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Masengo G, Zhang X, Dong R, Alhassan AB, Hamza K, Mudaheranwa E. Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research. Front Neurorobot 2023; 16:913748. [PMID: 36714152 PMCID: PMC9875327 DOI: 10.3389/fnbot.2022.913748] [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: 04/06/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017-2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.
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Affiliation(s)
- Gilbert Masengo
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China,Department of Mechanical Engineering, Rwanda Polytechnic/Integrated Polytechnic Regional College (IPRC) Karongi, Kigali, Rwanda,*Correspondence: Gilbert Masengo ✉
| | - Xiaodong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Runlin Dong
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Ahmad B. Alhassan
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Khaled Hamza
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Emmanuel Mudaheranwa
- Department of Mechanical Engineering, Rwanda Polytechnic/Integrated Polytechnic Regional College (IPRC) Karongi, Kigali, Rwanda,Department of Engineering, Cardiff University, Cardiff, United Kingdom
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Khan MA, Saibene M, Das R, Brunner IC, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 2021; 18. [PMID: 34736239 DOI: 10.1088/1741-2552/ac36aa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities. APPROACH For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application. MAIN RESULTS In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained. SIGNIFICANCE This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.
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Affiliation(s)
- Muhammad Ahmed Khan
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 215, Lyngby, 2800, DENMARK
| | - Matteo Saibene
- Technical University of Denmark, Ørsteds Plads, Building 345C, Lyngby, 2800, DENMARK
| | - Rig Das
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 214, Lyngby, 2800, DENMARK
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Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng 2019; 16:011001. [DOI: 10.1088/1741-2552/aaf12e] [Citation(s) in RCA: 270] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Sullivan JL, Bhagat NA, Yozbatiran N, Paranjape R, Losey CG, Grossman RG, Contreras-Vidal JL, Francisco GE, O'Malley MK. Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. IEEE Int Conf Rehabil Robot 2018; 2017:122-127. [PMID: 28813805 DOI: 10.1109/icorr.2017.8009233] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control. These results are encouraging and suggest that increasing subject engagement during therapy through the addition of an intent-detecting BMI enhances the effectiveness of standard robotic rehabilitation.
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Rodríguez-Ugarte M, Iáñez E, Ortíz M, Azorín JM. Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent. Front Neuroinform 2017; 11:45. [PMID: 28744212 PMCID: PMC5504298 DOI: 10.3389/fninf.2017.00045] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 06/26/2017] [Indexed: 11/13/2022] Open
Abstract
The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients.
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Affiliation(s)
- Marisol Rodríguez-Ugarte
- Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of ElcheElche, Spain
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of ElcheElche, Spain
| | - Mario Ortíz
- Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of ElcheElche, Spain
| | - Jose M Azorín
- Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of ElcheElche, Spain
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EEG neural correlates of goal-directed movement intention. Neuroimage 2017; 149:129-140. [PMID: 28131888 PMCID: PMC5387183 DOI: 10.1016/j.neuroimage.2017.01.030] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 01/11/2017] [Accepted: 01/13/2017] [Indexed: 11/21/2022] Open
Abstract
Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users.
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Bhagat NA, Venkatakrishnan A, Abibullaev B, Artz EJ, Yozbatiran N, Blank AA, French J, Karmonik C, Grossman RG, O'Malley MK, Francisco GE, Contreras-Vidal JL. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors. Front Neurosci 2016; 10:122. [PMID: 27065787 PMCID: PMC4815250 DOI: 10.3389/fnins.2016.00122] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/13/2016] [Indexed: 11/13/2022] Open
Abstract
This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected -367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.
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Affiliation(s)
- Nikunj A Bhagat
- Non-Invasive Brain Machine Interface Systems Laboratory, Department of Electrical Engineering, University of Houston Houston, TX, USA
| | - Anusha Venkatakrishnan
- Non-Invasive Brain Machine Interface Systems Laboratory, Department of Electrical Engineering, University of Houston Houston, TX, USA
| | - Berdakh Abibullaev
- Non-Invasive Brain Machine Interface Systems Laboratory, Department of Electrical Engineering, University of Houston Houston, TX, USA
| | - Edward J Artz
- Mechatronics and Haptics Interfaces Laboratory, Department of Mechanical Engineering, Rice University Houston, TX, USA
| | - Nuray Yozbatiran
- NeuroRecovery Research Center at TIRR Memorial Hermann and University of Texas Health Sciences Center Houston, TX, USA
| | - Amy A Blank
- Mechatronics and Haptics Interfaces Laboratory, Department of Mechanical Engineering, Rice University Houston, TX, USA
| | - James French
- Mechatronics and Haptics Interfaces Laboratory, Department of Mechanical Engineering, Rice University Houston, TX, USA
| | | | | | - Marcia K O'Malley
- Mechatronics and Haptics Interfaces Laboratory, Department of Mechanical Engineering, Rice UniversityHouston, TX, USA; NeuroRecovery Research Center at TIRR Memorial Hermann and University of Texas Health Sciences CenterHouston, TX, USA
| | - Gerard E Francisco
- NeuroRecovery Research Center at TIRR Memorial Hermann and University of Texas Health Sciences Center Houston, TX, USA
| | - Jose L Contreras-Vidal
- Non-Invasive Brain Machine Interface Systems Laboratory, Department of Electrical Engineering, University of HoustonHouston, TX, USA; Houston Methodist Research InstituteHouston, TX, USA
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Xu R, Jiang N, Mrachacz-Kersting N, Dremstrup K, Farina D. Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation. Front Neurosci 2016; 9:527. [PMID: 26834551 PMCID: PMC4720791 DOI: 10.3389/fnins.2015.00527] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 12/30/2015] [Indexed: 11/23/2022] Open
Abstract
Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.
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Affiliation(s)
- Ren Xu
- Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical CenterGöttingen, Germany; Institute of Computer Science, Faculty of Mathematics and Computer Secience, Georg-August UniversityGöttingen, Germany
| | - Ning Jiang
- Department of Systems Design Engineering, University of Waterloo Waterloo, ON, Canada
| | - Natalie Mrachacz-Kersting
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University Aalborg, Denmark
| | - Kim Dremstrup
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University Aalborg, Denmark
| | - Dario Farina
- Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Germany
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Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy. CURRENT PHYSICAL MEDICINE AND REHABILITATION REPORTS 2014; 2:184-195. [PMID: 26005600 DOI: 10.1007/s40141-014-0056-z] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Stroke is one of the leading causes of long-term disability today; therefore, many research efforts are focused on designing maximally effective and efficient treatment methods. In particular, robotic stroke rehabilitation has received significant attention for upper-limb therapy due to its ability to provide high-intensity repetitive movement therapy with less effort than would be required for traditional methods. Recent research has focused on increasing patient engagement in therapy, which has been shown to be important for inducing neural plasticity to facilitate recovery. Robotic therapy devices enable unique methods for promoting patient engagement by providing assistance only as needed and by detecting patient movement intent to drive to the device. Use of these methods has demonstrated improvements in functional outcomes, but careful comparisons between methods remain to be done. Future work should include controlled clinical trials and comparisons of effectiveness of different methods for patients with different abilities and needs in order to inform future development of patient-specific therapeutic protocols.
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