The MultiSenseBadminton dataset can be used to build
AI-based coaching assistants for badminton players
GWANGJU, South Korea, May 6, 2024
/PRNewswire/ -- In sports training, practice is the key, but
being able to emulate the techniques of professional athletes can
take a player's performance to the next level. AI-based
personalized sports coaching assistants can make this a reality by
utilizing published datasets. With cameras and sensors
strategically placed on the athlete's body, these systems can track
everything, including joint movement patterns, muscle activation
levels, and gaze movements.
Using this data, personalized feedback is provided on player
technique, along with improvement recommendations. Athletes can
access this feedback anytime, and anywhere, making these systems
versatile for athletes at all levels.
Now, in a study published in the journal Scientific
Data on April 5, 2024,
researchers led by Associate Professor SeungJun Kim from the Gwangju Institute of
Science and Technology (GIST), South
Korea, in collaboration with researchers from Massachusetts Institute of Technology (MIT), CSAIL, USA,
have developed a MultiSenseBadminton dataset for AI-driven
badminton training.
"Badminton could benefit from these various sensors, but
there is a scarcity of comprehensive badminton action datasets for
analysis and training feedback," says Ph.D. candidate
Minwoo Seong, the first author of
the study.
Supported by the 2024 GIST-MIT project, this study took
inspiration from MIT's ActionSense
project, which used wearable sensors to track everyday kitchen
tasks such as peeling, slicing vegetables, and opening jars. Seong
collaborated with MIT's team, including
MIT CSAIL postdoc researcher Joseph DelPreto and MIT CSAIL Director
and MIT EECS Professor Daniela Rus and Wojciech Matusik. Together, they developed the
MultiSenseBadminton dataset, capturing movements and physiological
responses of badminton players. This dataset, shaped with insights
from professional badminton coaches, aims to enhance the quality of
forehand clear and backhand drive strokes. For this, the
researchers collected 23 hours of swing motion data from 25 players
with varying levels of training experience.
During the study, players were tasked with repeatedly executing
forehand clear and backhand drive shots while sensors recorded
their movements and responses. These included inertial measurement
units (IMU) sensors to track joint movements, electromyography
(EMG) sensors to monitor muscle signals, insole sensors for foot
pressure, and a camera to record both body movements and
shuttlecock positions. With a total of 7,763 data points collected,
each swing was meticulously labeled based on stroke type, player's
skill level, shuttlecock landing position, impact location relative
to the player, and sound upon impact. The dataset was then
validated using a machine learning model, ensuring its suitability
for training AI models to evaluate stroke quality and offer
feedback.
"The MultiSenseBadminton dataset can be used to build
AI-based education and training systems for racket sports players.
By analyzing the disparities in motion and sensor data among
different levels of players and creating AI-generated action
trajectories, the dataset can be applied to personalized motion
guides for each level of players," says Seong.
The gathered data can enhance training through haptic
vibration or electrical muscle stimulation, promoting better motion
and refining swing techniques. Additionally, player tracking data,
like that in the MultiSenseBadminton dataset, could fuel virtual
reality games or training simulations, making sports training more
accessible and affordable, potentially transforming how people
exercise.
In the long run, the researchers speculate that
this dataset could make sports training more accessible and
affordable for a broader audience, promote overall well-being, and
foster a healthier population.
Reference
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Title of original
paper:
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MultiSenseBadminton:
Wearable Sensor–Based Biomechanical Dataset for Evaluation of
Badminton Performance
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Journal:
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Scientific
Data
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DOI:
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https://doi.org/10.1038/s41597-024-03144-z
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About Gwangju Institute of Science and Technology
(GIST)
http://www.gist.ac.kr/.
Contact:
Chang-Sung Kang
82 62 715 6253
377094@email4pr.com
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SOURCE Gwangju Institute of Science and Technology (GIST)