SparseSense: Human Activity Recognition from Highly Sparse Sensor
Data-streams Using Set-based Neural Networks
Abstract
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 characterized 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 realworld 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
visualization of the learned activity feature spaces