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Detection of Motion on a Trampoline with a Smartwatch
Satoshi Kobayashi1, Tatsuhito Hasegawa1
1Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan.
Sensors (Basel, Switzerland)
|December 28, 2021
Summary
This study introduces a smartwatch-based method using a convolutional neural network to detect trampoline exercises. The system achieves 78.8% accuracy, promoting home trampoline fitness effectively.
Area of Science:
- Sports Science
- Wearable Technology
- Machine Learning
Background:
- Home trampoline use is increasing for fitness.
- Objective motion detection for trampoline exercise is lacking.
- Smartwatch accelerometers offer a potential solution for motion tracking.
Purpose of the Study:
- To develop a smartwatch-based method for detecting motions performed on a trampoline.
- To create a system promoting home trampoline exercise through accurate motion detection.
- To evaluate the feasibility of on-device prediction for real-time feedback.
Main Methods:
- Utilized a convolutional neural network (CNN) model for motion detection.
- Mounted an accelerometer on a smartwatch for data acquisition.
- Conducted leave-one-subject-out cross-validation with eight participants.
Main Results:
- Achieved 78.8% estimation accuracy in detecting trampoline motions.
- Outperformed baseline methods in accuracy.
- Demonstrated effectiveness for on-device prediction with favorable inference time and battery consumption.
Conclusions:
- The proposed CNN-based method accurately detects trampoline exercises using smartwatch data.
- The system is suitable for promoting home trampoline workouts.
- On-device processing capabilities make it practical for real-world application.
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