29,308 research outputs found
Wearable Fall Detector Using Recurrent Neural Networks
Falls have become a relevant public health issue due to their high prevalence and negative
effects in elderly people. Wearable fall detector devices allow the implementation of continuous
and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low
energy consumption is one of the most relevant characteristics of these devices. Recurrent neural
networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing
sequential inputs. However, getting appropriate response times in low power microcontrollers
remains a difficult task due to their limited hardware resources. This work shows a feasibility study
about using RNN-based deep learning models to detect both falls and falls’ risks in real time using
accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall
dataset at different frequencies. The resulting models were integrated into two different embedded
systems to analyze the execution times and changes in the model effectiveness. Finally, a study of
power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained.
The simplest models reached inference times lower than 34 ms, which implies the capability to
detect fall events in real-time with high energy efficiency. This suggests that RNN models provide
an effective method that can be implemented in low power microcontrollers for the creation of
autonomous wearable fall detection systems in real-time
Deep learning-based fall detection
In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring
An Analysis of Scale Invariance in Object Detection - SNIP
An analysis of different techniques for recognizing and detecting objects
under extreme scale variation is presented. Scale specific and scale invariant
design of detectors are compared by training them with different configurations
of input data. By evaluating the performance of different network architectures
for classifying small objects on ImageNet, we show that CNNs are not robust to
changes in scale. Based on this analysis, we propose to train and test
detectors on the same scales of an image-pyramid. Since small and large objects
are difficult to recognize at smaller and larger scales respectively, we
present a novel training scheme called Scale Normalization for Image Pyramids
(SNIP) which selectively back-propagates the gradients of object instances of
different sizes as a function of the image scale. On the COCO dataset, our
single model performance is 45.7% and an ensemble of 3 networks obtains an mAP
of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train
with bounding box supervision. Our submission won the Best Student Entry in the
COCO 2017 challenge. Code will be made available at
\url{http://bit.ly/2yXVg4c}.Comment: CVPR 2018, camera ready versio
Novel deep learning methods for track reconstruction
For the past year, the HEP.TrkX project has been investigating machine
learning solutions to LHC particle track reconstruction problems. A variety of
models were studied that drew inspiration from computer vision applications and
operated on an image-like representation of tracking detector data. While these
approaches have shown some promise, image-based methods face challenges in
scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In
contrast, models that can operate on the spacepoint representation of track
measurements ("hits") can exploit the structure of the data to solve tasks
efficiently. In this paper we will show two sets of new deep learning models
for reconstructing tracks using space-point data arranged as sequences or
connected graphs. In the first set of models, Recurrent Neural Networks (RNNs)
are used to extrapolate, build, and evaluate track candidates akin to Kalman
Filter algorithms. Such models can express their own uncertainty when trained
with an appropriate likelihood loss function. The second set of models use
Graph Neural Networks (GNNs) for the tasks of hit classification and segment
classification. These models read a graph of connected hits and compute
features on the nodes and edges. They adaptively learn which hit connections
are important and which are spurious. The models are scaleable with simple
architecture and relatively few parameters. Results for all models will be
presented on ACTS generic detector simulated data.Comment: CTD 2018 proceeding
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
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