Cargando…
Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model
Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificia...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416158/ https://www.ncbi.nlm.nih.gov/pubmed/37514738 http://dx.doi.org/10.3390/s23146445 |
_version_ | 1785087710230216704 |
---|---|
author | Kusunose, Masaya Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kuroda, Ryosuke |
author_facet | Kusunose, Masaya Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kuroda, Ryosuke |
author_sort | Kusunose, Masaya |
collection | PubMed |
description | Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries—MediaPipe and LightGBM—were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, −15°, or −30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R(2) and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion. |
format | Online Article Text |
id | pubmed-10416158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104161582023-08-12 Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model Kusunose, Masaya Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kuroda, Ryosuke Sensors (Basel) Article Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries—MediaPipe and LightGBM—were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, −15°, or −30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R(2) and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion. MDPI 2023-07-16 /pmc/articles/PMC10416158/ /pubmed/37514738 http://dx.doi.org/10.3390/s23146445 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kusunose, Masaya Inui, Atsuyuki Nishimoto, Hanako Mifune, Yutaka Yoshikawa, Tomoya Shinohara, Issei Furukawa, Takahiro Kato, Tatsuo Tanaka, Shuya Kuroda, Ryosuke Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title | Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title_full | Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title_fullStr | Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title_full_unstemmed | Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title_short | Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model |
title_sort | measurement of shoulder abduction angle with posture estimation artificial intelligence model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416158/ https://www.ncbi.nlm.nih.gov/pubmed/37514738 http://dx.doi.org/10.3390/s23146445 |
work_keys_str_mv | AT kusunosemasaya measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT inuiatsuyuki measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT nishimotohanako measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT mifuneyutaka measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT yoshikawatomoya measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT shinoharaissei measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT furukawatakahiro measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT katotatsuo measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT tanakashuya measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel AT kurodaryosuke measurementofshoulderabductionanglewithpostureestimationartificialintelligencemodel |