Research On Recognition Of Dairy Goat Movement Behavior Based On Wearable Multi-sensor

The movement behavior of dairy goats is an important manifestation of their health.It is of great significance to identify the movement behavior of dairy goats by intelligent wearable devices for intelligent breeding of dairy goats.Aiming at the problems of less wearable devices,large difference of research objects,less behavior recognition categories and low accuracy of behavior recognition in the current dairy goat behavior recognition process,this paper uses multi-sensor wearable devices,dairy goat behavior sensor data expansion algorithm based on improved generative adversarial network and dairy goat behavior recognition algorithm based on double attention time convolution network to realize the accurate recognition of dairy goat motion behavior.The main contents of this paper are as follows :(1)Dairy goat behavior data acquisition and preprocessing based on wearable multisensor.In order to solve the problems of less data sets and low data quality in the process of dairy goat behavior recognition,the individual characteristics,living environment characteristics and motion behavior characteristics of dairy goat were analyzed firstly.Secondly,the appropriate wearable multi-sensor equipment was selected.Then,the appropriate wearing position was selected and a reasonable data acquisition scheme was formulated.The six kinds of behavior data of dairy goat,including standing,lying,walking,running,jumping and turning,were collected.Finally,the wavelet filtering algorithm was used to reduce the noise of the collected data,and the Z-score algorithm was used to normalize the data to form the standard data set of dairy goat motion behavior.It provides a solid and reliable data foundation for dairy goat motion behavior recognition.(2)Data enhancement of dairy goat behavior sensor based on improved generative adversarial network.In order to solve the problem of imbalanced data categories in the collected behavioral data set of dairy goats,an improved generation confrontation networkbased behavioral data enhancement algorithm model of dairy goats was proposed.Onedimensional convolution layer and two-way long-short-term memory neural network layer were introduced into the generator and the discriminator to ensure the temporal correlation of the generated data and reduce the problems of gradient explosion and gradient disappearance in the training process.The reliability of the generated data was verified by the similarity evaluation of chords.Different algorithm models were used to conduct behavior recognition experiments based on the original data set and the data set after data enhancement.The experimental results show that the data generated by the model are of high quality.It can effectively improve the behavior recognition accuracy of the model.(3)Motion behavior recognition of dairy goat based on dual attention time convolution network.In view of the long time sequence and large amount of data of wearable multi-sensors,a dairy goat behavior recognition algorithm model based on dual-attention time convolution network is proposed.The dual-module attention is introduced into the time convolution network to ensure that the model can more effectively extract the deep sequence feature information in the time sequence sensor data.At the same time,the use of the triple loss function can better distinguish the similar heterogeneous behaviors of the sensor data.The experimental results show that the model can effectively identify the six behaviors of dairy goats,including standing,lying,walking,running,jumping and turning.The average accuracy rate of behavior recognition of the model can reach 97.15 %.