Animal respiration frequency monitoring system and respiration frequency recognition state method based on machine learning

Abstract

The invention discloses an animal respiration frequency monitoring system and a respiration frequency state recognition method based on machine learning. The animal respiration frequency monitoring system comprises an acquisition device and an intelligent terminal application, and the acquisition device acquires contact pressure, respiration frequency and acceleration information of monitored animals and judges whether effective contact occurs or not according to the magnitude of the contact pressure; the intelligent terminal application receives effective animal data of the acquisition deviceand recognizes the effective animal data into the animal respiration frequency state. By means of the recognition method, normal respiration of monitored animals in different motion states can be obtained through learning, and based on the learnt information, the respiration frequency state of the animals can be recognized. By means of the system, animal respiration frequency can be automaticallydetected and remotely monitored in real time, manual detection or recording is not needed, and a lot of manpower can be saved; by means of the method, the difference like the type, the age, the individual and the motion state of the animals can be eliminated, all monitored objects are truly customized, and the purpose of precisely and effectively detecting the respiration frequency is achieved.

Description

technical field [0001] The invention relates to the field of animal health monitoring, in particular to an animal respiratory frequency monitoring system and a machine learning-based respiratory frequency state identification method. Background technique [0002] Respiratory frequency is an important health indicator of animals. Respiratory frequency monitoring is one of the important ways to evaluate the health status of animals. The timeliness, accuracy and reliability of respiratory frequency monitoring directly affect the prevention, diagnosis, treatment and postoperative recovery of diseases. [0003] Traditional animal respiratory rate detection uses a single sample point of respiratory rate, which is very inconvenient to track changes in animal respiratory rate. At present, the measurement of animal respiratory rate is mainly done manually. The owner needs to manually measure the animal's respiratory rate every day, record the respiratory rate, and draw the breath Fre...

AI Technical Summary

Problems solved by technology

[0003] Traditional animal respiratory rate detection uses a single sample point of respiratory rate, which is very inconvenient to track changes in animal respiratory rate. At present, the measurement of animal respiratory rate is mainly done manually. The owner needs to manually measure the animal's respiratory rate every day, record the respiratory rate, and draw the breath frequency curve, which is a lot of work

Moreover, some animals, such as grazing cattle, cannot cooperate with respiratory rate detection, and often require anesthesia to achieve it. For such animals, traditional respiratory rate detection methods cannot implement high-frequency detection

[0004] In addition, different kinds of animals, different age groups of the same kind of animals, different individuals of the same kind of animals at the same age, and different exercise states of the same individual animals often have different normal / healthy breathing rates. For example, the breathing rate of macaques is 30-50 per minute. times, while the respiratory rate of dogs is 16-20 times per minute; another example: the normal respiratory rate of adult mice is 70-110 times per minute, while the respiratory rate of young mice is 100-130 times per minute, thus, the respiratory rate of animals The monitoring of animals should take into account differences in animal species, ages, individuals, and exercise status, which is difficult to achieve comprehensively in traditional methods

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