Sun'an Wang
Xi'an Jiaotong University
4 Papers
5 Citations
Sun'an Wang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Cluster analysis & Signal. The author has an hindex of 1, co-authored 4 publications.
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Papers
Differential Soft Sensor-Based Measurement of Interactive Force and Assistive Torque for a Robotic Hip Exoskeleton.
Abstract: With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.
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Superpixel Segmentation Based on Grid Point Density Peak Clustering.
TL;DR: In this article, the density peak clustering was used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects, and the experimental results show that the boundary recall (BR) and achievement segmentation accuracy (ASA) are 95.0% and 96.3% respectively.
6
A User-Adaptive Online Learning Approach of Real-Time Gait Phase Detection for Walking Assistance
Binquan Zhang,Sun'an Wang,Hanqi Zhu +2 more
- 01 Dec 2019
TL;DR: The gait phase detector in this paper can automatically adapt to the wearer’s gait changes, such as in a rehabilitation process, to achieve individualization and user-adaptive gait assistance.
5
The Marked-line Recognition Based on Network Topology Diagram Points Clustering
Xianyi Chen,Xiafu Peng,Sun'an Wang +2 more
- 01 Dec 2019
TL;DR: A new method in marked-line recognition that has good robustness in complex outdoor environment and it takes about 45ms and it has good real time performance.