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Real-time behavior recognition system based on low-power wide-area Internet of things and capsule network and working method thereof

A wide-area Internet of Things and recognition system technology, applied in the field of real-time behavior recognition system, can solve the problems of reducing the accuracy of behavior recognition and not considering the spatial characteristics, and achieve the effect of improving the accuracy, stability and accuracy of behavior recognition

Active Publication Date: 2019-02-19
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing mainstream algorithms only consider whether the behavior information contains certain features, but do not consider the spatial characteristics of these features, so to a certain extent, it will cause misjudgment and reduce the accuracy of behavior recognition.

Method used

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  • Real-time behavior recognition system based on low-power wide-area Internet of things and capsule network and working method thereof
  • Real-time behavior recognition system based on low-power wide-area Internet of things and capsule network and working method thereof
  • Real-time behavior recognition system based on low-power wide-area Internet of things and capsule network and working method thereof

Examples

Experimental program
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Effect test

Embodiment 1

[0111] A real-time behavior recognition system based on low-power wide-area Internet of Things and capsule network, such as figure 1 As shown, it includes a behavior information acquisition part, a behavior information transmission part, a behavior information processing part, and a behavior information application part connected in sequence. Each part is connected in sequence to realize system functions, and the behavior information application part is also connected in reverse to the behavior information processing part. Part, in order to realize the feedback adjustment to the system.

[0112] The behavior information acquisition part is used to: perceive, collect, store, and transmit the user's behavior information from the environment, and the behavior information includes: acceleration, angular velocity, heart rate;

[0113] The behavior information transmission part is used to: transmit the collected behavior information through the low-power wide-area Internet of Things...

Embodiment 2

[0118] A kind of real-time behavior recognition system based on low-power wide-area Internet of Things and capsule network according to embodiment 1, such as figure 2 As shown, the difference is:

[0119] The behavior information acquisition part includes a sensor module and an intelligent hardware module; the sensor module includes several different types of sensors, and the intelligent hardware module is respectively connected to several different types of sensors. The received behavior information is stored. The behavior information acquisition part selects the module and designs the equipment according to the relevant parameters of the sensor module and intelligent hardware. Among them, the sensor parameters mainly include: sampling frequency, service life, precision, etc., and the intelligent hardware parameters mainly include: power consumption, volume, etc. Select the corresponding sensor module and intelligent hardware module according to the scene, user's needs and...

Embodiment 3

[0135] The working method of the real-time behavior recognition system based on low-power wide-area Internet of Things and capsule network described in embodiment 2, such as image 3 As shown, this embodiment is applied to the hospital for real-time behavior recognition of patients. Take monitoring the patient's daily activity as an example. The patients in the hospital need to do different activities for different patients during the recovery stage. For example, some patients are in the wound Running and other sports are not allowed before healing. Some patients jogging for 3 hours a day may be helpful for physical recovery. The system uses the acceleration sensor S1, angular velocity sensor S2, and heart rate sensor S3 to obtain the behavior information of the patient in a day, and improves the credibility of the information after information preprocessing, and then conducts real-time behavior recognition through the trained model. Hospital administrators can set different p...

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Abstract

The invention relates to a real-time behavior recognition system based on low-power wide-area Internet of tings and a capsule network and a working method thereof. The system comprises four parts, namely, behavior information acquisition, behavior information transmission, behavior information processing and behavior information application. A low-power wide-area network node and a low-power localarea network gateway are adopted for the transmission of a behavior information access layer, so that remote low-power behavior information transmission is realized. Behavior information indeterminacy is subjected to inconsistency and incompleteness processing in a behavior information platform layer, so that the credibility of behavior information is improved. A capsule is adopted for automatically acquiring available features and a space relationship between the features for recognition, so that the accuracy is improved greatly. An error correction mechanism is added in the behavior information application layer, so that the generalization performance of the system is improved, and an effective feasible method is provided for real-time behavior recognition. Thus, the system has certainadvantages on the aspects of practicability, adaptivity, reliability and the like.

Description

technical field [0001] The invention relates to a real-time behavior recognition system based on low-power wide-area Internet of Things and a capsule network and a working method thereof, belonging to the technical field of artificial intelligence and pattern recognition. Background technique [0002] With the development and maturity of advanced technologies such as the Internet of Things, artificial intelligence, big data, and cloud computing, more and more scholars have begun to pay attention to the research of behavior recognition. Behavior recognition has become a hot research direction in the field of artificial intelligence and pattern recognition research. Currently, there are mainly two types of behavior recognition: video-based behavior recognition and sensor-based behavior recognition. Video-based behavior recognition focuses on analyzing human motion videos or images captured by camera equipment, and sensor-based behavior recognition focuses on analyzing motion ...

Claims

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Application Information

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IPC IPC(8): H04W4/70H04W52/02H04W84/18G06N3/04
CPCH04W4/70H04W52/0203H04W84/18G06N3/045Y02D30/70
Inventor 许宏吉石磊鑫陈敏王珏邢庆华
Owner SHANDONG UNIV
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