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Robbin Romijnders
Kiel University: Christian-Albrechts-Universitat zu Kiel
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2507-0924
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Identi cation of individual gait events is essential for clinical gait analysis, because it can be
used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's
disease. Previous research has shown that gait events can be detected from a shank-mounted inertial
measurement unit (IMU), however detection performance was often evaluated only from straight-line walking.
For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as
in single-task and dual-task conditions.
Methods: Participants (older adults, people with Parkinson's disease, or people who had su ered from a
stroke) performed three di erent walking trials: 1) straight-line walking, 2) slalom walking, 3) Stroop-and-walk
trial. An optical motion capture system was used a reference system. Markers were attached to the heel and
toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of
the shank IMUs was used to detect instances of initial foot contact (IC) and nal foot contact (FC), which
were compared to reference values obtained from the marker trajectories.
Results: The detection method showed high recall, precision and F1 scores in di erent populations for both
initial contacts and nal contacts during straight-line walking (IC: recall = 100%, precision = 100%, F1 score
= 100%; FC: recall = 100%, precision = 100%, F1 score = 100%), slalom walking (IC: recall = 100%,
precision 99%, F1 score =100%; FC: recall = 100%, precision 99%, F1 score =100%), and turning (IC:
recall 85%, precision 95%, F1 score 91%; FC: recall 84%, precision 95%, F1 score 89%).
Conclusions: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom
walking and turning. However, more false events were observed during turning and more events were missed
during turning. For use in daily life we recommend identifying turning before extracting temporal gait
parameters from identi ed gait events.
Keywords
gait, gyroscope, older adults, Parkinson, step detection, stroke, turns, walking, wearable sensors
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CURRENT STATUS: Published
View the published article at Journal of NeuroEngineering and Rehabilitation.
No community comments so far
Editorial decision: Accept
On 24 Jan, 2021
Review #1 received
Received 10 Jan, 2021
Review #2 received
Received 10 Jan, 2021
Reviewer #2 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Editor assigned
On 24 Dec, 2020
Submission checks complete
On 24 Dec, 2020
Editor invited
On 24 Dec, 2020
Version 1
Posted 09 Sep, 2020
No comments provided
Editorial decision: Major revision
On 26 Nov, 2020
Review #2 received
Received 15 Nov, 2020
Review #1 received
Received 09 Nov, 2020
Reviewer #2 agreed
On 25 Oct, 2020
Reviewer #1 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
First submitted
On 07 Sep, 2020
Editor assigned
On 07 Sep, 2020
Submission checks complete
On 06 Sep, 2020
Editor invited
On 06 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Physical Medicine & Rehab
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See the published version of this preprint at Journal of NeuroEngineering and Rehabilitation.
This is a preprint, 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
Robbin Romijnders
Kiel University: Christian-Albrechts-Universitat zu Kiel
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2507-0924
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: Published
View the published article at Journal of NeuroEngineering and Rehabilitation.
No community comments so far
Editorial decision: Accept
On 24 Jan, 2021
Review #1 received
Received 10 Jan, 2021
Review #2 received
Received 10 Jan, 2021
Reviewer #2 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 03 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Editor assigned
On 24 Dec, 2020
Submission checks complete
On 24 Dec, 2020
Editor invited
On 24 Dec, 2020
Version 1
Posted 09 Sep, 2020
No comments provided
Editorial decision: Major revision
On 26 Nov, 2020
Review #2 received
Received 15 Nov, 2020
Review #1 received
Received 09 Nov, 2020
Reviewer #2 agreed
On 25 Oct, 2020
Reviewer #1 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
First submitted
On 07 Sep, 2020
Editor assigned
On 07 Sep, 2020
Submission checks complete
On 06 Sep, 2020
Editor invited
On 06 Sep, 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
View the published article at Journal of NeuroEngineering and Rehabilitation.
Background: Identi cation of individual gait events is essential for clinical gait analysis, because it can be
used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's
disease. Previous research has shown that gait events can be detected from a shank-mounted inertial
measurement unit (IMU), however detection performance was often evaluated only from straight-line walking.
For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as
in single-task and dual-task conditions.
Methods: Participants (older adults, people with Parkinson's disease, or people who had su ered from a
stroke) performed three di erent walking trials: 1) straight-line walking, 2) slalom walking, 3) Stroop-and-walk
trial. An optical motion capture system was used a reference system. Markers were attached to the heel and
toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of
the shank IMUs was used to detect instances of initial foot contact (IC) and nal foot contact (FC), which
were compared to reference values obtained from the marker trajectories.
Results: The detection method showed high recall, precision and F1 scores in di erent populations for both
initial contacts and nal contacts during straight-line walking (IC: recall = 100%, precision = 100%, F1 score
= 100%; FC: recall = 100%, precision = 100%, F1 score = 100%), slalom walking (IC: recall = 100%,
precision 99%, F1 score =100%; FC: recall = 100%, precision 99%, F1 score =100%), and turning (IC:
recall 85%, precision 95%, F1 score 91%; FC: recall 84%, precision 95%, F1 score 89%).
Conclusions: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom
walking and turning. However, more false events were observed during turning and more events were missed
during turning. For use in daily life we recommend identifying turning before extracting temporal gait
parameters from identi ed gait events.
Figure 1
Figure 2
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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