This is the short presentation of METIER.
In this study we solve activity recognition and user recognition tasks jointly
First, let me introduce some existing problems. For activity recognition, most methods merge all the data from all training users.
The variations in how users perform activities are usually ignored. Because of that variation, activity recognition accuracy usually drops when a well-trained model is applied to new users.
Secondly, for user recognition, most methods can identify users only by walking data, which limits its application in daily life.
Extending user recognition from walking to other activities is valuable but also challenging, as there are significant differences between the sensor data in different activities.
So, to address the problems mentioned above, we propose METIER, which solves user and activity recognition jointly.
For activity recognition, by introducing user-related knowledge, it can take users' characteristics into account.
For user recognition, with the guidance of activity-related knowledge, it can automatically adapt to current activity.
This is the overall framework of METIER. It contains two netw- orks, activity recognition and user recognition networks, there will be some interactions between them.
In each network, a time window first goes through a convolutional neural network, and then a bidirectional long short-term memory layer.
Finally, attention mechanism will highlight important features by weights, and these weighted features will be used for classificat- ion.
METIER softly shares parameters between activity and user recogn- ition networks, and optimizes them together. Next up I will descri- be the network structure with more details.
Specifically, a time window is transformed through two convolutio- nal layers, one max pooling layer and then one more convolutional layer in the convolutional neural networks.
Note that here, we will be using a tensor decomposition based stra- tegy to softly share parameters between the convolutional layers of activity and user recognition networks,
so that the commonalities and differences across tasks will be exploited.
Then, a single bidirectional Long Short Term Memory layer will be used to capture the forward and backward temporal dependencies.
It takes the flattened outputs of convolutional neural network as its input,
then it process the input step by step from two directions, finally it concatenates the outputs into task-specific features vectors.
Finally, we introduce mutual attention to deal with the task- specific feature vectors that we have obtained.
Mutual attention help us exploit the knowledge from one task to highlight the important features of another.
So here, for activity recognition, it learns a mapping from user features to the weights of activity features, which highlights the features that are suit to the current user.
And also, for user recognition, it learns a mapping from activity features to the weights of user features, and adjusts these weights according to the current activity scenario.
Finally, these weighted features will be used for classification. And here is the entire structure of our network.
In order to demonstrate the competitive performance of METIER, we compare it with the state-of-the-art activity and user recognition models.
In our experiments, METIER outperforms all other activity and user recognition models, showing the strength of exploiting mutual knowledge.
For more details, you can find answers in our paper. Also, we' ll be happy to answer your questions.
Thank you so much for listening!!!