3 research outputs found
DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning
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.Comment: 6 pages, 9 figures, published in IEEE Global Communications
Conference (GLOBECOM
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
The number of pedestrian accidents continues to keep climbing. Distraction
from smartphone is one of the biggest causes for pedestrian fatalities. In this
paper, we develop SaferCross, a mobile system based on the embedded sensors of
smartphone to improve pedestrian safety by preventing distraction from
smartphone. SaferCross adopts a holistic approach by identifying and developing
essential system components that are missing in existing systems and
integrating the system components into a "fully-functioning" mobile system for
pedestrian safety. Specifically, we create algorithms for improving the
accuracy and energy efficiency of pedestrian positioning, effectiveness of
phone activity detection, and real-time risk assessment. We demonstrate that
SaferCross, through systematic integration of the developed algorithms,
performs situation awareness effectively and provides a timely warning to the
pedestrian based on the information obtained from smartphone sensors and Direct
Wi-Fi-based peer-to-peer communication with approaching cars. Extensive
experiments are conducted in a department parking lot for both component-level
and integrated testing. The results demonstrate that the energy efficiency and
positioning accuracy of SaferCross are improved by 52% and 72% on average
compared with existing solutions with missing support for positioning accuracy
and energy efficiency, and the phone-viewing event detection accuracy is over
90%. The integrated test results show that SaferCross alerts the pedestrian
timely with an average error of 1.6sec in comparison with the ground truth
data, which can be easily compensated by configuring the system to fire an
alert message a couple of seconds early.Comment: Published in IEEE Access, 202
Design, Implementation and a Pilot Study of Mobile Framework for Pedestrian Safety Using Smartphone Sensors
Pedestrian distraction from smartphones is a serious social problem that caused an ever increasing number of fatalities especially as virtual reality (VR) games have gained popularity recently. In this thesis, we present the design, implementation, and a pilot study of WiPedCross, a WiFi direct-based pedestrian safety system that senses and evaluates a risk, and alerts accordingly the user to prevent traffic accidents. In order to develop a non-intrusive, accurate, and energy-efficient pedestrian safety system, a number of technical challenges are addressed: to enhance the positioning accuracy of the user for precise risk assessment, a map-matching algorithm based on a Hidden Markov Model is designed; to minimize energy consumption, an adaptive scheme is developed that dynamically activates the GPS module of a phone according to pedestrian walking speed and the locations of nearby crosswalks; to suppress false alarms, a novel algorithm is developed to accurately identify the user-phone-viewing activity so that collision probability assessment is triggered only when the pedestrian is walking while viewing his or her phone. The prototype of the proposed framework is implemented on an Android platform for a pilot study to evaluate feasibility, reliability, and validity of WiPedCross. Extensive experiments are performed in a parking lot and the results demonstrate that WiPedCross assesses the collision probability effectively and provides warning to the user in a timely manner. The system modules of the proposed framework are expected to benefit numerous other pedestrian safety apps