TY - GEN
T1 - Effective Severity Assessment of Parkinson's Disease using Wearable Sensors in Free-living IoT Environment
AU - Li, Ziheng
AU - Zhao, Yuting
AU - Qi, Jun
AU - Wang, Xulong
AU - Yang, Yun
AU - Yang, Po
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Internet of Things (IoT) Wearable technology plays a crucial role in assisting the diagnosis of Parkinson's disease (PD), and an efficient model for auxiliary diagnosis of the severity of PD can help reduce the workload for doctors. However, due to the influence of data collection environments and annotators, noisy label data is inevitable, which may have a negative impact on modeling the severity of PD. To address the above challenges, on the one hand, we collected a large number of activity signal data of Parkinson's patients in free-living environments, and on the other hand, we proposed an efficient PD stage assessment framework, which includes a noisy label processing method to alleviate the noisy label negative impact. Specifically, we collected signal data from 15 healthy controls and 68 PD patients through 12 activities, and then we proposed a framework for noisy label detection and correction. The experimental results on real PD data sets demonstrated that the proposed framework achieve 75.9% accuracy in PD stage assessment and significantly improve the classification performance of different types of basic classifiers, which is better than other noisy label detection algorithms and other PD stage assessment frameworks. Overall, in this work, we focus on modeling PD severity in free-living environments using a single wearable sensor and reducing the negative impact of noisy label data to better help PD patients manage the disease.
AB - Internet of Things (IoT) Wearable technology plays a crucial role in assisting the diagnosis of Parkinson's disease (PD), and an efficient model for auxiliary diagnosis of the severity of PD can help reduce the workload for doctors. However, due to the influence of data collection environments and annotators, noisy label data is inevitable, which may have a negative impact on modeling the severity of PD. To address the above challenges, on the one hand, we collected a large number of activity signal data of Parkinson's patients in free-living environments, and on the other hand, we proposed an efficient PD stage assessment framework, which includes a noisy label processing method to alleviate the noisy label negative impact. Specifically, we collected signal data from 15 healthy controls and 68 PD patients through 12 activities, and then we proposed a framework for noisy label detection and correction. The experimental results on real PD data sets demonstrated that the proposed framework achieve 75.9% accuracy in PD stage assessment and significantly improve the classification performance of different types of basic classifiers, which is better than other noisy label detection algorithms and other PD stage assessment frameworks. Overall, in this work, we focus on modeling PD severity in free-living environments using a single wearable sensor and reducing the negative impact of noisy label data to better help PD patients manage the disease.
KW - Noisy Label detection
KW - Parkinson's disease Disease stage diagnosis model
KW - Wearable Sensor
UR - https://www.scopus.com/pages/publications/85190253314
U2 - 10.1109/ICPADS60453.2023.00134
DO - 10.1109/ICPADS60453.2023.00134
M3 - Conference Proceeding
AN - SCOPUS:85190253314
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 900
EP - 906
BT - Proceedings - 2023 IEEE 29th International Conference on Parallel and Distributed Systems, ICPADS 2023
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023
Y2 - 17 December 2023 through 21 December 2023
ER -