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Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

Gonzalez-Vargas J, Dosen S, Amsuess S, Yu W, Farina D - PLoS ONE (2015)

Bottom Line: However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity.To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels.The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces.

View Article: PubMed Central - PubMed

Affiliation: Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.

ABSTRACT
Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

No MeSH data available.


Grasp selection performance between the Menu interface modes (M1G4, M2G4) and the Myoelectric interface modes for 4 grasps (G4, G4A, G4AR).The error bars indicate the standard error.
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pone.0127528.g006: Grasp selection performance between the Menu interface modes (M1G4, M2G4) and the Myoelectric interface modes for 4 grasps (G4, G4A, G4AR).The error bars indicate the standard error.

Mentions: Fig 6 compares the GSP in different conditions. When no feedback and no resetting was used (G4) in the Myoelectric interface, the GSP was lower compared to all the other conditions, although no statistical difference was found mainly due to a large variability in G4. When comparing the EMI modes (M1G4 and M2G4) to the Myoelectric modes (G4A and G4AR) the results were similar and no significant differences were found. This is an important outcome showing that the subjects could select the desired grasp with the novel method with similar success rate as when using the classical approach. The results in Table 6 show an overall comparison (pooled data) between the methods for the GA, GP and RA outcome measures. The EMI for grasping and releasing was comparable in performance to the Myoelectric interface, with the exception of the release action where subjects required significantly more attempts (p<0.05, r = 0.33) to successfully release an object using the EMI. This is not surprising since synchronizing the elbow extension with the electrocutaneous pattern pacing the release action is more difficult than just activating the extensor muscles to open the hand.


Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

Gonzalez-Vargas J, Dosen S, Amsuess S, Yu W, Farina D - PLoS ONE (2015)

Grasp selection performance between the Menu interface modes (M1G4, M2G4) and the Myoelectric interface modes for 4 grasps (G4, G4A, G4AR).The error bars indicate the standard error.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4466571&req=5

pone.0127528.g006: Grasp selection performance between the Menu interface modes (M1G4, M2G4) and the Myoelectric interface modes for 4 grasps (G4, G4A, G4AR).The error bars indicate the standard error.
Mentions: Fig 6 compares the GSP in different conditions. When no feedback and no resetting was used (G4) in the Myoelectric interface, the GSP was lower compared to all the other conditions, although no statistical difference was found mainly due to a large variability in G4. When comparing the EMI modes (M1G4 and M2G4) to the Myoelectric modes (G4A and G4AR) the results were similar and no significant differences were found. This is an important outcome showing that the subjects could select the desired grasp with the novel method with similar success rate as when using the classical approach. The results in Table 6 show an overall comparison (pooled data) between the methods for the GA, GP and RA outcome measures. The EMI for grasping and releasing was comparable in performance to the Myoelectric interface, with the exception of the release action where subjects required significantly more attempts (p<0.05, r = 0.33) to successfully release an object using the EMI. This is not surprising since synchronizing the elbow extension with the electrocutaneous pattern pacing the release action is more difficult than just activating the extensor muscles to open the hand.

Bottom Line: However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity.To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels.The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces.

View Article: PubMed Central - PubMed

Affiliation: Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.

ABSTRACT
Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

No MeSH data available.