Presentation on returning from International Conference by master student Liangliang LI
Topic: ICCR2019 International Conference presentation
Time：Friday 8:00am,December 20,2019
Location：Room2333,Middle Building 3, Xingqing Campus
Presenter：Liangliang LI(李亮亮)
Conference Name：2019 2nd International Conference on Control and Robots (ICCR 2019)
Conference Time：December 12-14, 2019
Conference Location：Jeju Island, Korea
Conference Introduction：ICCR I conference that cover many topics and research area such as smart robotics bring together diverse concepts, disciplines, and techniques that, together, create a collaborative variety of useful devices, manipulators, and autonomous entities that serve intended purposes of specific human communities such as manufacturing devices, medical and remote manipulators that function in a broad range of environments.
Control technologies provide the basic tools used by robot creators to identify strategies, and optimizers to achieve the goals of the robotic enterprise.
The conference focuses on the trending, exciting and highly challenging areas of Computationally Intelligent Control Systems and Optimization algorithms. Applications include aerospace, underwater, biological medical and underground systems.
Information of conference paper(Oral Presentation)：
Title: Research on Elderly Fall Prediction for Walking Posture of Elderly-Assistant Robot
Author: Liangliang Li, Xiaodong Zhang, Xiaoqi Mu, Haipeng Xu
Abstract:
A prediction method of elderly fall based on RBF neural network and multi-sensor information fusion was proposed in this paper, which could help the user of elderly-assistant robot walk outside safe and reduce the damage caused by falls. Firstly, overall scheme of the elderly fall prediction system was designed. Tactile sensors, tri-axial acceleration sensor and gyroscope were used to collect the touch information of user’s hands, the acceleration information and angle of inclination information of human trunk, respectively. Then, three kinds of feature information were extracted separately and fused up by RBF neural network to get probability of elderly fall. If the probability exceeded the threshold, it was judged that the human body had a tendency to fall. Finally, experimental system construction and verification were carried out and the results showed that the prediction method of the user fall was reliable, and the overall prediction accuracy rate was 98%. Among them, the prediction accuracy of normal samples and user fall samples were 100% and 96%, respectively. This method can predict the user fall more accurately, and it provides some guarantees for preventing elderly fall.
