TY - GEN
T1 - VR-RAISE
T2 - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
AU - Tan, Shaun Q.Y.
AU - Zhong, Yishan
AU - Marteau, Benoit
AU - Hornback, Andrew
AU - Shi, Wenqi
AU - Warshauer, Spencer
AU - Courter, Robert
AU - Donahue, Seth
AU - Lottiers, Lauren
AU - Russo, Stephanie
AU - Richardson, Robert T.
AU - Chafetz, Ross
AU - Wang, May D.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Upper Extremity (UE) Reachable Workspace (RW) is a key indicator of UE mobility. A quantitative clinical assess-ment of this workspace provides critical insights to understand patient quality of life and to guide UE-related surgeries. The assessment is typically performed in a professional motion tracking lab, using marker-based tracking technology. However, this assessment is impossible without the guidance of experienced motion capture experts and clinicians. It is also time-consuming to setup a session as well as post-process the data afterwards. Additionally, such a lab requires high setup costs. To address these limitations, we developed a virtual reality (VR) based system involving a single Meta Quest 3 VR headset. Our assessment application uses markerless tracking and is designed so the assessments require less subject preparation and system calibration. Also, the reachable workspace measurement values can be calculated automatically using the local computation power of the VR headset, making the assessment possible outside of a motion lab. With a low investment, the patient could perform the assessment at home. In addition to the advantages of VR in terms of cost and convenience, our markerless VR-based approach was shown to correlate with the marker-based method done in motion labs in measuring the upper extremity reachable workspace. In this pilot study consisting of five healthy subjects, we not only tracked the body using VR system and marker-based system but also experimented with two methods in anterior RW calculation. When averaging across the five subjects, we found that the VR tracking showed relative errors of 0.7% and 23.6% compared with the marker-based tracking, for the two methods respectively. Thus the findings of this study suggest that VR is a practical approach for measuring UE RW.
AB - Upper Extremity (UE) Reachable Workspace (RW) is a key indicator of UE mobility. A quantitative clinical assess-ment of this workspace provides critical insights to understand patient quality of life and to guide UE-related surgeries. The assessment is typically performed in a professional motion tracking lab, using marker-based tracking technology. However, this assessment is impossible without the guidance of experienced motion capture experts and clinicians. It is also time-consuming to setup a session as well as post-process the data afterwards. Additionally, such a lab requires high setup costs. To address these limitations, we developed a virtual reality (VR) based system involving a single Meta Quest 3 VR headset. Our assessment application uses markerless tracking and is designed so the assessments require less subject preparation and system calibration. Also, the reachable workspace measurement values can be calculated automatically using the local computation power of the VR headset, making the assessment possible outside of a motion lab. With a low investment, the patient could perform the assessment at home. In addition to the advantages of VR in terms of cost and convenience, our markerless VR-based approach was shown to correlate with the marker-based method done in motion labs in measuring the upper extremity reachable workspace. In this pilot study consisting of five healthy subjects, we not only tracked the body using VR system and marker-based system but also experimented with two methods in anterior RW calculation. When averaging across the five subjects, we found that the VR tracking showed relative errors of 0.7% and 23.6% compared with the marker-based tracking, for the two methods respectively. Thus the findings of this study suggest that VR is a practical approach for measuring UE RW.
UR - https://www.scopus.com/pages/publications/105026921071
UR - https://www.scopus.com/pages/publications/105026921071#tab=citedBy
U2 - 10.1109/ICIR64558.2024.10976979
DO - 10.1109/ICIR64558.2024.10976979
M3 - Conference contribution
AN - SCOPUS:105026921071
T3 - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
BT - 3rd IEEE International Conference on Intelligent Reality, ICIR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2024 through 6 December 2024
ER -