  Wearable computing and context awareness are the focuses of study in the
field of artificial intelligence recently. One of the most appealing as well as
challenging applications is the Human Activity Recognition (HAR) utilizing
smart phones. Conventional HAR based on Support Vector Machine relies on
subjective manually extracted features. This approach is time and energy
consuming as well as immature in prediction due to the partial view toward
which features to be extracted by human. With the rise of deep learning,
artificial intelligence has been making progress toward being a mature
technology. This paper proposes a new approach based on deep learning and
traditional feature engineering called HAR-Net to address the issue related to
HAR. The study used the data collected by gyroscopes and acceleration sensors
in android smart phones. The raw sensor data was put into the HAR-Net proposed.
The HAR-Net fusing the hand-crafted features and high-level features extracted
from convolutional network to make prediction. The performance of the proposed
method was proved to be 0.9% higher than the original MC-SVM approach. The
experimental results on the UCI dataset demonstrate that fusing the two kinds
of features can make up for the shortage of traditional feature engineering and
deep learning techniques.
