1,619 research outputs found
Deep representation learning for human motion prediction and classification
Generative models of 3D human motion are often restricted to a small number
of activities and can therefore not generalize well to novel movements or
applications. In this work we propose a deep learning framework for human
motion capture data that learns a generic representation from a large corpus of
motion capture data and generalizes well to new, unseen, motions. Using an
encoding-decoding network that learns to predict future 3D poses from the most
recent past, we extract a feature representation of human motion. Most work on
deep learning for sequence prediction focuses on video and speech. Since
skeletal data has a different structure, we present and evaluate different
network architectures that make different assumptions about time dependencies
and limb correlations. To quantify the learned features, we use the output of
different layers for action classification and visualize the receptive fields
of the network units. Our method outperforms the recent state of the art in
skeletal motion prediction even though these use action specific training data.
Our results show that deep feedforward networks, trained from a generic mocap
database, can successfully be used for feature extraction from human motion
data and that this representation can be used as a foundation for
classification and prediction.Comment: This paper is published at the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 201
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles
Machine learning shows remarkable success for recognizing patterns in data.
Here we apply the machine learning (ML) for the diagnosis of early stage
diabetes, which is known as a challenging task in medicine. Blood glucose
levels are tightly regulated by two counter-regulatory hormones, insulin and
glucagon, and the failure of the glucose homeostasis leads to the common
metabolic disease, diabetes mellitus. It is a chronic disease that has a long
latent period the complicates detection of the disease at an early stage. The
vast majority of diabetics result from that diminished effectiveness of insulin
action. The insulin resistance must modify the temporal profile of blood
glucose. Thus we propose to use ML to detect the subtle change in the temporal
pattern of glucose concentration. Time series data of blood glucose with
sufficient resolution is currently unavailable, so we confirm the proposal
using synthetic data of glucose profiles produced by a biophysical model that
considers the glucose regulation and hormone action. Multi-layered perceptrons,
convolutional neural networks, and recurrent neural networks all identified the
degree of insulin resistance with high accuracy above .Comment: 4 pages, 2 figur
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