Abstract
This study explores the utility of Electromyogram
(EMG) signals in the context of upper-limb exercises during
human-robot interaction considering muscle fatigue of the participant.
We hypothesise that the Electromyogram features from
muscles and kinematic measurements from the robotic sensors
can be used as indicators of fatigue and there is a potential
to identify the muscle contribution during the activity where
the Electromyogram data is correlated with the kinematic data.
Electromyogram measurements were taken from four upper limb
muscles of 10 healthy individuals. HapticMaster robot in active
assisted mode together with a virtual environment was used to
guide the participants for moving the robotic arm in a prescribed
path in a horizontal plane consisting of four segments. The
experiments were conducted until the participants reached a state
of fatigue or until a defined maximum number of 6 trials were
reached. Comparing the first and last trials indicated that the
muscle fatigue had caused an increase in the average power and
a decrease in the median frequency of EMG, which was more
visible in Trapezius (TRP) and Anterior Deltoid (DLT) muscles
in most of the analysed cases compared to Biceps Brachii (BB)
and Triceps Brachii (TB) muscles. As the muscles came to a
state of fatigue, the kinematic position also showed an increase
in tracking error between the first and last trials. The ’near-thebody’
segment movements (S1 and S4 segments) were found to
have less increase of tracking error compared to the ’away-frombody’
movements (S2 and S3 segments). A further analysis on
this proved that the tracking error observed was mainly due to
fatigue building up over the number of trials when performing
’away-from-body’ movements, and not a bi-product of perception
errors. We identify that Deltoid and Trapezius muscles were
fatigued more. These EMG fatigue indications can be mapped to
kinematic indications of fatigue mainly in the segments S2 and S3,
which required away from body movements because of the role
of these two muscles in lifting the arm to the shoulder height
in order to perform the activity. Our extracted features have
shown the potential to identify the fatigued muscles as expected.
The study also showed that the Electromyogram and kinematic
features have a potential to be used to highlight the extent of
muscle involvement.
(EMG) signals in the context of upper-limb exercises during
human-robot interaction considering muscle fatigue of the participant.
We hypothesise that the Electromyogram features from
muscles and kinematic measurements from the robotic sensors
can be used as indicators of fatigue and there is a potential
to identify the muscle contribution during the activity where
the Electromyogram data is correlated with the kinematic data.
Electromyogram measurements were taken from four upper limb
muscles of 10 healthy individuals. HapticMaster robot in active
assisted mode together with a virtual environment was used to
guide the participants for moving the robotic arm in a prescribed
path in a horizontal plane consisting of four segments. The
experiments were conducted until the participants reached a state
of fatigue or until a defined maximum number of 6 trials were
reached. Comparing the first and last trials indicated that the
muscle fatigue had caused an increase in the average power and
a decrease in the median frequency of EMG, which was more
visible in Trapezius (TRP) and Anterior Deltoid (DLT) muscles
in most of the analysed cases compared to Biceps Brachii (BB)
and Triceps Brachii (TB) muscles. As the muscles came to a
state of fatigue, the kinematic position also showed an increase
in tracking error between the first and last trials. The ’near-thebody’
segment movements (S1 and S4 segments) were found to
have less increase of tracking error compared to the ’away-frombody’
movements (S2 and S3 segments). A further analysis on
this proved that the tracking error observed was mainly due to
fatigue building up over the number of trials when performing
’away-from-body’ movements, and not a bi-product of perception
errors. We identify that Deltoid and Trapezius muscles were
fatigued more. These EMG fatigue indications can be mapped to
kinematic indications of fatigue mainly in the segments S2 and S3,
which required away from body movements because of the role
of these two muscles in lifting the arm to the shoulder height
in order to perform the activity. Our extracted features have
shown the potential to identify the fatigued muscles as expected.
The study also showed that the Electromyogram and kinematic
features have a potential to be used to highlight the extent of
muscle involvement.
| Original language | English |
|---|---|
| Title of host publication | The Tenth International Conference on Advances in Computer-Human Interactions |
| Editors | Roy Oberhauser, Jaehyun Park, Steffen Gerhard Scholz, Paul Rosenthal, Ljilja (Lilia) Ruzic Kascak |
| Publisher | IARIA |
| Pages | 187-192 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-61208-538-8 |
| Publication status | Published - 19 Mar 2017 |
| Event | The Tenth International Conference on Advances in Computer-Human Interactions - Nice, France Duration: 19 Mar 2017 → 23 Mar 2017 Conference number: 10th https://www.iaria.org/conferences2017/ProgramACHI17.html |
Conference
| Conference | The Tenth International Conference on Advances in Computer-Human Interactions |
|---|---|
| Abbreviated title | ACHI2017 |
| Country/Territory | France |
| City | Nice |
| Period | 19/03/17 → 23/03/17 |
| Internet address |
Keywords
- Robotic Rehabilitation
- Upper Limb Training
- Fatigue Detection
- Electromyogram
- kinematic fatigue indicator