Version 1
: Received: 23 August 2018 / Approved: 23 August 2018 / Online: 23 August 2018 (05:55:56 CEST)
How to cite:
Yahya, M.; Shah, J.A.; Warsi, A.; Kadir, K.; Khan, S.; Izani, M. Real Time Elbow Angle Estimation Using Single RGB Camera. Preprints2018, 2018080407. https://doi.org/10.20944/preprints201808.0407.v1
Yahya, M.; Shah, J.A.; Warsi, A.; Kadir, K.; Khan, S.; Izani, M. Real Time Elbow Angle Estimation Using Single RGB Camera. Preprints 2018, 2018080407. https://doi.org/10.20944/preprints201808.0407.v1
Yahya, M.; Shah, J.A.; Warsi, A.; Kadir, K.; Khan, S.; Izani, M. Real Time Elbow Angle Estimation Using Single RGB Camera. Preprints2018, 2018080407. https://doi.org/10.20944/preprints201808.0407.v1
APA Style
Yahya, M., Shah, J.A., Warsi, A., Kadir, K., Khan, S., & Izani, M. (2018). Real Time Elbow Angle Estimation Using Single RGB Camera. Preprints. https://doi.org/10.20944/preprints201808.0407.v1
Chicago/Turabian Style
Yahya, M., Sheroz Khan and Mohamad Izani. 2018 "Real Time Elbow Angle Estimation Using Single RGB Camera" Preprints. https://doi.org/10.20944/preprints201808.0407.v1
Abstract
The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants of (height, 168 ± 8 cm; mass, 61 ± 17 kg) to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.
Keywords
angle estimation; microsoft kinect; single camera; markerless mocap system
Subject
Engineering, Control and Systems Engineering
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.
