Convolutional neural network-based tumble detection method

A convolutional neural network and detection method technology, applied in the field of fall detection, can solve the problems of MEMS gyroscope signal drift error, 3-axis accelerometer voltage fluctuation, affecting the accuracy and effectiveness of the fall detection algorithm, etc.

Inactive Publication Date: 2019-10-01
BEIJING UNIV OF TECH
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Problems solved by technology

However, there are signal drift errors in the MEMS gyroscope, and voltage fluctuations in the 3-axis accelerometer during motion. These factors will affect the accuracy and effectiveness of the fall detection algorithm

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  • Convolutional neural network-based tumble detection method
  • Convolutional neural network-based tumble detection method
  • Convolutional neural network-based tumble detection method

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Embodiment Construction

[0034] (1) With reference to the human body activity model, the size of the sensing module used for the human body activity data used in the present invention is 30mm×30mm×9mm. It is mainly composed of CC2530 microcontroller, MEMS activity sensor integrating 3-axis acceleration and gyroscope, ZigBee radio frequency and power management module, etc. Among them, the transmission rate of the ZigBee module is 115200baud, and the maximum transmission distance is 100m. MEMS activity sensor 3-axis gyroscope, accelerometer. The gyroscope can measure up to ±2000° / sec, and the accelerometer can measure up to ±16g. The sampling frequency of the motion perception module is 100Hz.

[0035] (2) Referring to the human activity model, coordinate conversion is performed on the data sets released by SisFall and MobiFall, and the range specification and visual representation are performed on the data after coordinate conversion, and the 3-axis acceleration and angular velocity data are convert...

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Abstract

The invention provides a convolutional neural network-based tumble detection method, and belongs to the field of electronic information. The method comprises the following steps: converting three-axisacceleration and angular velocity data acquired by an MEMS into RGB pixels, and introducing a sliding window to construct a pixel graph capable of simultaneously reflecting the change characteristicsof the three-axis acceleration and the angular velocity in the activity process of old people; designing a fall monitoring algorithm FD-CNN based on CNN by referring to LeNet 5 architecture, and classifying pixel images to realize a fall detection algorithm. By constructing the FD-CNN, interferences such as signal drift errors of the MEMS gyroscope and voltage fluctuation generated by the three-axis accelerometer in a motion state can be overcome, and fall detection is accurately realized. The accuracy of the FD-CNN network model system reaches 98.62%, and the sensitivity and the specificityof the FD-CNN network model system reach 98.65% and 99.80% respectively. The sensitivity and specificity of the system to fall detection both reach 100%.

Description

technical field [0001] The invention belongs to the field of electronic information, and is a technology based on a convolutional neural network that can be applied to fall detection. Background technique [0002] The existing fall detection technologies can be generally divided into the following three categories according to the different sensor technologies used: Wearable Sensors, Ambient Sensors and Vision-based Sensors. Although situational awareness technology and visual perception technology have the characteristics of high accuracy and intuitive motion capture, sensor deployment and detection algorithms are complex, and the monitoring range is limited, which may even easily expose user privacy. With the development of MEMS (microelectromechanical systems) technology, researchers have integrated inertial sensors into small wearable devices for fall detection. The fall detection method based on MEMS has the advantages of low deployment cost, less environmental impact,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/103G06V20/10G06N3/045
Inventor 何坚祖天奇余立张子浩
Owner BEIJING UNIV OF TECH
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