1
|
Jiang X, Li J, Zhu Z, Liu X, Yuan Y, Chou C, Yan S, Dai C, Jia F. MovePort: Multimodal Dataset of EMG, IMU, MoCap, and Insole Pressure for Analyzing Abnormal Movements and Postures in Rehabilitation Training. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2633-2643. [PMID: 39024074 DOI: 10.1109/tnsre.2024.3429637] [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: 07/20/2024]
Abstract
In most real world rehabilitation training, patients are trained to regain motion capabilities with the aid of functional/epidural electrical stimulation (FES/EES), under the support of gravity-assist systems to prevent falls. However, the lack of motion analysis dataset designed specifically for rehabilitation-related applications largely limits the conduct of pilot research. We provide an open access dataset, consisting of multimodal data collected via 16 electromyography (EMG) sensors, 6 inertial measurement unit (IMU) sensors, and 230 insole pressure sensors (IPS) per foot, together with a 26-sensor motion capture system, under different MOVEments and POstures for Rehabilitation Training (MovePort). Data were collected under diverse experimental paradigms. Twenty four participants first imitated multiple normal and abnormal body postures including (1) normal standing still, (2) leaning forward, (3) leaning back, and (4) half-squat, which in practical applications, can be detected as feedback to tune the parameters of FES/EES and gravity-assist systems to keep patients in a target body posture. Data under imitated abnormal gaits, e.g., (1) with legs raised higher under excessive electrical stimulation, and (2) with dragging legs under insufficient stimulation, were also collected. Data under normal gaits with low, medium and high speeds are also included. Pathological gait data from a subject with spastic paraplegia further increases the clinical value of our dataset. We also provide source codes to perform both intra- and inter-participant motion analyses of our dataset. We expect our dataset can provide a unique platform to promote collaboration among neurorehabilitation engineers.
Collapse
|
2
|
Ryali P, Wilson V, Celian C, Srivatsa AV, Ghani Y, Lentz J, Patton J. Evaluation of a passive wearable arm ExoNET. Front Robot AI 2024; 11:1387177. [PMID: 39050486 PMCID: PMC11265997 DOI: 10.3389/frobt.2024.1387177] [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: 02/16/2024] [Accepted: 06/17/2024] [Indexed: 07/27/2024] Open
Abstract
Wearable ExoNETs offer a novel, wearable solution to support and facilitate upper extremity gravity compensation in healthy, unimpaired individuals. In this study, we investigated the safety and feasibility of gravity compensating ExoNETs on 10 healthy, unimpaired individuals across a series of tasks, including activities of daily living and resistance exercises. The direct muscle activity and kinematic effects of gravity compensation were compared to a sham control and no device control. Mixed effects analysis revealed significant reductions in muscle activity at the biceps, triceps and medial deltoids with effect sizes of -3.6%, -4.5%, and -7.2% rmsMVC, respectively, during gravity support. There were no significant changes in movement kinematics as evidenced by minimal change in coverage metrics at the wrist. These findings reveal the potential for the ExoNET to serve as an alternative to existing bulky and encumbering devices in post-stroke rehabilitation settings and pave the way for future clinical trials.
Collapse
Affiliation(s)
- Partha Ryali
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Robotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Valentino Wilson
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Robotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Courtney Celian
- Robotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Adith V. Srivatsa
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Robotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Yaseen Ghani
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Jeremy Lentz
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - James Patton
- Neuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Robotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States
| |
Collapse
|
3
|
Jiryaei Z, Jafarpisheh AS. The usefulness of assistive soft robotics in the rehabilitation of patients with hand impairment: A systematic review. J Bodyw Mov Ther 2024; 39:398-409. [PMID: 38876658 DOI: 10.1016/j.jbmt.2024.02.025] [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: 03/30/2023] [Revised: 11/29/2023] [Accepted: 02/25/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION Loss of hand function causes severe limitations in activity in daily living. The hand-soft robot is one of the methods that has recently been growing to increase the patient's independence. The purpose of the present systematic review was to provide a classification, a comparison, and a design overview of mechanisms and the efficacy of the soft hand robots to help researchers approach this field. METHODS The literature research regarding such tools was conducted in PubMed, Google Scholar, Science Direct, and Cochrane Central Register for Controlled Trials. We included peer-reviewed studies that considered a soft robot glove as an assistive device to provide function. The two investigators screened the titles and abstracts, then independently reviewed the full-text articles. Disagreements about inclusion were resolved by consensus or a third reviewer. RESULTS A total of 15 articles were identified, describing 210 participants (23 healthy subjects). The tools were in three categories according to their actuation type (pneumatic system, cable-driven, another design). The most critical outcomes in studies included functional tasks (fourteen studies), grip strength (four studies), range of motion (ROM) (five studies), and user satisfaction (five studies). DISCUSSION Function and grip parameters are the most common critical parameters for tests of hand robots. Cable-driven transmission and soft pneumatic actuators are the most common choices for the actuation unit. Radder et al. study had the highest grade from other studies. That was the only RCT among studies. CONCLUSION Although few soft robotic gloves can be considered ready to reach the market, it seems these tools have the potential to be practical for people with a disability. But, we lack consistent evidence of comparing two or more soft robot gloves on the hand functions. Future research needs to assess the effect of soft robotic gloves on people with hand disorders with more populations.
Collapse
Affiliation(s)
- Zahra Jiryaei
- Rehabilitation Research Center, Department of Orthotics and Prosthetics, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran
| | - Amir Salar Jafarpisheh
- Department of Ergonomics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
| |
Collapse
|
4
|
Zhou YM, Hohimer CJ, Young HT, McCann CM, Pont-Esteban D, Civici US, Jin Y, Murphy P, Wagner D, Cole T, Phipps N, Cho H, Bertacchi F, Pignataro I, Proietti T, Walsh CJ. A portable inflatable soft wearable robot to assist the shoulder during industrial work. Sci Robot 2024; 9:eadi2377. [PMID: 38865477 DOI: 10.1126/scirobotics.adi2377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/17/2024] [Indexed: 06/14/2024]
Abstract
Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.
Collapse
Affiliation(s)
- Yu Meng Zhou
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Cameron J Hohimer
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Harrison T Young
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Connor M McCann
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - David Pont-Esteban
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Umut S Civici
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Yichu Jin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Patrick Murphy
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Diana Wagner
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Tazzy Cole
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Nathan Phipps
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Haedo Cho
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Franchesco Bertacchi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Isabella Pignataro
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Tommaso Proietti
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| |
Collapse
|
5
|
Urbina J, Abarca VE, Elias DA. Integration of music-based game approaches with wearable devices for hand neurorehabilitation: a narrative review. J Neuroeng Rehabil 2024; 21:89. [PMID: 38811987 PMCID: PMC11134927 DOI: 10.1186/s12984-024-01379-w] [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: 01/19/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Restoring hand functionality is critical for fostering independence in individuals with neurological disorders. Various therapeutic approaches have emerged to address motor function restoration, with music-based therapies demonstrating notable advantages in enhancing neuroplasticity, an integral component of neurorehabilitation. Despite the positive effects observed, there remains a gap in the literature regarding implementing music treatments in neurorehabilitation, such as Neurologic Music Therapy (NMT), especially in conjunction with emerging fields like wearable devices and game-based therapies. METHODS A literature search was conducted in various databases, including PubMed, Scopus, IEEE Xplore, and ACM Digital Library. The search was performed using a literature search methodology based on keywords. Information collected from the studies pertained to the approach used in music therapy, the design of the video games, and the types of wearable devices utilized. RESULTS A total of 158 articles were found, including 39 from PubMed, 34 from IEEE Xplore, 48 from Scopus, 37 from ACM Digital Library, and 35 from other sources. Duplicate entries, of which there were 41, were eliminated. In the first screening phase, 152 papers were screened for title and abstract. Subsequently, 89 articles were removed if they contained at least one exclusion criterion. Sixteen studies were considered after 63 papers had their full texts verified. CONCLUSIONS The convergence of NMT with emerging fields, such as gamification and wearable devices designed for hand functionality, not only expands therapeutic horizons but also lays the groundwork for innovative, personalized approaches to neurorehabilitation. However, challenges persist in effectively incorporating NMT into rehabilitation programs, potentially hindering its effectiveness.
Collapse
Affiliation(s)
- Javier Urbina
- Biomechanics and Applied Robotics Research Laboratory, Pontificia Universidad Católica del Perú, 15008, Lima, Peru
| | - Victoria E Abarca
- Biomechanics and Applied Robotics Research Laboratory, Pontificia Universidad Católica del Perú, 15008, Lima, Peru.
| | - Dante A Elias
- Biomechanics and Applied Robotics Research Laboratory, Pontificia Universidad Católica del Perú, 15008, Lima, Peru
| |
Collapse
|
6
|
Jeong J, Cho M, Kyung KU. Soft Artificial Muscle Based on Pre-Detwinned Shape Memory Alloy Spring Actuator Achieving High Passive Assistive Torque for Wearable Robot. Soft Robot 2024. [PMID: 38324013 DOI: 10.1089/soro.2023.0154] [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: 02/08/2024] Open
Abstract
For designing the assistive wearable rehabilitation robots, it is challenging to design the robot as energy efficient because the actuators have to be capable of overcoming human loads such as gravity of the body and spastic torque continuously during the assistance. To address these challenges, we propose a novel design of soft artificial muscle that utilizes shape memory alloy (SMA) spring actuators with pre-detwinning process. The SMA spring was fabricated through a process called pre-detwinning, which enhances the linearity of the SMA spring in martensite phase and unpowered restoring force, which is called passive force. The fabricated SMA spring can contract >60%. Finally, the soft wearable robot that can assist not only the gravitational torque exerted on the elbow by passive force, but also the elbow movements with active force was designed with a soft artificial muscle. A soft artificial muscle consists of the bundles of pre-detwinned SMA springs integrated with the stretchable coolant vessel. The stiffness of the muscle was measured as 1125 N/m in martensite phase and 1732 N/m in austenite phase. In addition, the muscle showed great actuation frequency performances, the bandwidth of which was measured as 0.5 Hz. The proposed wearable mechanism can fully compensate the gravitational torque for all the angles in passive mode. In addition, the proposed mechanism can produce high torque up to 3.5 Nm and movements in active mode.
Collapse
Affiliation(s)
- Jaeyeon Jeong
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Minjae Cho
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Ki-Uk Kyung
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| |
Collapse
|
7
|
LaMarca A, Tse I, Keysor J. Rehabilitation Technologies for Chronic Conditions: Will We Sink or Swim? Healthcare (Basel) 2023; 11:2751. [PMID: 37893825 PMCID: PMC10606667 DOI: 10.3390/healthcare11202751] [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: 08/17/2023] [Revised: 10/08/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
INTRODUCTION Chronic conditions such as stroke, Parkinson's disease, spinal cord injury, multiple sclerosis, vestibular disorders, chronic pain, arthritis, diabetes, chronic obstructive pulmonary disease (COPD), and heart disease are leading causes of disability among middle-aged and older adults. While evidence-based treatment can optimize clinical outcomes, few people with chronic conditions engage in the recommended levels of exercise for clinical improvement and successful management of their condition. Rehabilitation technologies that can augment therapeutic care-i.e., exoskeletons, virtual/augmented reality, and remote monitoring-offer the opportunity to bring evidence-based rehabilitation into homes. Successful integration of rehabilitation techniques at home could help recovery and access and foster long term self-management. However, widespread uptake of technology in rehabilitation is still limited, leaving many technologies developed but not adopted. METHODS In this narrative review, clinical need, efficacy, and obstacles and suggestions for implementation are discussed. The use of three technologies is reviewed in the management of the most prevalent chronic diseases that utilize rehabilitation services, including common neurological, musculoskeletal, metabolic, pulmonary, and cardiac conditions. The technologies are (i) exoskeletons, (ii) virtual and augmented reality, and (iii) remote monitoring. RESULTS Effectiveness evidence backing the use of technology in rehabilitation is growing but remains limited by high heterogeneity, lack of long-term outcomes, and lack of adoption outcomes. CONCLUSION While rehabilitation technologies bring opportunities to bridge the gap between clinics and homes, there are many challenges with adoption. Hybrid effectiveness and implementation trials are a possible path to successful technology development and adoption.
Collapse
Affiliation(s)
- Amber LaMarca
- Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA 02129, USA;
| | - Ivy Tse
- Doctor of Physical Therapy Program, MGH Institute of Health Professions, Boston, MA 02129, USA
| | - Julie Keysor
- School of Health Care Leadership, MGH Institute of Health Professions, Boston, MA 02129, USA
| |
Collapse
|
8
|
Ko MJ, Chuang YC, Ou-Yang LJ, Cheng YY, Tsai YL, Lee YC. The Application of Soft Robotic Gloves in Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sci 2023; 13:900. [PMID: 37371378 DOI: 10.3390/brainsci13060900] [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] [Received: 05/13/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Wearable robotic devices have been strongly put into use in both the clinical and research fields of stroke rehabilitation over the past decades. This study aimed to explore the effectiveness of soft robotic gloves (SRGs) towards improving the motor recovery and functional abilities in patients with post-stroke hemiparesis. Five major bibliographic databases, PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, were all reviewed for enrollment regarding comparative trials prior to 7 March 2023. We included adults with stroke and compared their rehabilitation using SRGs to conventional rehabilitation (CR) on hand function in terms of the Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE), Fugl-Meyer Distal Upper Extremity Motor Assessment (FMA-distal UE), box and blocks test score, grip strength test, and the Jebsen-Taylor hand function test (JTT). A total of 8 studies, comprising 309 participants, were included in the analysis. Compared to CR, rehabilitation involving SRGs achieved better FMA-UE (MD 6.52, 95% CI: 3.65~9.39), FMA-distal UE (MD 3.27, 95% CI: 1.50~5.04), and JJT (MD 13.34, CI: 5.16~21.53) results. Subgroup analysis showed that stroke latency of more than 6 months and training for more than 30 min offered a better effect as well. In conclusion, for patients with stroke, rehabilitation using SRGs is recommended to promote the functional abilities of the upper extremities.
Collapse
Affiliation(s)
- Ming-Jian Ko
- Department of Education, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ya-Chi Chuang
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Liang-Jun Ou-Yang
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taoyuan 333423, Taiwan
| | - Yuan-Yang Cheng
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Yu-Lin Tsai
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Yu-Chun Lee
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Department of Exercise Health Science, National Taiwan University of Sport, Taichung 404401, Taiwan
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan
| |
Collapse
|
9
|
Catalán JM, Trigili E, Nann M, Blanco-Ivorra A, Lauretti C, Cordella F, Ivorra E, Armstrong E, Crea S, Alcañiz M, Zollo L, Soekadar SR, Vitiello N, García-Aracil N. Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs). J Neuroeng Rehabil 2023; 20:61. [PMID: 37149621 PMCID: PMC10164333 DOI: 10.1186/s12984-023-01185-w] [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: 10/01/2022] [Accepted: 04/26/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. METHODS Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. RESULTS All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). CONCLUSIONS Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.
Collapse
Affiliation(s)
- José M Catalán
- Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernandez University, 03202, Elche, Spain.
| | - Emilio Trigili
- BioRobotics Institute, Scuola Superiore Sant'Anna, 56025, Pontedera, Italy.
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
| | - Marius Nann
- Clinical Neurotechnology Laboratory, Charité, Universitätsmedizin Berlin, 10117, Belin, Germany
| | - Andrea Blanco-Ivorra
- Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernandez University, 03202, Elche, Spain
| | - Clemente Lauretti
- Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, 00128, Rome, Italy
| | - Francesca Cordella
- Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, 00128, Rome, Italy
| | - Eugenio Ivorra
- University Institute for Human-Centered Technology Research (Human-Tech), Universitat Politècnica de València, 46022, Valencia, Spain
| | | | - Simona Crea
- BioRobotics Institute, Scuola Superiore Sant'Anna, 56025, Pontedera, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
- IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mariano Alcañiz
- University Institute for Human-Centered Technology Research (Human-Tech), Universitat Politècnica de València, 46022, Valencia, Spain
| | - Loredana Zollo
- Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, 00128, Rome, Italy
| | - Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Charité, Universitätsmedizin Berlin, 10117, Belin, Germany
| | - Nicola Vitiello
- BioRobotics Institute, Scuola Superiore Sant'Anna, 56025, Pontedera, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
- IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Nicolás García-Aracil
- Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernandez University, 03202, Elche, Spain
| |
Collapse
|
10
|
Fu J, Choudhury R, Hosseini SM, Simpson R, Park JH. Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8134. [PMID: 36365832 PMCID: PMC9655258 DOI: 10.3390/s22218134] [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: 09/13/2022] [Revised: 10/13/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human-robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.
Collapse
Affiliation(s)
- Jirui Fu
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Renoa Choudhury
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Saba M. Hosseini
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Rylan Simpson
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Joon-Hyuk Park
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| |
Collapse
|