This preprint is under consideration at Journal of NeuroEngineering and Rehabilitation. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Comprehensive Analysis of a Novel Real-time Adaptive Assist-as-needed Controller for Robot-assisted Rehabilitation for Stroke Patients
Ana Paula Arantes
Institute of Biomedical Engineering, University of New Brunswick
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Rehabilitation therapy plays an essential role in assisting stroke patients in regaining function. For this reason, many studies have been conducted to optimize rehabilitation interventions to improve effectiveness and efficiency. In this context, robotic devices for rehabilitation and assistance can be effective. Several studies have demonstrated that using a robot as a therapy tool can significantly reduce motor impairment. However, the slacking behavior, in which the patient lets the robot guide their movements even when they are capable of doing so by themselves, has been identified as a major barrier to reaching the full potential of robot-assist rehabilitation. This paper developed a novel electromyographical-based adaptive assist-as-needed (aAAN) controller aiming to avoid this slacking behavior.
Methods: Five stroke patients were recruited to test the controller. Motor impairment status was documented with the Fugl-Meyer (FMA) assessment. In this experiment, horizontal arms tasks were conducted with the robot off and on to assess the subject’s performance in both scenarios. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder EMG and electroencephalography (EEG) were used to assess the performance of the controller.
Results: The cross-sectional results showed strong second-order relationships between Fugl-Meyer score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed and electrophysiological metrics (EMG and EEG) were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks.
Conclusion: The preliminary results were very promising; slacking was avoided for all participants during training with the aAAN controller.
Keywords
Rehabilitation therapy, electromyographical-based adaptive assist-as-needed (aAAN), speed and electrophysiological metrics (EMG and EEG)
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, table 1 is only available as a download in the Supplemental Files section.
This is a list of supplementary files associated with this preprint. Click to download.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 06 Jan, 2021
No community comments so far
Reviewer #1 agreed
On 11 Jan, 2021
Reviewers invited
Invitations sent on 09 Jan, 2021
Editor assigned
On 31 Dec, 2020
Submission checks complete
On 31 Dec, 2020
Editor invited
On 31 Dec, 2020
First submitted
On 29 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Physical Medicine & Rehab
Learn more about our company.
This preprint is under consideration at Journal of NeuroEngineering and Rehabilitation. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Comprehensive Analysis of a Novel Real-time Adaptive Assist-as-needed Controller for Robot-assisted Rehabilitation for Stroke Patients
Ana Paula Arantes
Institute of Biomedical Engineering, University of New Brunswick
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 06 Jan, 2021
No community comments so far
Reviewer #1 agreed
On 11 Jan, 2021
Reviewers invited
Invitations sent on 09 Jan, 2021
Editor assigned
On 31 Dec, 2020
Submission checks complete
On 31 Dec, 2020
Editor invited
On 31 Dec, 2020
First submitted
On 29 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Physical Medicine & Rehab
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Rehabilitation therapy plays an essential role in assisting stroke patients in regaining function. For this reason, many studies have been conducted to optimize rehabilitation interventions to improve effectiveness and efficiency. In this context, robotic devices for rehabilitation and assistance can be effective. Several studies have demonstrated that using a robot as a therapy tool can significantly reduce motor impairment. However, the slacking behavior, in which the patient lets the robot guide their movements even when they are capable of doing so by themselves, has been identified as a major barrier to reaching the full potential of robot-assist rehabilitation. This paper developed a novel electromyographical-based adaptive assist-as-needed (aAAN) controller aiming to avoid this slacking behavior.
Methods: Five stroke patients were recruited to test the controller. Motor impairment status was documented with the Fugl-Meyer (FMA) assessment. In this experiment, horizontal arms tasks were conducted with the robot off and on to assess the subject’s performance in both scenarios. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder EMG and electroencephalography (EEG) were used to assess the performance of the controller.
Results: The cross-sectional results showed strong second-order relationships between Fugl-Meyer score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed and electrophysiological metrics (EMG and EEG) were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks.
Conclusion: The preliminary results were very promising; slacking was avoided for all participants during training with the aAAN controller.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, table 1 is only available as a download in the Supplemental Files section.