Version 1
: Received: 1 November 2024 / Approved: 1 November 2024 / Online: 1 November 2024 (19:12:32 CET)
How to cite:
Yuda, E.; Ando, T.; Yoshida, Y. Detection of the Leg-Crossing Position Using Pressure Distribution Sensor and Machine Learning. Preprints2024, 2024110103. https://doi.org/10.20944/preprints202411.0103.v1
Yuda, E.; Ando, T.; Yoshida, Y. Detection of the Leg-Crossing Position Using Pressure Distribution Sensor and Machine Learning. Preprints 2024, 2024110103. https://doi.org/10.20944/preprints202411.0103.v1
Yuda, E.; Ando, T.; Yoshida, Y. Detection of the Leg-Crossing Position Using Pressure Distribution Sensor and Machine Learning. Preprints2024, 2024110103. https://doi.org/10.20944/preprints202411.0103.v1
APA Style
Yuda, E., Ando, T., & Yoshida, Y. (2024). Detection of the Leg-Crossing Position Using Pressure Distribution Sensor and Machine Learning. Preprints. https://doi.org/10.20944/preprints202411.0103.v1
Chicago/Turabian Style
Yuda, E., Tomoki Ando and Yutaka Yoshida. 2024 "Detection of the Leg-Crossing Position Using Pressure Distribution Sensor and Machine Learning" Preprints. https://doi.org/10.20944/preprints202411.0103.v1
Abstract
Human often cross their legs unconsciously while sitting, which can lead to issues like shifting the center of gravity, lower back pain, decreased blood flow and pelvic distortion. Detecting unconscious leg-crossing is important for maintaining correct posture. In this study, we explored the detection of leg-crossing postures using machine learning on data from body pressure distribution sensors. Collected 180 seconds of pressure data from 4 male subjects (25.8 ± 6.29 y.o.) in three conditions: no leg crossing, right leg crossed, and left leg crossed. Seven classifiers, including SVM and RF, were evaluated using Accuracy, Recall, Precision, and Specificity. As a result, SVM was difficult to classify.
Keywords
pressure distribution sensor; leg-crossing; machine learning; posture estimation
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
