| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 104 === Recently, society is faced with the problematic issue of an aging population.
The eldercare issue is extremely important. The frequency of falls in the elderly
is higher than in younger people with a greater risk caused by treatment delay.
Therefore, the research of fall detection systems has been increasing drastically.
This thesis proposes to develop a fall detection system based on neural networks
with Kinect depth-camera. We hope it can operate reliable in a complex
environment or in multi-person scenarios. The system uses raw data of Kinect
depth images to locate the ground in the scene, identify the foreground pixels with
a background subtraction algorithm, and then tracked the foreground for analysis.
Last, the system will judge whether the fall events occurred by using its welltrained
neural networks model and the specified features. When fall events are
detected, the system would record the image and time immediately, then report to
caregivers for efficient aid. Additionally, this thesis will discuss the reasons for
rule decision system’s misjudgment and the advantages of using neural networks.
The performance of the proposed system was verified by six experimental
scenarios. There are three single person and for multi-person experimental
scenarios. After these experiments, we would compare the result of rule decision
system with the proposed system and discuss the difference and the reason of
misjudgment between both of them. Among all of these experimental scenarios:
168 are fall events and 168 are not fall events. The results show the sensitivity
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rate and the specificity rate were 97% and 90%, respectively. And the Kappa value
of the proposed system is 0.84 which is higher than 0.80, showing that we have a
reliable system that accurately reflects reality in terms of fall events.
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