TY - GEN
T1 - Classification of Therapeutic Hand Poses Using Convolutional Neural Networks
AU - Anaz, Aws
AU - Skubic, Marjorie
AU - Bridgeman, Jay
AU - Brogan, David M.
N1 - Funding Information:
ACKNOWLEDGMENT A.A. would like to acknowledge the Higher Committee for Education Development in Iraq (HCED/IRAQ) and University of Mosul/IRAQ for funding his scholarship.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Measurement of finger active range of motion (ARoM) is essential to quantify outcomes accurately after hand surgery and during rehabilitation. Currently, finger ARoM is measured by a hand-held goniometer, which introduces measurement error. Moreover, this method is time-consuming. To speed up and simplify this process, we developed a system to measure the ARoM automatically. However, to assess the ARoM for all joints, different hand poses are required. The goal, then, is to design a classifier that achieves accurate and automatic discovery of the hand pose. According to the detected pose, the system will apply the appropriate algorithm to measure the ARoM for all fingers. Furthermore, this will enable a camera capture control system to provide the best view by moving the camera as required by each algorithm. A critical part of the system is the classifier because it controls the accuracy and compute time of the measurement. In this paper, we describe a study of different classifiers for hand pose and include results. The best classifier achieves 99% accuracy in classifying 400 test samples from five previously unseen human subjects with a compute time of 8ms per sample.
AB - Measurement of finger active range of motion (ARoM) is essential to quantify outcomes accurately after hand surgery and during rehabilitation. Currently, finger ARoM is measured by a hand-held goniometer, which introduces measurement error. Moreover, this method is time-consuming. To speed up and simplify this process, we developed a system to measure the ARoM automatically. However, to assess the ARoM for all joints, different hand poses are required. The goal, then, is to design a classifier that achieves accurate and automatic discovery of the hand pose. According to the detected pose, the system will apply the appropriate algorithm to measure the ARoM for all fingers. Furthermore, this will enable a camera capture control system to provide the best view by moving the camera as required by each algorithm. A critical part of the system is the classifier because it controls the accuracy and compute time of the measurement. In this paper, we describe a study of different classifiers for hand pose and include results. The best classifier achieves 99% accuracy in classifying 400 test samples from five previously unseen human subjects with a compute time of 8ms per sample.
UR - http://www.scopus.com/inward/record.url?scp=85056595043&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513260
DO - 10.1109/EMBC.2018.8513260
M3 - Conference contribution
C2 - 30441208
AN - SCOPUS:85056595043
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3874
EP - 3877
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -