CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy
Convolution Neural Network
- URL: http://arxiv.org/abs/2306.01756v1
- Date: Wed, 24 May 2023 04:02:49 GMT
- Title: CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy
Convolution Neural Network
- Authors: Jingtao Guo, Ivan Wang-Hei Ho
- Abstract summary: We propose a Wi-Fi-based device-free self-quarantine monitoring system.
We exploit channel state information (CSI) derived from Wi-Fi signals as human activity features.
Our experimental results indicate that the proposed model can achieve an average accuracy of 98.19% for classifying five different human activities.
- Score: 2.609279398946235
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, Coronavirus disease (COVID-19) has become a global pandemic because
of its fast spread in various countries. To build an anti-epidemic barrier,
self-isolation is required for people who have been to any at-risk places or
have been in close contact with infected people. However, existing camera or
wearable device-based monitoring systems may present privacy leakage risks or
cause user inconvenience in some cases. In this paper, we propose a Wi-Fi-based
device-free self-quarantine monitoring system. Specifically, we exploit channel
state information (CSI) derived from Wi-Fi signals as human activity features.
We collect CSI data in a simulated self-quarantine scenario and present
BranchyGhostNet, a lightweight convolution neural network (CNN) with an early
exit prediction branch, for the efficient joint task of room occupancy
detection (ROD) and human activity recognition (HAR). The early exiting branch
is used for ROD, and the final one is used for HAR. Our experimental results
indicate that the proposed model can achieve an average accuracy of 98.19% for
classifying five different human activities. They also confirm that after
leveraging the early exit prediction mechanism, the inference latency for ROD
can be significantly reduced by 54.04% when compared with the final exiting
branch while guaranteeing the accuracy of ROD.
Related papers
- ORCHID: Streaming Threat Detection over Versioned Provenance Graphs [11.783370157959968]
We present ORCHID, a novel Prov-IDS that performs fine-grained detection of process-level threats over a real time event stream.
ORCHID takes advantage of the unique immutable properties of a versioned provenance graphs to iteratively embed the entire graph in a sequential RNN model.
We evaluate ORCHID on four public datasets, including DARPA TC, to show that ORCHID can provide competitive classification performance.
arXiv Detail & Related papers (2024-08-23T19:44:40Z) - DNA: Differentially private Neural Augmentation for contact tracing [62.740950398187664]
Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early.
We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing.
This work marks an important first step in integrating deep learning into contact tracing while maintaining essential privacy guarantees.
arXiv Detail & Related papers (2024-04-20T13:43:28Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - TeD-SPAD: Temporal Distinctiveness for Self-supervised
Privacy-preservation for video Anomaly Detection [59.04634695294402]
Video anomaly detection (VAD) without human monitoring is a complex computer vision task.
Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information.
We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
arXiv Detail & Related papers (2023-08-21T22:42:55Z) - Time-Selective RNN for Device-Free Multi-Room Human Presence Detection
Using WiFi CSI [9.927073290898848]
Device-free human presence detection is crucial technology for various applications, including home automation, security, and healthcare.
Recent research has explored the use of wireless channel state information extracted from commercial WiFi access points (APs) to provide detailed channel characteristics.
We propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent network.
arXiv Detail & Related papers (2023-04-25T19:21:47Z) - Attention-Enhanced Deep Learning for Device-Free Through-the-Wall
Presence Detection Using Indoor WiFi Systems [9.087163485833054]
We propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals.
Our system named attention-enhanced deep learning for presence detection (ALPD) employs an attention mechanism to automatically select informative subcarriers from the CSI data.
We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks.
arXiv Detail & Related papers (2023-04-25T19:17:36Z) - CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human
Presence Detection using Wi-Fi CSI [9.927073290898848]
Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people.
We propose a system called CRONOS, which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people.
arXiv Detail & Related papers (2022-11-07T16:18:18Z) - Self-Supervised Masked Convolutional Transformer Block for Anomaly
Detection [122.4894940892536]
We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level.
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
arXiv Detail & Related papers (2022-09-25T04:56:10Z) - New Normal: Cooperative Paradigm for Covid-19 Timely Detection and
Containment using Internet of Things and Deep Learning [12.618653234201089]
The spread of the novel coronavirus (COVID-19) has caused trillions of dollars in damages to the governments and health authorities by affecting the global economies.
This study introduces a connected smart paradigm that not only detects the possible spread of viruses but also helps to restart businesses/economies, and resume social life.
arXiv Detail & Related papers (2020-08-15T14:33:53Z) - Decentralized Privacy-Preserving Proximity Tracing [50.27258414960402]
DP3T provides a technological foundation to help slow the spread of SARS-CoV-2.
System aims to minimise privacy and security risks for individuals and communities.
arXiv Detail & Related papers (2020-05-25T12:32:02Z) - Firearm Detection and Segmentation Using an Ensemble of Semantic Neural
Networks [62.997667081978825]
We present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks.
A set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel.
The overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives.
arXiv Detail & Related papers (2020-02-11T13:58:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.