29,364 research outputs found
Ambient health monitoring: the smartphone as a body sensor network component
Inertial measurement units used in commercial body sensor networks (e.g. animation suits) are inefficient, difficult to use and expensive when adapted for movement science applications concerning medical and sports science. However, due to advances in micro-electro mechanical sensors, these inertial sensors have become ubiquitous in mobile computing technologies such as smartphones. Smartphones generally use inertial sensors to enhance the interface usability. This paper investigates the use of a smartphone’s inertial sensing capability as a component in body sensor networks. It discusses several topics centered on inertial sensing: body sensor networks, smartphone networks and a prototype framework for integrating these and other heterogeneous devices. The proposed solution is a smartphone application that gathers, processes and filters sensor data for the purpose of tracking physical activity. All networking functionality is achieved by Skeletrix, a framework for gathering and organizing motion data in online repositories that are conveniently accessible to researchers, healthcare professionals and medical care workers
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
SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network
Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults. Red, green, and blue pixel intensities were estimated for each of 100 area blocks in each frame and the patterns across the 300 frames were described. ANN was then used to develop a model using the extracted video features to predict hemoglobin levels. In our study sample, with patients 20-56 years of age, and gold standard hemoglobin levels of 7.6 to 13.5 g/dL., we observed a 0.93 rank order of correlation between model and gold standard hemoglobin levels. Moreover, we identified specific regions of interest in the video images which reduced the required feature space
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