  Recently, deep learning has represented an important research trend in human
activity recognition (HAR). In particular, deep convolutional neural networks
(CNNs) have achieved state-of-the-art performance on various HAR datasets. For
deep learning, improvements in performance have to heavily rely on increasing
model size or capacity to scale to larger and larger datasets, which inevitably
leads to the increase of operations. A high number of operations in deep
leaning increases computational cost and is not suitable for real-time HAR
using mobile and wearable sensors. Though shallow learning techniques often are
lightweight, they could not achieve good performance. Therefore, deep learning
methods that can balance the trade-off between accuracy and computation cost is
highly needed, which to our knowledge has seldom been researched. In this
paper, we for the first time propose a computation efficient CNN using
conditionally parametrized convolution for real-time HAR on mobile and wearable
devices. We evaluate the proposed method on four public benchmark HAR datasets
consisting of WISDM dataset, PAMAP2 dataset, UNIMIB-SHAR dataset, and
OPPORTUNITY dataset, achieving state-of-the-art accuracy without compromising
computation cost. Various ablation experiments are performed to show how such a
network with large capacity is clearly preferable to baseline while requiring a
similar amount of operations. The method can be used as a drop-in replacement
for the existing deep HAR architectures and easily deployed onto mobile and
wearable devices for real-time HAR applications.

Computer Vision and Pattern Recognition