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Xiao Z, Du Z, Yan Z, Huang T, Xu D, Huang Q, Han B. Channel Selection for Gesture Recognition Using Force Myography: A Universal Model for Gesture Measurement Points. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2016-2026. [PMID: 38771682 DOI: 10.1109/tnsre.2024.3403941] [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: 05/23/2024]
Abstract
Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.
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Skoraczynski DJ, Chen C. Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:1379. [PMID: 38474915 DOI: 10.3390/s24051379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 03/14/2024]
Abstract
This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.
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Affiliation(s)
- Dariusz J Skoraczynski
- Laboratory of Motion Generation and Analysis (LMGA), Monash University, Clayton, VIC 3800, Australia
| | - Chao Chen
- Laboratory of Motion Generation and Analysis (LMGA), Monash University, Clayton, VIC 3800, Australia
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3
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Ovadia D, Segal A, Rabin N. Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform. Sci Rep 2024; 14:4134. [PMID: 38374342 PMCID: PMC10876538 DOI: 10.1038/s41598-024-54677-7] [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: 09/29/2023] [Accepted: 02/15/2024] [Indexed: 02/21/2024] Open
Abstract
Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.
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Affiliation(s)
- Daniel Ovadia
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Alex Segal
- Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel.
| | - Neta Rabin
- Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel
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Wang J, Meng H. Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2023. [DOI: 10.4018/ijdst.317941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
During the process of daily training or competition, athletes may suffer the situation that the load exceeds the body's bearing capacity, which makes the body's physiological function temporarily decline. It is one of the characteristics of sports fatigue. Continuous sports fatigue may incur permanent damage to the athletes if they cannot timely get enough rest to recover. In order to solve this issue and improve the quality of athlete's daily training, this paper establish a fatigue monitoring system by using multi-source sensors. First, the sEMG signals of athlete are collected by multi-source sensors which are installed in a wearable device. Second, the collected sEMG signals are segmented by using fixed window to be converted as Mel-frequency cepstral coefficients (MFCCs). Third, the MFCC features are used learn a Gaussian processing model which is used to monitor future muscle fatigue status. The experiments show that the proposed system can recognize more than 90% muscle fatigue states.
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Affiliation(s)
| | - Huan Meng
- Mudanjiang Medical University, China
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Wang H, Zuo S, Cerezo-Sánchez M, Arekhloo NG, Nazarpour K, Heidari H. Wearable super-resolution muscle-machine interfacing. Front Neurosci 2022; 16:1020546. [PMID: 36466163 PMCID: PMC9714306 DOI: 10.3389/fnins.2022.1020546] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/21/2022] [Indexed: 09/19/2023] Open
Abstract
Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.
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Affiliation(s)
- Huxi Wang
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Siming Zuo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - María Cerezo-Sánchez
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Negin Ghahremani Arekhloo
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
| | - Kianoush Nazarpour
- Neuranics Ltd., Glasgow, United Kingdom
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom
- Neuranics Ltd., Glasgow, United Kingdom
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Nsugbe E, Ser HL, Ong HF, Ming LC, Goh KW, Goh BH, Lee WL. On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines. Diagnostics (Basel) 2022; 12:diagnostics12092099. [PMID: 36140500 PMCID: PMC9497845 DOI: 10.3390/diagnostics12092099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.
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Affiliation(s)
- Ejay Nsugbe
- Nsugbe Research Labs, Swindon SN1 3LG, UK
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
| | - Hooi-Leng Ser
- Department of Biological Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway 47500, Malaysia
| | - Huey-Fang Ong
- School of Information Technology, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Long Chiau Ming
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong BE-1410, Brunei
| | - Khang-Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
| | - Bey-Hing Goh
- Biofunctional Molecule Exploratory (BMEX) Research Group, School of Pharmacy, Monash University Malaysia, Subang Jaya 47500, Malaysia
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Wai-Leng Lee
- School of Science, Monash University Malaysia, Subang Jaya 47500, Malaysia
- Correspondence: (E.N.); (K.-W.G.); (W.-L.L.); Tel.: +603-551-46098 (W.-L.L.)
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Nsugbe E, Al‐Timemy AH. Shoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Ali H. Al‐Timemy
- Biomedical Engineering Department Al‐Khwarizmi College of Engineering University of Baghdad Baghdad Iraq
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Rosati G, Cisotto G, Sili D, Compagnucci L, De Giorgi C, Pavone EF, Paccagnella A, Betti V. Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition. Sci Rep 2021; 11:14938. [PMID: 34294822 PMCID: PMC8298403 DOI: 10.1038/s41598-021-94526-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/05/2021] [Indexed: 11/11/2022] Open
Abstract
The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users' needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93-95% for flexion and extension, respectively.
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Affiliation(s)
- Giulio Rosati
- Department of Information Engineering, University of Padova, via G. Gradenigo 6b, 35131, Padova, Italy.
| | - Giulia Cisotto
- Department of Information Engineering, University of Padova, via G. Gradenigo 6b, 35131, Padova, Italy
- NCNP, National Centre of Neurology and Psychiatry, Tokyo, Japan
- CNIT, the National, Inter-University Consortium for Telecommunications, Rome, Italy
| | - Daniele Sili
- Department of Psychology, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
- IRCCS Fondazione Santa Lucia, Via Ardeatina, 306/354, 00179, Rome, Italy
| | - Luca Compagnucci
- Department of Psychology, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
- IRCCS Fondazione Santa Lucia, Via Ardeatina, 306/354, 00179, Rome, Italy
| | - Chiara De Giorgi
- Department of Psychology, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
- IRCCS Fondazione Santa Lucia, Via Ardeatina, 306/354, 00179, Rome, Italy
| | | | - Alessandro Paccagnella
- Department of Information Engineering, University of Padova, via G. Gradenigo 6b, 35131, Padova, Italy
| | - Viviana Betti
- Department of Psychology, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
- IRCCS Fondazione Santa Lucia, Via Ardeatina, 306/354, 00179, Rome, Italy
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9
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Nsugbe E, William Samuel O, Asogbon MG, Li G. Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12039] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
| | - Oluwarotimi William Samuel
- Key Laboratory of Human‐Machine Intelligence‐Synergy Systems Chinese Academy of Sciences (CAS) Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Mojisola Grace Asogbon
- Key Laboratory of Human‐Machine Intelligence‐Synergy Systems Chinese Academy of Sciences (CAS) Shenzhen Institutes of Advanced Technology Shenzhen China
| | - Guanglin Li
- Key Laboratory of Human‐Machine Intelligence‐Synergy Systems Chinese Academy of Sciences (CAS) Shenzhen Institutes of Advanced Technology Shenzhen China
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