  With the popularity and development of the wearable devices such as
smartphones, human activity recognition (HAR) based on sensors has become as a
key research area in human computer interaction and ubiquitous computing. The
emergence of deep learning leads to a recent shift in the research of HAR,
which requires massive strictly labeled data. In comparison with video data,
activity data recorded from accelerometer or gyroscope is often more difficult
to interpret and segment. Recently, several attention mechanisms are proposed
to handle the weakly labeled human activity data, which do not require accurate
data annotation. However, these attention-based models can only handle the
weakly labeled dataset whose sample includes one target activity, as a result
it limits efficiency and practicality. In the paper, we propose a recurrent
attention networks (RAN) to handle sequential weakly labeled multi-activity
recognition and location tasks. The model can repeatedly perform steps of
attention on multiple activities of one sample and each step is corresponding
to the current focused activity. The effectiveness of the RAN model is
validated on a collected sequential weakly labeled multi-activity dataset and
the other two public datasets. The experiment results show that our RAN model
can simultaneously infer multi-activity types from the coarse-grained
sequential weak labels and determine specific locations of every target
activity with only knowledge of which types of activities contained in the long
sequence. It will greatly reduce the burden of manual labeling. The code of our
work is available at https://github.com/KennCoder7/RAN.
