Hongjie Zou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hongjie Zou has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chuanglu Chen
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
1 shared publication - Haiying Zhang
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
1 shared publication - Jun Zhang
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
1 shared publication - Shaolong Zhang
Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
1 shared publication - Xingguang Geng
Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China
1 shared publication - Yitao Zhang
Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 14 November 2020
- Published by MDPI AG in Algorithms
- Vol. 13 (11) , 297
A Novel Multi-Dimensional Composition Method Based on Time Series Similarity for Array Pulse Wave Signals Detecting
Pulse wave signal sensed over the radial artery on the wrist is a crucial physiological indicator in disease diagnosis. The sensor array composed of multiple sensors has the ability to collect abundant pulse wave information. As a result, it has gradually attracted the attention of practitioners. However, few practical methods are used to obtain a one-dimensional pulse wave from the sensor array’s spatial multi-dimensional signals. The current algorithm using pulse wave with the highest amplitude value as the significant data suffers from low consistency because the signal acquired each time differs significantly due to the sensor’s relative position shift to the test area. This paper proposes a processing method based on time series similarity, which can take full advantage of sensor arrays’ spatial multi-dimensional characteristics and effectively avoid the above factors’ influence. A pulse wave acquisition system (PWAS) containing a micro-electro-mechanical system (MEMS) sensor array is continuously extruded using a stable dynamic pressure input source to simulate the pulse wave acquisition process. Experiments are conducted at multiple test locations with multiple data acquisitions to evaluate the performance of the algorithm. The experimental results show that the newly proposed processing method using time series similarity as the criterion has better consistency and stability.
- 14 November 2020
- Published by MDPI AG in Algorithms
- Vol. 13 (11) , 297
Open AccessA Novel Multi-Dimensional Composition Method Based on Time Series Similarity for Array Pulse Wave Signals Detecting
Pulse wave signal sensed over the radial artery on the wrist is a crucial physiological indicator in disease diagnosis. The sensor array composed of multiple sensors has the ability to collect abundant pulse wave information. As a result, it has gradually attracted the attention of practitioners. However, few practical methods are used to obtain a one-dimensional pulse wave from the sensor array’s spatial multi-dimensional signals. The current algorithm using pulse wave with the highest amplitude value as the significant data suffers from low consistency because the signal acquired each time differs significantly due to the sensor’s relative position shift to the test area. This paper proposes a processing method based on time series similarity, which can take full advantage of sensor arrays’ spatial multi-dimensional characteristics and effectively avoid the above factors’ influence. A pulse wave acquisition system (PWAS) containing a micro-electro-mechanical system (MEMS) sensor array is continuously extruded using a stable dynamic pressure input source to simulate the pulse wave acquisition process. Experiments are conducted at multiple test locations with multiple data acquisitions to evaluate the performance of the algorithm. The experimental results show that the newly proposed processing method using time series similarity as the criterion has better consistency and stability.