Posts Tagged Sociology
[Abstract + References] Soft Robotic Glove with Alpha Band Brain Computer Interface for Post-Stroke Hand Function Rehabilitation – Conference Publication
Posted by Kostas Pantremenos in Paretic Hand, REHABILITATION, Rehabilitation robotics on January 26, 2023
Abstract:
Loss of hand dexterity is a major challenge faced by post-stroke patients who strive to resume their ordinary daily lives. Effective hand function rehabilitation treatment for such population is therefore necessary. A soft robotic glove system operated through SSVEP-based BCI has been reported to be an effective tool for post-stroke hand motor function recovery. This study further evaluated the application of visual stimulation in the alpha band for SSVEP-assisted rehabilitation. We compared the treatment outcome with stimulations within the alpha band to that outside the band. A total of 20 post-stroke patients with severe upper limb dysfunction were randomly assigned to alpha band group and non-alpha band group. The experiment result was assessed with Fugl-Meyer upper limb Motor Assessment (FMAUE) and alpha EEG oscillation analysis. The alpha band group showed slightly but notably higher FMA-UE scores (P<0.05), and significantly increased alpha wave EEG oscillations (P<0.05). The result demonstrated the usefulness of alpha band SSVEP for post stroke hand function rehabilitation.
Published in: 2022 14th Biomedical Engineering International Conference (BMEiCON)
References
1.
E. S. Donkor, “Stroke in the 21(st) Century: A Snapshot of the Burden Epidemiology and Quality of Life”, Stroke Res Treat, vol. 2018, pp. 3238165, 2018.
Show in Context CrossRef Google Scholar
2.
P. H. Chau, J. Woo, W. B. Goggins, M. Wong, K. C. Chan and S. C. Ho, “Analysis of spatio-temporal variations in stroke incidence and case-fatality in Hong Kong”, Geospat Health, vol. 6, no. 1, pp. 13-20, Nov 2011, [online] Available: http://www.ncbi.nlm.nih.gov/pubmed/22109859.
Show in Context CrossRef Google Scholar
3.
S. M. Hatem et al., “Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery”, Front Hum Neurosci, vol. 10, pp. 442, 2016.
Show in Context CrossRef Google Scholar
4.
D. Mattia et al., “The Promotoer a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response”, BMC Neurol, vol. 20, no. 1, pp. 254, Jun 2020.
Show in Context CrossRef Google Scholar
5.
J. Bernhardt et al., “Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce”, Int J Stroke, vol. 12, no. 5, pp. 444-450, Jul 2017.
Show in Context CrossRef Google Scholar
6.
T. J. Wallin, J. Pikul and R. F. Shepherd, “3D printing of soft robotic systems”, Nat Rev Mater, vol. 3, no. 6, pp. 84-100, Jun 2018.
Show in Context CrossRef Google Scholar
7.
K. K. Ang et al., “Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke”, Front Neuroeng, vol. 7, pp. 30, 2014.
Show in Context CrossRef Google Scholar
8.
M. A. Cervera et al., “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Ann Clin Transl Neurol, vol. 5, no. 5, pp. 651-663, May 2018.
Show in Context CrossRef Google Scholar
9.
K. K. Ang et al., “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clin EEG Neurosci, vol. 46, no. 4, pp. 310-20, Oct 2015.
Show in Context CrossRef Google Scholar
10.
M. Arvaneh et al., “Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement”, Neural Comput Appl, vol. 28, no. 11, pp. 3259-3272, 2017.
Show in Context CrossRef Google Scholar
11.
J. Cantillo-Negrete, R. I. Carino-Escobar, P. Carrillo-Mora, D. Elias-Vinas and J. Gutierrez-Martinez, “Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients”, J Healthc Eng, vol. 2018, pp. 1624637, 2018.
Show in Context CrossRef Google Scholar
12.
A. A. Frolov et al., “Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial”, Front Neurosci, vol. 11, pp. 400, 2017.
Show in Context CrossRef Google Scholar
13.
K. LaFleur, K. Cassady, A. Doud, K. Shades, E. Rogin and B. He, “Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface”, J Neural Eng, vol. 10, no. 4, pp. 046003, Aug 2013.
Show in Context CrossRef Google Scholar
14.
X. G. Chen, B. Zhao, Y. J. Wang and X. R. Gao, “Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm”, J Neural Eng, vol. 16, no. 2, Apr 2019.
Show in Context CrossRef Google Scholar
15.
Y. Q. Chu, X. G. Zhao, Y. J. Zou, W. L. Xu and Y. W. Zhao, “Robot-Assisted Rehabilitation System Based on SSVEP Brain-Computer Interface for Upper Extremity”, 2018 Ieee International Conference on Robotics and Biomimetics (Robio), pp. 1058-1063, 2018, [online] Available: //WOS:000468772200169.
16.
N. Guo et al., “SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation”, IEEE Trans Neural Syst Rehabil Eng, vol. 30, pp. 1737-1744, 2022.
17.
N. Shi et al., “Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report”, Journal of Neurorestoratology, vol. 8, no. 1, pp. 40-52, 2020.
Show in Context CrossRef Google Scholar
18.
Y. L. Zhu, Y. Li, J. L. Lu and P. C. Li, “A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control”, Front Neurorobotics, vol. 14, Nov 2020.
Show in Context CrossRef Google Scholar
19.
G. S. Li, H. L. Li, J. B. Pu, F. Wan and Y. Hu, “Effect of brain alpha oscillation on the performance in laparoscopic skills simulator training”, Surg Endosc, vol. 35, no. 2, pp. 584-592, Feb 2021.
Show in Context CrossRef Google Scholar
20.
A. Mottaz, M. Solcà, C. Magnin, T. Corbet, A. Schnider and A. G. Guggisberg, “Neurofeedback training of alpha-band coherence enhances motor performance”, CLIN NEUROPHYSIOL, vol. 126, no. 9, pp. 1754-1760, 2014.
Show in Context CrossRef Google Scholar
21.
A. R. Fugl-Meyer, L. Jaasko, I. Leyman, S. Olsson and S. Steglind, “The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance”, Scand J Rehabil Med, vol. 7, no. 1, pp. 13-31, 1975, [online] Available: https://www.ncbi.nlm.nih.gov/pubmed/1135616.
Show in Context Google Scholar
22.
Z. M. Miao, Q. H. Wu, F. Wan and Y. Hu, “State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review”, Journal of Neurorestoratology, vol. 8, no. 1, pp. 12-25.
Show in Context Google Scholar
23.
S. Fok et al., “An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology”, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 30 Aug.-3 Sept. 2011, pp. 6277-6280, 2011.
24.
Z. He, Y. Watanabe, R. R. Yurievich, Y. Ogai, Y. Kang and D. Shin, “Development of a support robot hand system using SSVEP”, 2019.
Show in Context Google Scholar
25.
C. Yaqi, X. Zhao, Y. Zou, P. Xu and Y. Zhao, “Robot-Assisted Rehabilitation System Based on SSVEP Brain-Computer Interface for Upper Extremity”, 2018.
Show in Context Google Scholar
26.
Y. Chu, X. Zhao, Y. Zou, P. Xu and Y. Zhao, “Robot-Assisted Rehabilitation System Based on SSVEP Brain-Computer Interface for Upper Extremity”, 2018.
27.
A. M. Savić, N. M. Malešević and M. B. Popović, “Feasibility of a hybrid brain-computer interface for advanced functional electrical therapy”, The Scientific World Journal, vol. 2014, 2014.
Show in Context CrossRef Google Scholar
28.
L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces a review”, sensors, vol. 12, no. 2, pp. 1211-1279, 2012.
Show in Context CrossRef Google Scholar
29.
W. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis”, Brain Res Brain Res Rev, vol. 29, no. 2, pp. 169-3, Apr 1999.
[Abstract] The Ethics of Rehabilitation in Virtual Reality: the role of Self-Avatars and Deep Learning
Posted by Kostas Pantremenos in Virtual reality rehabilitation on January 17, 2022
Abstract
Medical Rehabilitation systems are constantly improving according to the technological evolution, and the use of virtual reality for assistive purposes is being investigated in research. One particular case of rehabilitation regards the hands and upper limbs of hemiplegic post-stroke patients. While studying possible ways to support this population, we discuss the ethical issues that arise from the use of extended reality technologies, in particular self-avatars, and the use of artificial intelligence, in particular deep learning, in neuro-cognitive applications. We present a rehabilitation approach, based on the digital embodiment of virtual limbs, in which the movements of the self-avatars are modified and optimized by the system, to lead the patients to the performance of the natural actions they lost. The ethical discussion starts from the policy of the representation of the self in virtual environments, and additional issues arise when the users have disabilities. A learning system, based on a convolutional neural network, allows the personalization of the parameters of the therapy in the long term. The collection and analysis of physiological data is also discussed, again in a scenario that involves vulnerable users.
[Abstract] A Systematic Review of the Use of Virtual Reality Games in Post-stroke Rehabilitation – Conference Publication
Posted by Kostas Pantremenos in Paretic Hand, Video Games/Exergames, Virtual reality rehabilitation on October 19, 2021
Abstract
Video games are known for being great at captivating the attention of the user, and by applying Virtual Reality they create vivid, realistic, and exciting experiences. Strokes affect a large portion of the world’s population, causing innumerable deaths, and leaving a great number of its survivors with disabilities in their upper limbs. With this paper we intend to review the role of virtual reality games on the post-stroke recovery of the upper limbs. A systematic search was conducted in March 2021 using IEEE, PubMed, ScienceDirect, and ACM Digital Library databases. According to the 9 articles selected from the 153 surveyed, Virtual Reality has proven to be particularly useful in both keeping patients focused and entertained during their training, which would otherwise be viewed as a repetitive and uninteresting task. Based on the analysis of the reviewed papers, we identify and present in this study several useful techniques for developing an engaging, motivating, and effective Virtual Reality serious game for poststroke rehabilitation.
[Abstract] Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation – IEEE Conference Publication
Posted by Kostas Pantremenos in Paretic Hand, Virtual reality rehabilitation on January 5, 2019
Abstract
The objective of this paper was to compare the incidence of a rehabilitation game in motor ability with dynamic difficulty adjustment (ADD) in comparison to a manual configuration. To achieve that, a virtual tool called “Bug catcher” was developed, which is focused in upper limb rehabilitation. This tool uses a dynamic difficulty adjustment based in fuzzy logic. The population involved for the present study were made by 2 users, a 18-year-old patient with a hemiparesis that limits her motor ability in her left upper limb, and a 37-year-old patient with motor monoparesis in his right upper limb. This tool was used in both users, each one with a different configuration (automatic or manual), and the motor ability from both participants was objectively measured using Box and Blocks Test, applied before, during and after each session; additionally, a performance index (percentage of success) was defined in order to determine the progress of the participants in the virtual tool. As a result, it was obtained that user number one using the game with ADD, managed to obtain not only a better performance in the sessions but also an important advance in her motor skill in comparison to the user 2 with the manual configuration.
[Abstract] Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation
Posted by Kostas Pantremenos in Paretic Hand, Rehabilitation robotics on July 31, 2018
Abstract
I. Introduction
In the modern world an extended life expectancy coupled with a sedentary lifestyle raises concerns over long term health in the population. This is highlighted by the increasing incidence of disability stemming from multiple sources, for example medical conditions such as cancer or stroke [1]. While avoiding the lifestyle factors that have a high association with these diseases would be the preferred solutions of health services the world over, as populations get progressively older and more sedentary, this becomes increasingly more difficult [1], [2]. The treatment of these conditions is often complex; in stroke for example, the initial incident is a constriction of blood flow in the brain which in turn damages the nervous system’s ability to communicate with the rest of the body. This damage will occur in one hemisphere of the body but can impact both the upper and lower limbs, as well as impairing functional processes such as speech and cognitive thinking.
TBI Rehabilitation 