  Walking speed estimation is an essential component of mobile apps in various
fields such as fitness, transportation, navigation, and health-care. Most
existing solutions are focused on specialized medical applications that utilize
body-worn motion sensors. These approaches do not serve effectively the general
use case of numerous apps where the user holding a smartphone tries to find his
or her walking speed solely based on smartphone sensors. However, existing
smartphone-based approaches fail to provide acceptable precision for walking
speed estimation. This leads to a question: is it possible to achieve
comparable speed estimation accuracy using a smartphone over wearable sensor
based obtrusive solutions?
  We find the answer from advanced neural networks. In this paper, we present
DeepWalking, the first deep learning-based walking speed estimation scheme for
smartphone. A deep convolutional neural network (DCNN) is applied to
automatically identify and extract the most effective features from the
accelerometer and gyroscope data of smartphone and to train the network model
for accurate speed estimation. Experiments are performed with 10 participants
using a treadmill. The average root-mean-squared-error (RMSE) of estimated
walking speed is 0.16m/s which is comparable to the results obtained by
state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE
of 0.11m/s). The results indicate that a smartphone can be a strong tool for
walking speed estimation if the sensor data are effectively calibrated and
supported by advanced deep learning techniques.
