  Batteryless or so called passive wearables are providing new and innovative
methods for human activity recognition (HAR), especially in healthcare
applications for older people. Passive sensors are low cost, lightweight,
unobtrusive and desirably disposable; attractive attributes for healthcare
applications in hospitals and nursing homes. Despite the compelling
propositions for sensing applications, the data streams from these sensors are
characterised by high sparsity---the time intervals between sensor readings are
irregular while the number of readings per unit time are often limited. In this
paper, we rigorously explore the problem of learning activity recognition
models from temporally sparse data. We describe how to learn directly from
sparse data using a deep learning paradigm in an end-to-end manner. We
demonstrate significant classification performance improvements on real-world
passive sensor datasets from older people over the state-of-the-art deep
learning human activity recognition models. Further, we provide insights into
the model's behaviour through complementary experiments on a benchmark dataset
and visualisation of the learned activity feature spaces.
