1,462 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
Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment
Phone sensors could be useful in assessing changes in gait that occur with
alcohol consumption. This study determined (1) feasibility of collecting
gait-related data during drinking occasions in the natural environment, and (2)
how gait-related features measured by phone sensors relate to estimated blood
alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to
complete a 5-step gait task every hour from 8pm to 12am over four consecutive
weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data
from phone sensors, and computed 24 gait-related features using a sliding
window technique. eBAC levels were calculated at each time point based on
Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial
neural network model to analyze associations between sensor features and eBACs
in training (70% of the data) and validation and test (30% of the data)
datasets. We analyzed 128 data points where both eBAC and gait-related sensor
data was captured, either when not drinking (n=60), while eBAC was ascending
(n=55) or eBAC was descending (n=13). 21 data points were captured at times
when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian
regularized neural network, gait-related phone sensor features showed a high
correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would
fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect
gait-related data from smartphone sensors during drinking occasions in the
natural environment. Sensor-based features can be used to infer gait changes
associated with elevated blood alcohol content
Reliable and robust detection of freezing of gait episodes with wearable electronic devices
A wearable wireless sensing system for assisting patients affected by Parkinson's disease is proposed. It uses integrated micro-electro-mechanical inertial sensors able to recognize the episodes of involuntary gait freezing. The system operates in real time and is designed for outdoor and indoor applications. Standard tests were performed on a noticeable number of patients and healthy persons and the algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors. The overall performances of the system are excellent with a specificity higher than 97%
Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system
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