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Shi K, Huang R, Peng Z, Mu F, Yang X. MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram. Front Neurosci 2021; 15:704603. [PMID: 34867145 PMCID: PMC8636050 DOI: 10.3389/fnins.2021.704603] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/08/2021] [Indexed: 11/13/2022] Open
Abstract
The human-robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human-exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.
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Affiliation(s)
- Kecheng Shi
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.,Engineering Research Center of Human Robot Hybrid Intelligent Technologies and Systems, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Rui Huang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.,Engineering Research Center of Human Robot Hybrid Intelligent Technologies and Systems, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhinan Peng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.,Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.,Engineering Research Center of Human Robot Hybrid Intelligent Technologies and Systems, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengjun Mu
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.,Engineering Research Center of Human Robot Hybrid Intelligent Technologies and Systems, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao Yang
- Department of Orthopedics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Céspedes N, Irfan B, Senft E, Cifuentes CA, Gutierrez LF, Rincon-Roncancio M, Belpaeme T, Múnera M. A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World. Front Neurorobot 2021; 15:633248. [PMID: 33828473 PMCID: PMC8020889 DOI: 10.3389/fnbot.2021.633248] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/17/2021] [Indexed: 01/16/2023] Open
Abstract
What are the benefits of using a socially assistive robot for long-term cardiac rehabilitation? To answer this question we designed and conducted a real-world long-term study, in collaboration with medical specialists, at the Fundación Cardioinfantil-Instituto de Cardiología clinic (Bogotá, Colombia) lasting 2.5 years. The study took place within the context of the outpatient phase of patients' cardiac rehabilitation programme and aimed to compare the patients' progress and adherence in the conventional cardiac rehabilitation programme (control condition) against rehabilitation supported by a fully autonomous socially assistive robot which continuously monitored the patients during exercise to provide immediate feedback and motivation based on sensory measures (robot condition). The explicit aim of the social robot is to improve patient motivation and increase adherence to the programme to ensure a complete recovery. We recruited 15 patients per condition. The cardiac rehabilitation programme was designed to last 36 sessions (18 weeks) per patient. The findings suggest that robot increases adherence (by 13.3%) and leads to faster completion of the programme. In addition, the patients assisted by the robot had more rapid improvement in their recovery heart rate, better physical activity performance and a higher improvement in cardiovascular functioning, which indicate a successful cardiac rehabilitation programme performance. Moreover, the medical staff and the patients acknowledged that the robot improved the patient motivation and adherence to the programme, supporting its potential in addressing the major challenges in rehabilitation programmes.
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Affiliation(s)
- Nathalia Céspedes
- Biomedical Engineering Department, Colombian School of Engineering Julio Garavito, Bogotá, Colombia
| | - Bahar Irfan
- Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom
| | - Emmanuel Senft
- Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Carlos A. Cifuentes
- Biomedical Engineering Department, Colombian School of Engineering Julio Garavito, Bogotá, Colombia
| | | | | | - Tony Belpaeme
- Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom
- IDLab-imec, Ghent University, Ghent, Belgium
| | - Marcela Múnera
- Biomedical Engineering Department, Colombian School of Engineering Julio Garavito, Bogotá, Colombia
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Gliesche P, Krick T, Pfingsthorn M, Drolshagen S, Kowalski C, Hein A. Kinesthetic Device vs. Keyboard/Mouse: A Comparison in Home Care Telemanipulation. Front Robot AI 2021; 7:561015. [PMID: 33501324 PMCID: PMC7805703 DOI: 10.3389/frobt.2020.561015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 10/15/2020] [Indexed: 11/13/2022] Open
Abstract
Ensuring care is one of the biggest humanitarian challenges of the future since an acute shortage in nursing staff is expected. At the same time, this offers the opportunity for new technologies in nursing, as the use of robotic systems. One potential use case is outpatient care, which nowadays involves traveling long distances. Here, the use of telerobotics could provide a major relief for the nursing staff, as it could spare them many of those-partially far-journeys. Since autonomous robotic systems are not desired at least in Germany for ethical reasons, this paper evaluates the design of a telemanipulation system consisting of off-the-shelf components for outpatient care. Furthermore, we investigated the suitability of two different input devices for control, a kinesthetic device, and a keyboard plus mouse. We conducted the investigations in a laboratory study. This laboratory represents a realistic environment of an elderly home and a remote care service center. It was carried out with 25 nurses. Tasks common in outpatient care, such as handing out things (manipulation) and examining body parts (set camera view), were used in the study. After a short training period, all nurses were able to control a manipulator with the two input devices and perform the two tasks. It was shown that the Falcon leads to shorter execution times (on average 0:54.82 min, compared to 01:10.92 min with keyboard and mouse), whereby the participants were more successful with the keyboard plus mouse, in terms of task completion. There is no difference in usability and cognitive load. Moreover, we pointed out, that the access to this kind of technology is desirable, which is why we identified further usage scenarios.
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Affiliation(s)
- Pascal Gliesche
- R&D Division Health, OFFIS-Institute for Information Technology, Oldenburg, Germany
| | - Tobias Krick
- SOCIUM Research Center on Inequality and Social Policy, University of Bremen, Bremen, Germany.,High-Profile Area of Health Sciences, University of Bremen, Bremen, Germany
| | - Max Pfingsthorn
- R&D Division Production, OFFIS-Institute for Information Technology, Oldenburg, Germany
| | - Sandra Drolshagen
- R&D Division Production, OFFIS-Institute for Information Technology, Oldenburg, Germany
| | - Christian Kowalski
- R&D Division Health, OFFIS-Institute for Information Technology, Oldenburg, Germany
| | - Andreas Hein
- R&D Division Health, OFFIS-Institute for Information Technology, Oldenburg, Germany.,Assistance Systems and Medical Device Technology, Department of Health Services Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
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Shuggi IM, Oh H, Wu H, Ayoub MJ, Moreno A, Shaw EP, Shewokis PA, Gentili RJ. Motor Performance, Mental Workload and Self-Efficacy Dynamics during Learning of Reaching Movements throughout Multiple Practice Sessions. Neuroscience 2019; 423:232-248. [PMID: 31325564 DOI: 10.1016/j.neuroscience.2019.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 06/29/2019] [Accepted: 07/01/2019] [Indexed: 10/26/2022]
Abstract
The human capability to learn new motor skills depends on the efficient engagement of cognitive-motor resources, as reflected by mental workload, and psychological mechanisms (e.g., self-efficacy). While numerous investigations have examined the relationship between motor behavior and mental workload or self-efficacy in a performance context, a fairly limited effort focused on the combined examination of these notions during learning. Thus, this study aimed to examine their concomitant dynamics during the learning of a novel reaching skill practiced throughout multiple sessions. Individuals had to learn to control a virtual robotic arm via a human-machine interface by using limited head motion throughout eight practice sessions while motor performance, mental workload, and self-efficacy were assessed. The results revealed that as individuals learned to control the robotic arm, performance improved at the fastest rate, followed by a more gradual reduction of mental workload and finally an increase in self-efficacy. These results suggest that once the performance improved, less cognitive-motor resources were recruited, leading to an attenuated mental workload. Considering that attention is a primary cognitive resource driving mental workload, it is suggested that during early learning, attentional resources are primarily allocated to address task demands and not enough are available to assess self-efficacy. However, as the performance becomes more automatic, a lower level of mental workload is attained driven by decreased recruitment of attentional resources. These available resources allow for a reliable assessment of self-efficacy resulting in a subsequent observable change. These results are also discussed in terms of the application to the training and design of assistive technologies.
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Affiliation(s)
- Isabelle M Shuggi
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Hyuk Oh
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Helena Wu
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA
| | - Maria J Ayoub
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA
| | - Arianna Moreno
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA
| | - Emma P Shaw
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Patricia A Shewokis
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA; Nutrition Sciences Department, College of Nursing and Health Professions, Drexel University, Philadelphia, PA, USA
| | - Rodolphe J Gentili
- Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA; Maryland Robotics Center, University of Maryland, College Park, MD, USA.
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Abstract
Despite decades of societal investment in artificial learning systems, truly "intelligent" systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain "algorithm" itself-trying to replicate uniquely "neuromorphic" dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or "avatars", to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications.
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Affiliation(s)
- Philip H Goodman
- Department of Medicine and Program in Biomedical Engineering, University of Nevada Reno, USA.
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