Multitemporal Sampling Module for Real-Time Human Activity Recognition

Human activity recognition, which recognizes human activities from time-series signals collected by sensors, is an important task in human-centered intelligent systems such as in healthcare and smart vehicles. In these applications, rapid response of the system is necessary because critical events such as an elderly person falling or drowsy driving require immediate action. A straightforward approach to achieving this requirement is to reduce the amount of information the model must handle. To this end, traditional studies have attempted to abstract the original signal by sampling it with a pre-defined interval. However, it is difficult to achieve the best efficiency because the ideal sampling interval is unknown in advance. In this study, we propose a multi-temporal sampling module that allows the neural networks to consider multiple sampling intervals simultaneously. Experiments on four benchmark datasets showed that the proposed model achieved the best F1 score over seven conventional models under the computation budget of 10M multiply-accumulate operations. Especially, an experiment on PAMAP2 dataset demonstrated that the proposed model can achieve the best trade-off between efficiency and accuracy when the input signal is oversampled at a high sampling frequency. In addition, the proposed model achieved
$\sim 1,000 \times $
improvement with respect to model size compared to the conventional methods.