TY - GEN
T1 - A pervasive and sensor-free Deep Learning system for Parkinsonian gait analysis
AU - Ajay, Jerry
AU - Song, Chen
AU - Wang, Aosen
AU - Langan, Jeanne
AU - Li, Zhinan
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/6
Y1 - 2018/4/6
N2 - Parkinsonian gait is associated with life-threatening consequences such as fall risk in Parkinson patients. Conventional Parkinsonian gait analysis heavily relies on expensive sensors and human labor. In this work, we propose a sensor-free end-to-end system which enables the automated and accurate Parkinsonian gait detection and analysis upon the videos recorded by pervasive cameras. Specifically, we leverage Deep Learning technologies to extract the human skeleton in the video frame and address the camera random angle challenge. By analyzing the gait features, we train a classifier based on a binary decision tree. Out of 16 Parkinsonian gait and 13 healthy gait videos, our system is able to detect the Parkinsonian Gait with 93.75% accuracy and healthy gait with 100% accuracy.
AB - Parkinsonian gait is associated with life-threatening consequences such as fall risk in Parkinson patients. Conventional Parkinsonian gait analysis heavily relies on expensive sensors and human labor. In this work, we propose a sensor-free end-to-end system which enables the automated and accurate Parkinsonian gait detection and analysis upon the videos recorded by pervasive cameras. Specifically, we leverage Deep Learning technologies to extract the human skeleton in the video frame and address the camera random angle challenge. By analyzing the gait features, we train a classifier based on a binary decision tree. Out of 16 Parkinsonian gait and 13 healthy gait videos, our system is able to detect the Parkinsonian Gait with 93.75% accuracy and healthy gait with 100% accuracy.
UR - https://www.scopus.com/pages/publications/85050863770
U2 - 10.1109/BHI.2018.8333381
DO - 10.1109/BHI.2018.8333381
M3 - Conference contribution
AN - SCOPUS:85050863770
T3 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
SP - 108
EP - 111
BT - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
Y2 - 4 March 2018 through 7 March 2018
ER -