture in the sports training system. Although numerous technologies have grown in the sports training system domain, IoT plays a substantial role in its optimized health data processing framework for athletes during workouts. OBJECTIVE In this paper, a Dynamic data processing system (DDPS) has been suggested with IoT assistance to explore the conventional design architecture for sports training tracking. METHOD To track and estimate sportspersons physical activity in day-to-day living, a new paradigm has been combined with wearable IoT devices for efficient data processing during physical workouts. Uninterrupted observation and review of different sportspersons condition and operations by DDPS helps to assess the sensed data to analyze the sportspersons health condition. Additionally, Deep Neural Network (DNN) has been presented to extract important sports activity features. RESULTS The numerical results show that the suggested DDPS method enhances the accuracy of 94.3%, an efficiency ratio of 98.2, less delay of 24.6%, error range 28.8%, and energy utilization of 31.2% compared to other existing methods.
Collapse
Affiliation(s)
- Zhi Fang
- Department of Physical Education, Southeast University, Nanjing, Jiangsu, China
| | - Rajendra Prasad Mahapatra
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh, India
| | - P Selvaraj
- Department of Information Technology, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India
| |
Collapse
|
36
|
Coker J, Chen H, Schall MC, Gallagher S, Zabala M. EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee. SENSORS (BASEL, SWITZERLAND) 2021; 21:3622. [PMID: 34067477 PMCID: PMC8197024 DOI: 10.3390/s21113622] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/18/2021] [Accepted: 05/20/2021] [Indexed: 12/12/2022]
Abstract
Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.
Collapse
Affiliation(s)
- Jordan Coker
- Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA; (J.C.); (H.C.)
| | - Howard Chen
- Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA; (J.C.); (H.C.)
| | - Mark C. Schall
- Department of Industrial Engineering, Auburn University, Auburn, AL 36849, USA; (M.C.S.J.); (S.G.)
| | - Sean Gallagher
- Department of Industrial Engineering, Auburn University, Auburn, AL 36849, USA; (M.C.S.J.); (S.G.)
| | - Michael Zabala
- Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA; (J.C.); (H.C.)
| |
Collapse
|
37
|
Li W, Shi P, Yu H. Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future. Front Neurosci 2021; 15:621885. [PMID: 33981195 PMCID: PMC8107289 DOI: 10.3389/fnins.2021.621885] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/23/2021] [Indexed: 01/09/2023] Open
Abstract
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.
Collapse
Affiliation(s)
- Wei Li
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Ping Shi
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Hongliu Yu
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| |
Collapse
|
38
|
Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning. MACHINES 2021. [DOI: 10.3390/machines9030056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.
Collapse
|
39
|
Gohel V, Mehendale N. Review on electromyography signal acquisition and processing. Biophys Rev 2020; 12:10.1007/s12551-020-00770-w. [PMID: 33169207 PMCID: PMC7755956 DOI: 10.1007/s12551-020-00770-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 10/26/2020] [Indexed: 12/01/2022] Open
Abstract
Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.
Collapse
Affiliation(s)
- Vidhi Gohel
- K. J. Somaiya College of Engineering, Mumbai, India
| | | |
Collapse
|