Hello everyone it is my honor to present our work at Ubicomp 2021
My name is Wenzhong Li from Nanjing University
First I will introduce the research problem. Nowadays we have a lot of wearable devices
such as bracelets or smartphones. Those devices will generate a lot of sensing data and we can use
this data to extract features and use this feature to train a machine learning model,
which can be used to infer different kinds of human activities such as walking, running, or falling
There are many methods for human activity recognition.
The supervised learning method typically requires a large amount of label data and annotating those
data is time consuming and expensive, and it is infeasible for some situations. Therefore there
are some semi-supervised or unsupervised learning method. However those methods
have several drawbacks such as the extracted features
could be unfriendly to the classification task and the performance is poor. So to address
these issues our work proposed an end-to-end deep clustering method for human activity recognition
The overview of the proposed framework is illustrated in this figure. It
consists of three parts: the autoencoder part which is used for feature extraction,
the clustering part which is used to generate pseudo labels, and the classifier
which is a deep neural network that is used to output the recognition results.
The first part is an autoencoder structure. It takes the raw data as input to learn a latent
representation to reconstruct the signals. So the objective is to minimize the reconstruction error.
The second part is a k-means deep clustering method. it is similar to the conventional k-means
algorithm but we revise the algorithm to fit for deep learning method. So the objective is to
minimize the distance between the latent representation and their cluster centers.
The third part is a deep dnn classifier. It is a two-layer fully connected neural network.
It takes the latent representation and the pseudo labels as input to train a classifier
which intends to minimize the classification error.
All three parts of loss functions are combined together to form an end-to-end
model which is trained with a multi-task learning algorithm.
We conduct experiment to evaluate its performance it is based on three well-known datasets
and we compare our method with the baseline algorithms
from both supervised learning and unsupervised learning.
Here is the performance
of different algorithms. In this table we can show that in an unsupervised learning environment our
method significantly outperforms the shallow clustering and the autoencoder,
and it observed up to 30 percent performance improvement. If we retrain our model with some
labels, it has comparable performance with the state-of-the-art deep learning supervised methods.
We visualize the result of different clustering methods.
It is shown that the conventional clustering methods
are mixed up together, but our method is clearly more discriminative and clustering-friendly.
We further conduct ablation study which shows that with the multi-tasks learning and joint-training,
they do improve deep overall performance.
We then study the transferability of the proposed method. We apply the method trained from MobiAct
to other two datasets, It is showed that the performance still well with the transferred model.
Okay that's all for my presentation. Thank you for your attention!
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