  Human Activity Recognition (HAR) is one of the core research areas in mobile
and wearable computing. With the application of deep learning (DL) techniques
such as CNN, recognizing periodic or static activities (e.g, walking, lying,
cycling, etc.) has become a well studied problem. What remains a major
challenge though is the sporadic activity recognition (SAR) problem, where
activities of interest tend to be non periodic, and occur less frequently when
compared with the often large amount of irrelevant background activities.
Recent works suggested that sequential DL models (such as LSTMs) have great
potential for modeling nonperiodic behaviours, and in this paper we studied
some LSTM training strategies for SAR. Specifically, we proposed two simple yet
effective LSTM variants, namely delay model and inverse model, for two SAR
scenarios (with and without time critical requirement). For time critical SAR,
the delay model can effectively exploit predefined delay intervals (within
tolerance) in form of contextual information for improved performance. For
regular SAR task, the second proposed, inverse model can learn patterns from
the time series in an inverse manner, which can be complementary to the forward
model (i.e.,LSTM), and combining both can boost the performance. These two LSTM
variants are very practical, and they can be deemed as training strategies
without alteration of the LSTM fundamentals. We also studied some additional
LSTM training strategies, which can further improve the accuracy. We evaluated
our models on two SAR and one non-SAR datasets, and the promising results
demonstrated the effectiveness of our approaches in HAR applications.
