1
|
Winterbottom L, Chen A, Mendonca R, Nilsen DM, Ciocarlie M, Stein J. Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis. Disabil Rehabil 2024:1-10. [PMID: 38975689 DOI: 10.1080/09638288.2024.2375056] [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: 10/19/2023] [Accepted: 06/27/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care. METHODS In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians' perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video. RESULTS 154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time. CONCLUSIONS Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders' perspectives on utility and design.
Collapse
Affiliation(s)
- Lauren Winterbottom
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Ava Chen
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Rochelle Mendonca
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Dawn M Nilsen
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
- NewYork-Presbyterian Hospital, New York, NY, USA
| | - Matei Ciocarlie
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Joel Stein
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
- NewYork-Presbyterian Hospital, New York, NY, USA
| |
Collapse
|
2
|
Shao G, Xu G, Huo C, Nie Z, Zhang Y, Yi L, Wang D, Shao Z, Weng S, Sun J, Li Z. Effect of the VR-guided grasping task on the brain functional network. BIOMEDICAL OPTICS EXPRESS 2024; 15:77-94. [PMID: 38223191 PMCID: PMC10783918 DOI: 10.1364/boe.504669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/16/2024]
Abstract
Virtual reality (VR) technology has been demonstrated to be effective in rehabilitation training with the assistance of VR games, but its impact on brain functional networks remains unclear. In this study, we used functional near-infrared spectroscopy imaging to examine the brain hemodynamic signals from 18 healthy participants during rest and grasping tasks with and without VR game intervention. We calculated and compared the graph theory-based topological properties of the brain networks using phase locking values (PLV). The results revealed significant differences in the brain network properties when VR games were introduced compared to the resting state. Specifically, for the VR-guided grasping task, the modularity of the brain network was significantly higher than the resting state, and the average clustering coefficient of the motor cortex was significantly lower compared to that of the resting state and the simple grasping task. Correlation analyses showed that a higher clustering coefficient, local efficiency, and modularity were associated with better game performance during VR game participation. This study demonstrates that a VR game task intervention can better modulate the brain functional network compared to simple grasping movements and may be more beneficial for the recovery of grasping abilities in post-stroke patients with hand paralysis.
Collapse
Affiliation(s)
- Guangjian Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Gongcheng Xu
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Congcong Huo
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Zichao Nie
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Yizheng Zhang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Li Yi
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Dongyang Wang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Zhiyong Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Shanfan Weng
- School of Medicine, Foshan University, Foshan, China
| | - Jinyan Sun
- School of Medicine, Foshan University, Foshan, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| |
Collapse
|
3
|
Cisek K, Kelleher JD. Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3341-3352. [PMID: 37578924 DOI: 10.1109/tnsre.2023.3304758] [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: 08/16/2023]
Abstract
BACKGROUND There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.
Collapse
|
4
|
Chen Y, Zhang H, Wang C, Ang KK, Ng SH, Jin H, Lin Z. A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention. Sci Rep 2023; 13:4730. [PMID: 36959307 PMCID: PMC10036485 DOI: 10.1038/s41598-023-30716-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/28/2023] [Indexed: 03/25/2023] Open
Abstract
Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.
Collapse
Affiliation(s)
- Yongming Chen
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Haihong Zhang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore.
| | - Chuanchu Wang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
| | - Kai Keng Ang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Soon Huat Ng
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
| | - Huiwen Jin
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|
5
|
Hsu HY, Yang KC, Yeh CH, Lin YC, Lin KR, Su FC, Kuo LC. A Tenodesis-Induced-Grip exoskeleton robot (TIGER) for assisting upper extremity functions in stroke patients: a randomized control study. Disabil Rehabil 2022; 44:7078-7086. [PMID: 34586927 DOI: 10.1080/09638288.2021.1980915] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
PURPOSE This study was aimed toward developing a lightweight assisting tenodesis-induced-grip exoskeleton robot (TIGER) and to examine the performance of the TIGER in stroke patients with hemiplegia. METHODS This was a single-blinded, randomized control trial with pre-treatment, immediate post-treatment, and 12-week follow-up assessments. Thirty-four stroke patients were recruited and randomized to either an experimental or control group, where each participant in both groups underwent 40 min of training. In addition to a 20-min bout of regular task-specific motor training, each participant in the experimental group received 20 min of TIGER training, and the controls received 20 min of traditional occupational therapy in each treatment session. Primary outcomes based on the Fugl-Meyer Motor Assessment of Upper Extremity (FMA-UE) were recorded. RESULTS Thirty-two patients (94.1%) completed the study: 17 and 15 patients in the experimental and control groups, respectively. Significant beneficial effects were found on the total score (ANCOVA, p = 0.006), the wrist score (ANCOVA, p = 0.037), and the hand score (ANCOVA, p = 0.006) for the FMA-UE in the immediate post-treatment assessment of the participants receiving the TIGER training. CONCLUSION The TIGER has beneficial effects on remediating upper limb impairments in chronic stroke patients. Clinical trial registration: ClinicalTrials.gov; identifier NCT03713476Implications for rehabilitationBased on use-dependent plasticity concepts, robot training with the more distal segments of the upper extremities has a beneficial effect in patients with chronic stroke.A novel lightweight assisting tenodesis-induced-grip exoskeleton robot (TIGER) system using a mechanism involving musculotendinous coordination of the wrist and hand was proposed in this study.Between-group differences in changes in the upper limb motor performance were observed in the experimental group as compared to patients in the control group. For patients with chronic stroke, receiving 20 min of TIGER training in conjunction with 20 min of task-specific motor training led to clinically important changes in motor control and functioning of the affected upper limb.
Collapse
Affiliation(s)
- Hsiu-Yun Hsu
- Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Kang-Chin Yang
- Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan
| | - Chien-Hsien Yeh
- Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan
| | - Yu-Ching Lin
- Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Keng-Ren Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Fong-Chin Su
- Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan.,Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Li-Chieh Kuo
- Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan.,Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan
| |
Collapse
|
6
|
Chen A, Winterbottom L, O'Reilly K, Park S, Nilsen D, Stein J, Ciocarlie M. Design of Spiral-Cable Forearm Exoskeleton to Assist Supination for Hemiparetic Stroke Subjects. IEEE Int Conf Rehabil Robot 2022; 2022:1-6. [PMID: 36176095 PMCID: PMC9673240 DOI: 10.1109/icorr55369.2022.9896608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We present the development of a cable-based passive forearm exoskeleton that is designed to assist supination for hemiparetic stroke survivors. Our device uniquely provides torque sufficient for counteracting spasticity within a below-elbow apparatus. The mechanism consists of a spiral single-tendon routing embedded in a rigid forearm brace and terminated at the hand and upper-forearm. A spool with an internal releasable-ratchet mechanism allows the user to manually retract the tendon and rotate the hand to counteract involuntary pronation synergies due to stroke. We characterize the mechanism with benchtop testing and five healthy subjects, and perform a preliminary assessment of the exoskeleton with a single chronic stroke subject having minimal supination ability. The mechanism can be integrated into an existing active hand-opening orthosis to enable supination support during grasping tasks, and also allows for a future actuated supination strategy.
Collapse
|
7
|
Xu J, Meeker C, Chen A, Winterbottom L, Fraser M, Park S, Weber LM, Miya M, Nilsen D, Stein J, Ciocarlie M. Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for Stroke. IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION : ICRA : [PROCEEDINGS]. IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION 2022; 2022:8097-8103. [PMID: 37181542 PMCID: PMC10181849 DOI: 10.1109/icra46639.2022.9811932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In order to provide therapy in a functional context, controls for wearable robotic orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG-based method to operate a robotic hand orthosis, but the process of training a control that is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for controlling a powered hand orthosis for stroke subjects. To the best of our knowledge, this is the first use of semi-supervised learning for an orthotic application. Specifically, we propose a disagreement-based semi-supervision algorithm for handling intrasession concept drift based on multimodal ipsilateral sensing. We evaluate the performance of our algorithm on data collected from five stroke subjects. Our results show that the proposed algorithm helps the device adapt to intrasession drift using unlabeled data and reduces the training burden placed on the user. We also validate the feasibility of our proposed algorithm with a functional task; in these experiments, two subjects successfully completed multiple instances of a pick-and-handover task.
Collapse
Affiliation(s)
- Jingxi Xu
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Cassie Meeker
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Ava Chen
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Lauren Winterbottom
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Michaela Fraser
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Sangwoo Park
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Lynne M Weber
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Mitchell Miya
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Dawn Nilsen
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
- Co-Principal Investigators
| | - Joel Stein
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
- Co-Principal Investigators
| | - Matei Ciocarlie
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
- Co-Principal Investigators
| |
Collapse
|
8
|
Li X, Yin J, Li H, Xu G, Huo C, Xie H, Li W, Liu J, Li Z. Effects of Ordered Grasping Movement on Brain Function in the Performance Virtual Reality Task: A Near-Infrared Spectroscopy Study. Front Hum Neurosci 2022; 16:798416. [PMID: 35431845 PMCID: PMC9008886 DOI: 10.3389/fnhum.2022.798416] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 02/03/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Virtual reality (VR) grasping exercise training helps patients participate actively in their recovery and is a critical approach to the rehabilitation of hand dysfunction. This study aimed to explore the effects of active participation and VR grasping on brain function combined with the kinematic information obtained during VR exercises. Methods The cerebral oxygenation signals of the prefrontal cortex (LPFC/RPFC), the motor cortex (LMC/RMC), and the occipital cortex (LOC/ROC) were measured by functional near-infrared spectroscopy (fNIRS) in 18 young people during the resting state, grasping movements, and VR grasping movements. The EPPlus plug-in was used to collect the hand motion data during simulated interactive grasping. The wavelet amplitude (WA) of each cerebral cortex and the wavelet phase coherence (WPCO) of each pair of channels were calculated by wavelet analysis. The total difference in acceleration difference of the hand in the VR grasping movements was calculated to acquire kinematic characteristics (KCs). The cortical activation and brain functional connectivity (FC) of each brain region were compared and analyzed, and a significant correlation was found between VR grasping movements and brain region activation. Results Compared with the resting state, the WA values of LPFC, RPFC, LMC, RMC, and ROC increased during the grasping movements and the VR grasping movements, these changes were significant in LPFC (p = 0.0093) and LMC (p = 0.0007). The WA values of LMC (p = 0.0057) in the VR grasping movements were significantly higher than those in the grasping movements. The WPCO of the cerebral cortex increased during grasping exercise compared with the resting state. Nevertheless, the number of significant functional connections during VR grasping decreased significantly, and only the WPCO strength between the LPFC and LMC was enhanced. The increased WA of the LPFC, RPFC, LMC, and RMC during VR grasping movements compared with the resting state showed a significant negative correlation with KCs (p < 0.001). Conclusion The VR grasping movements can improve the activation and FC intensity of the ipsilateral brain region, inhibit the FC of the contralateral brain region, and reduce the quantity of brain resources allocated to the task. Thus, ordered grasping exercises can enhance active participation in rehabilitation and help to improve brain function.
Collapse
Affiliation(s)
- Xiangyang Li
- Nanchang Key Laboratory of Medical and Technology Research, Nanchang University, Nanchang, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Jiahui Yin
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Huiyuan Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Gongcheng Xu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Congcong Huo
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hui Xie
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wenhao Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jizhong Liu
- Nanchang Key Laboratory of Medical and Technology Research, Nanchang University, Nanchang, China
- *Correspondence: Jizhong Liu,
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
- Zengyong Li,
| |
Collapse
|
9
|
Gantenbein J, Dittli J, Meyer JT, Gassert R, Lambercy O. Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation. Front Neurorobot 2022; 16:815693. [PMID: 35264940 PMCID: PMC8900616 DOI: 10.3389/fnbot.2022.815693] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.
Collapse
Affiliation(s)
- Jessica Gantenbein
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Dittli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Thomas Meyer
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| |
Collapse
|
10
|
Chen W, Li G, Li N, Wang W, Yu P, Wang R, Xue X, Zhao X, Liu L. Soft Exoskeleton With Fully Actuated Thumb Movements for Grasping Assistance. IEEE T ROBOT 2022. [DOI: 10.1109/tro.2022.3148909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
11
|
Chen A, Winterbottom L, Park S, Xu J, Nilsen DM, Stein J, Ciocarlie M. Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke Hemiparesis. IEEE Robot Autom Lett 2022; 7:8276-8282. [DOI: 10.1109/lra.2022.3185365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ava Chen
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Lauren Winterbottom
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Sangwoo Park
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Jingxi Xu
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Dawn M. Nilsen
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Joel Stein
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Matei Ciocarlie
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| |
Collapse
|
12
|
Li W, Xu D. Application of intelligent rehabilitation equipment in occupational therapy for enhancing upper limb function of patients in the whole phase of stroke. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
|
13
|
Zhou H, Zhang Q, Zhang M, Shahnewaz S, Wei S, Ruan J, Zhang X, Zhang L. Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics. Front Neurorobot 2021; 15:659876. [PMID: 34054455 PMCID: PMC8155590 DOI: 10.3389/fnbot.2021.659876] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step toward the development of wearable hand robots. Electromyography (EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition toward application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.
Collapse
Affiliation(s)
- Hui Zhou
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Qianqian Zhang
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Mengjun Zhang
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Sameer Shahnewaz
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Shaocong Wei
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Jingzhi Ruan
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Xinyan Zhang
- Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Lingling Zhang
- Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| |
Collapse
|
14
|
Alicea R, Xiloyannis M, Chiaradia D, Barsotti M, Frisoli A, Masia L. A soft, synergy-based robotic glove for grasping assistance. WEARABLE TECHNOLOGIES 2021; 2:e4. [PMID: 38486631 PMCID: PMC10936321 DOI: 10.1017/wtc.2021.3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 01/18/2021] [Accepted: 02/18/2021] [Indexed: 03/17/2024]
Abstract
This paper presents a soft, tendon-driven, robotic glove designed to augment grasp capability and provide rehabilitation assistance for postspinal cord injury patients. The basis of the design is an underactuation approach utilizing postural synergies of the hand to support a large variety of grasps with a single actuator. The glove is lightweight, easy to don, and generates sufficient hand closing force to assist with activities of daily living. Device efficiency was examined through a characterization of the power transmission elements, and output force production was observed to be linear in both cylindrical and pinch grasp configurations. We further show that, as a result of the synergy-inspired actuation strategy, the glove only slightly alters the distribution of forces across the fingers, compared to a natural, unassisted grasping pattern. Finally, a preliminary case study was conducted using a participant suffering from an incomplete spinal cord injury (C7). It was found that through the use of the glove, the participant was able to achieve a 50% performance improvement (from four to six blocks) in a standard Box and Block test.
Collapse
Affiliation(s)
- Ryan Alicea
- Assistive Robotics and Interactive ExoSuits (ARIES) Lab, Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
| | - Michele Xiloyannis
- Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
- The Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Domenico Chiaradia
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Michele Barsotti
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Antonio Frisoli
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Lorenzo Masia
- Assistive Robotics and Interactive ExoSuits (ARIES) Lab, Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
| |
Collapse
|