scholarly journals Enhance embodiment of a virtual prosthesis through a training protocol using an EMG-based human-machine interface: a case series

Author(s):  
Karina Aparecida Rodrigues ◽  
João Vitor da Silva Moreira ◽  
Daniel José Lins Leal Pinheiro ◽  
Rodrigo Lantyer Marques Dantas ◽  
Thaís Cardoso Santos ◽  
...  

Abstract Background: The embodiment of a prosthesis can bring a series of benefits during the rehabilitation of people with amputation, such as improvement of motor control and sense of agency, in addition to optimizing the training process with the prosthetic limb. New therapeutic strategies capable of enhancing prosthesis embodiment are, therefore, a key point for better adaptation to and acceptance of prosthesis use. In this study, we developed a system and a new rehabilitation protocol using an EMG-based human-machine interface (HMI) to induce and enhance the embodiment of a virtual prosthesis.Methods: This is a case series with seven people of both sexes with unilateral transfemoral traumatic amputation without previous use of prostheses. Participants performed a training protocol with the EMG-based HMI during the preprosthetic rehabilitation phase, composed of six sessions held twice a week, each lasting thirty minutes. This system was composed of myoelectric control of the movements of a virtual prosthesis immersed in a 3D virtual environment. Additionally, vibrotactile stimuli were provided on the participant’s back corresponding to the movements performed. The objectives were to evaluate the virtual prosthesis embodiment, to investigate motor learning during training with EMG-based HMI, and to determine whether vibrotactile stimuli could facilitate the perception of virtual limb movements. The embodiment was investigated from a set of physiological and behavioral measurements and reports before and after the training. Motor learning was assessed through performance analysis. To investigate the use of vibrotactile stimulation to guide virtual prosthesis movements, the performance was assessed during the virtual prosthesis control test without adding vision.Results and conclusions: The different features evaluated throughout the protocol training consistently showed the induction and enhancement of virtual prosthesis embodiment and increased motor control. Therefore, this protocol using EMG-based HMI was shown to be a viable option to achieve the embodiment of a virtual prosthetic limb and to train motor control. Furthermore, the participants were able to guide the prosthesis based on vibrotactile stimuli, showing that this method can be used as an alternative sensorial path to be implemented in new therapeutic strategies and neuroprostheses to facilitate the movement perception of a prosthetic limb.

2021 ◽  
pp. 1-10
Author(s):  
Kathryn J. M. Lambert ◽  
Anthony Singhal ◽  
Ada W. S. Leung

Abstract Background: Motor imagery (MI) has become an increasingly popular rehabilitation tool for individuals with motor impairments. However, it has been proposed that individuals with Parkinson’s Disease (PKD) may not benefit from MI due to impairments in motor learning. Objective: This case series study investigated the effects of a 4-week MI training protocol on MI ability in three male individuals with PKD, with an emphasis on examining changes in brain responses. Methods: Training was completed primarily at home, via audio recordings, and emphasized the imagination of functional tasks. MI ability was assessed pre and post-training using subjective and objective imagery questionnaires, alongside an electroencephalographic (EEG) recording of a functional MI task. EEG analysis focused on the mu rhythm, as it has been proposed that suppression in the mu rhythm may reflect MI success and motor learning. Previous research has indicated that mu suppression is impaired in individuals with PKD, and may contribute to the disease’s associated deficits in motor learning. Results: Following training, all three participants improved in MI accuracy, but reported no notable improvements in MI vividness. Greater suppression in the mu rhythm was also exhibited by all three participants post-training. Conclusion: These results suggest the participants learned from the training protocol and that individuals with PKD are responsive to MI training. Further research on a larger scale is needed to verify the findings and determine if this learning translates to improvements in motor function.


Author(s):  
Amir Moeintaghavi ◽  
Negar Azami ◽  
Mohammad Sadegh Zohrevand ◽  
Farid Shiezadeh ◽  
Hamid Jafarzadeh ◽  
...  

1990 ◽  
Author(s):  
B. Bly ◽  
P. J. Price ◽  
S. Park ◽  
S. Tepper ◽  
E. Jackson ◽  
...  

2020 ◽  
Author(s):  
Jonathan Sanching Tsay ◽  
Alan S. Lee ◽  
Guy Avraham ◽  
Darius E. Parvin ◽  
Jeremy Ho ◽  
...  

Motor learning experiments are typically run in-person, exploiting finely calibrated setups (digitizing tablets, robotic manipulandum, full VR displays) that provide high temporal and spatial resolution. However, these experiments come at a cost, not limited to the one-time expense of purchasing equipment but also the substantial time devoted to recruiting participants and administering the experiment. Moreover, exceptional circumstances that limit in-person testing, such as a global pandemic, may halt research progress. These limitations of in-person motor learning research have motivated the design of OnPoint, an open-source software package for motor control and motor learning researchers. As with all online studies, OnPoint offers an opportunity to conduct large-N motor learning studies, with potential applications to do faster pilot testing, replicate previous findings, and conduct longitudinal studies (GitHub repository: https://github.com/alan-s-lee/OnPoint).


Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 687
Author(s):  
Jinzhen Dou ◽  
Shanguang Chen ◽  
Zhi Tang ◽  
Chang Xu ◽  
Chengqi Xue

With the development and promotion of driverless technology, researchers are focusing on designing varied types of external interfaces to induce trust in road users towards this new technology. In this paper, we investigated the effectiveness of a multimodal external human–machine interface (eHMI) for driverless vehicles in virtual environment, focusing on a two-way road scenario. Three phases of identifying, decelerating, and parking were taken into account in the driverless vehicles to pedestrian interaction process. Twelve eHMIs are proposed, which consist of three visual features (smile, arrow and none), three audible features (human voice, warning sound and none) and two physical features (yielding and not yielding). We conducted a study to gain a more efficient and safer eHMI for driverless vehicles when they interact with pedestrians. Based on study outcomes, in the case of yielding, the interaction efficiency and pedestrian safety in multimodal eHMI design was satisfactory compared to the single-modal system. The visual modality in the eHMI of driverless vehicles has the greatest impact on pedestrian safety. In addition, the “arrow” was more intuitive to identify than the “smile” in terms of visual modality.


Author(s):  
Saverio Trotta ◽  
Dave Weber ◽  
Reinhard W. Jungmaier ◽  
Ashutosh Baheti ◽  
Jaime Lien ◽  
...  

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 488-493
Author(s):  
Florian Beuss ◽  
Frederik Schmatz ◽  
Marten Stepputat ◽  
Fabian Nokodian ◽  
Wilko Fluegge ◽  
...  

Nanoscale ◽  
2021 ◽  
Author(s):  
Qiufan Wang ◽  
Jiaheng Liu ◽  
Guofu Tian ◽  
Daohong Zhang

The rapid development of human-machine interface and artificial intelligence is dependent on flexible and wearable soft devices such as sensors and energy storage systems. One of the key factors for...


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