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Al-Hammouri S, Barioul R, Lweesy K, Ibbini M, Kanoun O. A wrapper framework for feature selection and ELM weights optimization for FMG-based sign recognition. Comput Biol Med 2024; 179:108817. [PMID: 39004049 DOI: 10.1016/j.compbiomed.2024.108817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/28/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024]
Abstract
Force myography (FMG) is increasingly gaining importance in gesture recognition because of it's ability to achieve high classification accuracy without having a direct contact with the skin. In this study, we investigate the performance of a bracelet with only six commercial force sensitive resistors (FSR) sensors for classifying many hand gestures representing all letters and numbers from 0 to 10 in the American sign language. For this, we introduce an optimized feature selection in combination with the Extreme Learning Machine (ELM) as a classifier by investigating three swarm intelligence algorithms, which are the binary grey wolf optimizer (BGWO), binary grasshopper optimizer (BGOA), and binary hybrid grey wolf particle swarm optimizer (BGWOPSO), which is used as an optimization method for ELM for the first time in this study. The findings reveal that the BGWOPSO, in which PSO supports the GWO optimizer by controlling its exploration and exploitation using inertia constant to improve the convergence speed to reach the best global optima, outperformed the other investigated algorithms. In addition, the results show that optimizing ELM with BGWOPSO for feature selection can efficiently improve the performance of ELM to enhance the classification accuracy from 32% to 69.84% for classifying 37 gestures collected from multiple volunteers and using only a band with 6 FSR sensors.
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Affiliation(s)
- S Al-Hammouri
- Biomedical Engineering Department, College of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
| | - R Barioul
- Professorship of Measurement and Sensor Technology, Technische Universitaet Chemnitz, Chemnitz, 09126, Germany.
| | - K Lweesy
- Biomedical Engineering Department, College of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan; Electrical, Computer, and Biomedical Engineering Department, College of Engineering, Abu Dhabi University, P.O. Box 59911, Abu Dhabi, United Arab Emirates.
| | - M Ibbini
- Biomedical Engineering Department, College of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
| | - O Kanoun
- Professorship of Measurement and Sensor Technology, Technische Universitaet Chemnitz, Chemnitz, 09126, Germany.
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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: 14] [Impact Index Per Article: 4.7] [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.
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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
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Prakash A, Sahi AK, Sharma N, Sharma S. Force myography controlled multifunctional hand prosthesis for upper-limb amputees. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102122] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Grushko S, Spurný T, Černý M. Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4883. [PMID: 32872291 PMCID: PMC7506660 DOI: 10.3390/s20174883] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 02/07/2023]
Abstract
The loss of a hand can significantly affect one's work and social life. For many patients, an artificial limb can improve their mobility and ability to manage everyday activities, as well as provide the means to remain independent. This paper provides an extensive review of available biosensing methods to implement the control system for transradial prostheses based on the measured activity in remnant muscles. Covered techniques include electromyography, magnetomyography, electrical impedance tomography, capacitance sensing, near-infrared spectroscopy, sonomyography, optical myography, force myography, phonomyography, myokinetic control, and modern approaches to cineplasty. The paper also covers combinations of these approaches, which, in many cases, achieve better accuracy while mitigating the weaknesses of individual methods. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each technique along with their relationship with proprioceptive feedback, which is an important factor for intuitive control over the prosthetic device, especially for high dexterity prosthetic hands.
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Affiliation(s)
- Stefan Grushko
- Department of Robotics, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; (T.S.); (M.Č.)
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Nissler C, Nowak M, Connan M, Büttner S, Vogel J, Kossyk I, Márton ZC, Castellini C. VITA-an everyday virtual reality setup for prosthetics and upper-limb rehabilitation. J Neural Eng 2020; 16:026039. [PMID: 30864550 DOI: 10.1088/1741-2552/aaf35f] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Currently, there are some 95 000 people in Europe suffering from upper-limb impairment. Rehabilitation should be undertaken right after the impairment occurs and should be regularly performed thereafter. Moreover, the rehabilitation process should be tailored specifically to both patient and impairment. APPROACH To address this, we have developed a low-cost solution that integrates an off-the-shelf virtual reality (VR) setup with our in-house developed arm/hand intent detection system. The resulting system, called VITA, enables an upper-limb disabled person to interact in a virtual world as if her impaired limb were still functional. VITA provides two specific features that we deem essential: proportionality of force control and interactivity between the user and the intent detection core. The usage of relatively cheap commercial components enables VITA to be used in rehabilitation centers, hospitals, or even at home. The applications of VITA range from rehabilitation of patients with musculodegenerative conditions (e.g. ALS), to treating phantom-limb pain of people with limb-loss and prosthetic training. MAIN RESULTS We present a multifunctional system for upper-limb rehabilitation in VR. We tested the system using a VR implementation of a standard hand assessment tool, the Box and Block test and performed a user study on this standard test with both intact subjects and a prosthetic user. Furthermore, we present additional applications, showing the versatility of the system. SIGNIFICANCE The VITA system shows the applicability of a combination of our experience in intent detection with state-of-the art VR system for rehabilitation purposes. With VITA, we have an easily adaptable experimental tool available, which allows us to quickly and realistically simulate all kind of real-world problems and rehabilitation exercises for upper-limb impaired patients. Additionally, other scenarios such as prostheses simulations and control modes can be quickly implemented and tested.
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Affiliation(s)
- Christian Nissler
- Institute of Robotics and Mechatronics, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
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Wu YT, Gomes MK, da Silva WH, Lazari PM, Fujiwara E. Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures. Biomed Eng Comput Biol 2020; 11:1179597220912825. [PMID: 32269474 PMCID: PMC7093689 DOI: 10.1177/1179597220912825] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 02/18/2020] [Indexed: 11/16/2022] Open
Abstract
Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.
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Affiliation(s)
- Yu Tzu Wu
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Matheus K Gomes
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Willian Ha da Silva
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Pedro M Lazari
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Eric Fujiwara
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
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Vujaklija I, Roche AD, Hasenoehrl T, Sturma A, Amsuess S, Farina D, Aszmann OC. Translating Research on Myoelectric Control into Clinics-Are the Performance Assessment Methods Adequate? Front Neurorobot 2017; 11:7. [PMID: 28261085 PMCID: PMC5306214 DOI: 10.3389/fnbot.2017.00007] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2016] [Accepted: 02/01/2017] [Indexed: 11/23/2022] Open
Abstract
Missing an upper limb dramatically impairs daily-life activities. Efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective article, we suggest that one relevant factor determining the relatively small clinical impact of myocontrol algorithms for upper limb prostheses is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively support this argument with representative data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reaching relatively high classification accuracy offline, their clinical scores varied greatly and were not strongly predicted by classification accuracy. We therefore support the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.
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Affiliation(s)
- Ivan Vujaklija
- Clinic for Trauma Surgery, Orthopaedic Surgery and Plastic Surgery, Research Department for Neurorehabilitation Systems, University Medical Centre GöttingenGoettingen, Germany; Department of Bioengineering, Imperial College LondonLondon, UK
| | - Aidan D Roche
- Christian Doppler Laboratory for Restoration of Extremity Function, Medical University of Vienna, Vienna Austria
| | - Timothy Hasenoehrl
- Department of Physical Medicine, Rehabilitation and Occupational Medicine, Medical University of Vienna, Vienna Austria
| | - Agnes Sturma
- Christian Doppler Laboratory for Restoration of Extremity Function, Medical University of Vienna, ViennaAustria; Master Degree Program "Health Assisting Engineering", University of Applied Sciences FH Campus Wien, ViennaAustria
| | | | - Dario Farina
- Department of Bioengineering, Imperial College London London, UK
| | - Oskar C Aszmann
- Christian Doppler Laboratory for Restoration of Extremity Function, Medical University of Vienna, ViennaAustria; Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, ViennaAustria
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