  Recently, convolutional neural networks (CNNs) have set latest
state-of-the-art on various human activity recognition (HAR) datasets. However,
deep CNNs often require more computing resources, which limits their
applications in embedded HAR. Although many successful methods have been
proposed to reduce memory and FLOPs of CNNs, they often involve special network
architectures designed for visual tasks, which are not suitable for deep HAR
tasks with time series sensor signals, due to remarkable discrepancy.
Therefore, it is necessary to develop lightweight deep models to perform HAR.
As filter is the basic unit in constructing CNNs, it deserves further research
whether re-designing smaller filters is applicable for deep HAR. In the paper,
inspired by the idea, we proposed a lightweight CNN using Lego filters for HAR.
A set of lower-dimensional filters is used as Lego bricks to be stacked for
conventional filters, which does not rely on any special network structure. The
local loss function is used to train model. To our knowledge, this is the first
paper that proposes lightweight CNN for HAR in ubiquitous and wearable
computing arena. The experiment results on five public HAR datasets, UCI-HAR
dataset, OPPORTUNITY dataset, UNIMIB-SHAR dataset, PAMAP2 dataset, and WISDM
dataset collected from either smartphones or multiple sensor nodes, indicate
that our novel Lego CNN with local loss can greatly reduce memory and
computation cost over CNN, while achieving higher accuracy. That is to say, the
proposed model is smaller, faster and more accurate. Finally, we evaluate the
actual performance on an Android smartphone.
