Lstm With Uniqueness Attention For Human Activity Recognition

Deep neural network has promoted the development of human activity recognition research and becomes an indispensable tool for it. Deep neural networks, such as LSTM, can automatically learn important features from the data of human activities. But parts of these data are irrelevant and correspond to the Null activity [3], which can affect the recognition performance. Therefore, we propose a uniqueness attention mechanism to solve this problem. Every human activity consists of many atom motions. Some of these atom motions only occur in one single human activity. This kind of motion is more effective to discriminate human activities, and should therefore receive more attention. We design a model, named LSTM with Uniqueness Attention. When we identify the category of an unknown activity, our model first discover unknown activity's atom motions which are unique to a known activity, and then use these motions to discriminate this unknown activity. In this way, irrelevant information can be filtered out. Moreover, by discovering an activity's unique atom motion, we can get more insights to understand this human activity. We evaluate our approach on two public datasets and obtain state-of-the-art results. We also visualize this uniqueness attention, which has an excellent interpretability and goes pretty well with common sense.