This paper presents a pan-tilt sensor fusion platform for activity tracking and fall-detection which can work as a reliable surveillance system with long-term care function. A low cost thermal array sensor and a distance sensor are integrated together as the sensor module. The sensor module is installed on a pan-tilt orienting mechanism with two rotation degrees of freedom to increase the field of view while reducing the number of sensors used on-board. The performance of the sensor test platform is analyzed. The location of the indoor object as well as its size can be estimated based on a novel sensor fusion algorithm. The support vector machine (SVM) based machine learning algorithm is applied for fall detection. The preliminary experiment result shows a 95% accuracy to identify falling action from similar normal indoor activity such as sitting and picking up stuff.
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ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
September 18–20, 2017
Snowbird, Utah, USA
Conference Sponsors:
- Aerospace Division
ISBN:
978-0-7918-5826-4
PROCEEDINGS PAPER
A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection
Zhangjie Chen,
Zhangjie Chen
Stony Brook University, Stony Brook, NY
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Hanwei Liu,
Hanwei Liu
Stony Brook University, Stony Brook, NY
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Yuqiao Wang,
Yuqiao Wang
Stony Brook University, Stony Brook, NY
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Ya Wang
Ya Wang
Stony Brook University, Stony Brook, NY
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Zhangjie Chen
Stony Brook University, Stony Brook, NY
Hanwei Liu
Stony Brook University, Stony Brook, NY
Yuqiao Wang
Stony Brook University, Stony Brook, NY
Ya Wang
Stony Brook University, Stony Brook, NY
Paper No:
SMASIS2017-3882, V002T05A005; 7 pages
Published Online:
November 9, 2017
Citation
Chen, Z, Liu, H, Wang, Y, & Wang, Y. "A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection." Proceedings of the ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 2: Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation; Structural Health Monitoring. Snowbird, Utah, USA. September 18–20, 2017. V002T05A005. ASME. https://doi.org/10.1115/SMASIS2017-3882
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