119 research outputs found
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
The vast proliferation of sensor devices and Internet of Things enables the
applications of sensor-based activity recognition. However, there exist
substantial challenges that could influence the performance of the recognition
system in practical scenarios. Recently, as deep learning has demonstrated its
effectiveness in many areas, plenty of deep methods have been investigated to
address the challenges in activity recognition. In this study, we present a
survey of the state-of-the-art deep learning methods for sensor-based human
activity recognition. We first introduce the multi-modality of the sensory data
and provide information for public datasets that can be used for evaluation in
different challenge tasks. We then propose a new taxonomy to structure the deep
methods by challenges. Challenges and challenge-related deep methods are
summarized and analyzed to form an overview of the current research progress.
At the end of this work, we discuss the open issues and provide some insights
for future directions
MAISON -- Multimodal AI-based Sensor platform for Older Individuals
There is a global aging population requiring the need for the right tools
that can enable older adults' greater independence and the ability to age at
home, as well as assist healthcare workers. It is feasible to achieve this
objective by building predictive models that assist healthcare workers in
monitoring and analyzing older adults' behavioral, functional, and
psychological data. To develop such models, a large amount of multimodal sensor
data is typically required. In this paper, we propose MAISON, a scalable
cloud-based platform of commercially available smart devices capable of
collecting desired multimodal sensor data from older adults and patients living
in their own homes. The MAISON platform is novel due to its ability to collect
a greater variety of data modalities than the existing platforms, as well as
its new features that result in seamless data collection and ease of use for
older adults who may not be digitally literate. We demonstrated the feasibility
of the MAISON platform with two older adults discharged home from a large
rehabilitation center. The results indicate that the MAISON platform was able
to collect and store sensor data in a cloud without functional glitches or
performance degradation. This paper will also discuss the challenges faced
during the development of the platform and data collection in the homes of
older adults. MAISON is a novel platform designed to collect multimodal data
and facilitate the development of predictive models for detecting key health
indicators, including social isolation, depression, and functional decline, and
is feasible to use with older adults in the community
Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning
The current state of the art on jamming detection relies on link-layer
metrics. A few examples are the bit-error-rate (BER), the packet delivery
ratio, the throughput, and the increase in the signal-to-noise ratio (SNR). As
a result, these techniques can only detect jamming \emph{ex-post}, i.e., once
the attack has already taken down the communication link. These solutions are
unfit for mobile devices, e.g., drones, which might lose the connection to the
remote controller, being unable to predict the attack.
Our solution is rooted in the idea that a drone unknowingly flying toward a
jammed area is experiencing an increasing effect of the jamming, e.g., in terms
of BER and SNR. Therefore, drones might use the above-mentioned phenomenon to
detect jamming before the decrease of the BER and the increase of the SNR
completely disrupt the communication link. Such an approach would allow drones
and their pilots to make informed decisions and maintain complete control of
navigation, enhancing security and safety.
This paper proposes Bloodhound+, a solution for jamming detection on mobile
devices in low-BER regimes. Our approach analyzes raw physical-layer
information (I-Q samples) acquired from the wireless channel. We assemble this
information into grayscale images and use sparse autoencoders to detect image
anomalies caused by jamming attacks. To test our solution against a wide set of
configurations, we acquired a large dataset of indoor measurements using
multiple hardware, jamming strategies, and communication parameters. Our
results indicate that Bloodhound+ can detect indoor jamming up to 20 meters
from the jamming source at the minimum available relative jamming power, with a
minimum accuracy of 99.7\%. Our solution is also robust to various sampling
rates adopted by the jammer and to the type of signal used for jamming.Comment: 16 pages, 16 figures, 3 tables; Submitted and under revie
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
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