TY - GEN
T1 - Recognizing gait pattern of Parkinson's disease patients based on fine-grained movement function features
AU - Wang, Tianben
AU - Zhang, Daqing
AU - Wang, Zhu
AU - Jia, Jiangbo
AU - Ni, Hongbo
AU - Zhou, Xinshe
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/7/20
Y1 - 2016/7/20
N2 - Parkinson's Disease (PD) is one of the typical movement disorder diseases, which has a serious impact on the daily lives of elderly people. In this paper, we propose a novel framework for PD gait pattern recognition. The key idea of our approach is to distinguish PD gait patterns from healthy individuals by accurately extracting gait features that indicate three aspects of movement function, i.e., Stability, symmetry and harmony. Concretely, our framework contains three steps: gait phase discrimination, feature extraction and selection and pattern classification. In the first step, we put forward a key event based method to discriminate four gait phases from plantar pressure data. In the second step, based on the gait phases, we extract and select gait features that can indicate stability, symmetry and harmony of movement function. In the third step, we recognize PD gait pattern by employing BP neural network. We evaluate the framework using a real plantar pressure dataset that contains 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework outperforms the baseline approach by 32.7% on average in terms of Precision, 42.2% on average in terms of Recall, and 24.0% on average in terms of AUC.
AB - Parkinson's Disease (PD) is one of the typical movement disorder diseases, which has a serious impact on the daily lives of elderly people. In this paper, we propose a novel framework for PD gait pattern recognition. The key idea of our approach is to distinguish PD gait patterns from healthy individuals by accurately extracting gait features that indicate three aspects of movement function, i.e., Stability, symmetry and harmony. Concretely, our framework contains three steps: gait phase discrimination, feature extraction and selection and pattern classification. In the first step, we put forward a key event based method to discriminate four gait phases from plantar pressure data. In the second step, based on the gait phases, we extract and select gait features that can indicate stability, symmetry and harmony of movement function. In the third step, we recognize PD gait pattern by employing BP neural network. We evaluate the framework using a real plantar pressure dataset that contains 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework outperforms the baseline approach by 32.7% on average in terms of Precision, 42.2% on average in terms of Recall, and 24.0% on average in terms of AUC.
KW - Gait pattern recognition
KW - Gait phases
KW - Harmony
KW - Parkinson's Disease
KW - Stability
KW - Symmetry
UR - http://www.scopus.com/inward/record.url?scp=84983396447&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.26
DO - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.26
M3 - 会议稿件
AN - SCOPUS:84983396447
T3 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
SP - 1
EP - 10
BT - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
A2 - Ma, Jianhua
A2 - Li, Ali
A2 - Ning, Huansheng
A2 - Yang, Laurence T.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
Y2 - 10 August 2015 through 14 August 2015
ER -