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Höhler C, Trigili E, Astarita D, Hermsdörfer J, Jahn K, Krewer C. The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a narrative and systematic review with a meta-analysis. Artif Organs 2024; 48:232-253. [PMID: 37548237 DOI: 10.1111/aor.14618] [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: 01/31/2023] [Revised: 06/30/2023] [Accepted: 07/17/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Paresis of the upper limb (UL) is the most frequent impairment after a stroke. Hybrid neuroprostheses, i.e., the combination of robots and electrical stimulation, have emerged as an option to treat these impairments. METHODS To give an overview of existing devices, their features, and how they are linked to clinical metrics, four different databases were systematically searched for studies on hybrid neuroprostheses for UL rehabilitation after stroke. The evidence on the efficacy of hybrid therapies was synthesized. RESULTS Seventy-three studies were identified, introducing 32 hybrid systems. Among the most recent devices (n = 20), most actively reinforce movement (3 passively) and are typical exoskeletons (3 end-effectors). If classified according to the International Classification of Functioning, Disability and Health, systems for proximal support are expected to affect body structures and functions, while the activity and participation level are targeted when applying Functional Electrical Stimulation distally plus the robotic component proximally. The meta-analysis reveals a significant positive effect on UL functions (p < 0.001), evident in a 7.8-point Mdiff between groups in the Fugl-Meyer assessment. This positive effect remains at the 3-month follow-up (Mdiff = 8.4, p < 0.001). CONCLUSIONS Hybrid neuroprostheses have a positive effect on UL recovery after stroke, with effects persisting at least three months after the intervention. Non-significant studies were those with the shortest intervention periods and the oldest patients. Improvements in UL functions are not only present in the subacute phase after stroke but also in long-term chronic stages. In addition to further technical development, more RCTs are needed to make assumptions about the determinants of successful therapy.
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Affiliation(s)
- Chiara Höhler
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
| | - Emilio Trigili
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Davide Astarita
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Joachim Hermsdörfer
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
| | - Klaus Jahn
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians University of Munich (LMU), Munich, Germany
| | - Carmen Krewer
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
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Al-Haddad LA, Alawee WH, Basem A. Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning. Comput Biol Med 2024; 169:107894. [PMID: 38154161 DOI: 10.1016/j.compbiomed.2023.107894] [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: 11/09/2023] [Revised: 12/04/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023]
Abstract
In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.
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Affiliation(s)
- Luttfi A Al-Haddad
- Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq.
| | - Wissam H Alawee
- Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq; Control and Systems Engineering Department, University of Technology- Iraq, Baghdad, Iraq
| | - Ali Basem
- Air Conditioning Engineering Department, Faculty of Engineering, Warith Al-Anbiyaa University, Iraq
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Chen X, Jiao Y, Zhang D, Wang Y, Wang X, Zang Y, Liang Z, Xie P. An Adaptive Spatial Filtering Method for Multi-Channel EMG Artifact Removal During Functional Electrical Stimulation With Time-Variant Parameters. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3597-3606. [PMID: 37682655 DOI: 10.1109/tnsre.2023.3311819] [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: 09/10/2023]
Abstract
Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts.
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Akmal M, Zubair S, Jochumsen M, Zia ur rehman M, Nlandu Kamavuako E, Irfan Abid M, Niazi IK. Scalable tensor factorization for recovering multiday missing intramuscular electromyography data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
To design a prosthetic hand which can classify movements based on the electromyography (EMG) signals, complete and good quality signals are essential. However, due to different reasons such as disconnection of electrodes or muscles fatigue the recorded EMG data can be incomplete, which degrades the classification of test movements. In this paper, we first acquire multiday intramuscular EMG (iEMG) signals (which are invasive) with higher Signal-to-Noise Ratio (SNR) compared to surface EMG (sEMG) signals; followed by application of matrix (non-negative matrix factorization – NMF) and tensor factorization methods (Canonical Polyadic Decomposition (CPD), Tucker decomposition (TD) & Canonical Polyadic-Weighted Optimization (CP-WOPT)) for recovering structured missing data i.e., chunks of missing samples in channels. Furthermore, we tested the scalability of NMF, CPD, TD and CP-WOPT by employing them on the large multiday (seven days) iEMG data where the size of missing data is increased from day 1 to day 7, and for each day a fixed percentage of missing data is introduced from 10% to worst case of 50%. Results show that CP-WOPT outperformed NMF, CPD and TD to recover large percentage of missing data in terms of Relative Mean Error (RME) even when 7 days of data is considered. CP-WOPT showed robustness even for the worse case even when 50% iEMG data is removed from day 1 to day 7 where it’s RME degraded slightly from 0.08 to 0.1.
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Affiliation(s)
- Muhammad Akmal
- Department of Electrical Engineering, Riphah International University, I-14 Islamabad, Pakistan
| | - Syed Zubair
- Deparment of Computer Science, University of Sialkot, Sialkot, Pakistan
| | - Mads Jochumsen
- Department of Health Science and Technology, SMI, Aalborg university, Aalborg, Denmark
| | - Muhammad Zia ur rehman
- Department of Biomedical Engineering, Riphah International University, I-14 Islamabad, Pakistan
| | | | - Muhammad Irfan Abid
- Department of Electrical Engineering, Riphah International University, Faisalabad, Pakistan
| | - Imran Khan Niazi
- Department of Health Science and Technology, SMI, Aalborg university, Aalborg, Denmark
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
- Health and Rehabilitation Research Institute, AUT University, Auckland, New Zealand
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Sattar NY, Kausar Z, Usama SA, Farooq U, Shah MF, Muhammad S, Khan R, Badran M. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees. SENSORS (BASEL, SWITZERLAND) 2022; 22:726. [PMID: 35161473 PMCID: PMC8837999 DOI: 10.3390/s22030726] [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: 11/26/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.
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Affiliation(s)
- Neelum Yousaf Sattar
- Department of Mechatronics and Biomedical Engineering, Air University, Main Campus, PAF Complex, Islamabad 44000, Pakistan; (Z.K.); (U.F.)
| | - Zareena Kausar
- Department of Mechatronics and Biomedical Engineering, Air University, Main Campus, PAF Complex, Islamabad 44000, Pakistan; (Z.K.); (U.F.)
| | - Syed Ali Usama
- Department of Mechatronics and Biomedical Engineering, Air University, Main Campus, PAF Complex, Islamabad 44000, Pakistan; (Z.K.); (U.F.)
| | - Umer Farooq
- Department of Mechatronics and Biomedical Engineering, Air University, Main Campus, PAF Complex, Islamabad 44000, Pakistan; (Z.K.); (U.F.)
| | - Muhammad Faizan Shah
- Department of Mechanical Engineering, Khwaja Fareed University of Engineering & IT, Rahim Yar Khan 64200, Pakistan;
| | - Shaheer Muhammad
- Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;
| | - Razaullah Khan
- Institute of Manufacturing, Engineering Management, University of Engineering and Applied Sciences, Swat, Mingora 19060, Pakistan;
| | - Mohamed Badran
- Department of Mechanical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt;
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