DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

TL;DR

DeepWalking demonstrates that a deep learning model applied to smartphone sensor data can accurately estimate walking speed, offering a practical alternative to wearable sensors for various mobile applications.

Contribution

This paper introduces the first deep learning-based method for smartphone-based walking speed estimation, achieving accuracy comparable to wearable sensor solutions.

Findings

Average RMSE of 0.16m/s in speed estimation

Deep CNN effectively extracts features from accelerometer and gyroscope data

Smartphone sensors can be used for accurate walking speed estimation

Abstract

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.