110 research outputs found
Fall Detection Using Channel State Information from WiFi Devices
Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme
SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals
Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive
applications in home surveillance, remote healthcare, road safety, and home
entertainment, among others. Most of the existing works are limited to the
activity classification of a single human subject at a given time. Conversely,
a more realistic scenario is to achieve simultaneous, multi-subject activity
classification. The first key challenge in that context is that the number of
classes grows exponentially with the number of subjects and activities.
Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new
environments and subjects. To address both issues, we propose SiMWiSense, the
first framework for simultaneous multi-subject activity classification based on
Wi-Fi that generalizes to multiple environments and subjects. We address the
scalability issue by using the Channel State Information (CSI) computed from
the device positioned closest to the subject. We experimentally prove this
intuition by confirming that the best accuracy is experienced when the CSI
computed by the transceiver positioned closest to the subject is used for
classification. To address the generalization issue, we develop a brand-new
few-shot learning algorithm named Feature Reusable Embedding Learning (FREL).
Through an extensive data collection campaign in 3 different environments and 3
subjects performing 20 different activities simultaneously, we demonstrate that
SiMWiSense achieves classification accuracy of up to 97%, while FREL improves
the accuracy by 85% in comparison to a traditional Convolutional Neural Network
(CNN) and up to 20% when compared to the state-of-the-art few-shot embedding
learning (FSEL), by using only 15 seconds of additional data for each class.
For reproducibility purposes, we share our 1TB dataset and code repository.Comment: This work has been accepted for publication in IEEE WoWMoM 202
The passive operating mode of the linear optical gesture sensor
The study evaluates the influence of natural light conditions on the
effectiveness of the linear optical gesture sensor, working in the presence of
ambient light only (passive mode). The orientations of the device in reference
to the light source were modified in order to verify the sensitivity of the
sensor. A criterion for the differentiation between two states: "possible
gesture" and "no gesture" was proposed. Additionally, different light
conditions and possible features were investigated, relevant for the decision
of switching between the passive and active modes of the device. The criterion
was evaluated based on the specificity and sensitivity analysis of the binary
ambient light condition classifier. The elaborated classifier predicts ambient
light conditions with the accuracy of 85.15%. Understanding the light
conditions, the hand pose can be detected. The achieved accuracy of the hand
poses classifier trained on the data obtained in the passive mode in favorable
light conditions was 98.76%. It was also shown that the passive operating mode
of the linear gesture sensor reduces the total energy consumption by 93.34%,
resulting in 0.132 mA. It was concluded that optical linear sensor could be
efficiently used in various lighting conditions.Comment: 10 pages, 14 figure
MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation
Having been studied for more than a decade, Wi-Fi human sensing still faces a
major challenge in the presence of multiple persons, simply because the limited
bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically
separate multiple subjects. Existing solutions mostly avoid this challenge by
switching to radars with GHz bandwidth, at the cost of cumbersome deployments.
Therefore, could Wi-Fi human sensing handle multiple subjects remains an open
question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing
system with physical separability. The principle behind MUSE-Fi is that, given
a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field
channel variation caused by the subject significantly overwhelms variations
caused by other distant subjects. Consequently, focusing on the channel state
information (CSI) carried by the traffic in and out of this device naturally
allows for physically separating multiple subjects. Based on this principle, we
propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI,
and iii) downlink beamforming feedback, where we specifically tackle signal
recovery from sparse (per-user) traffic under realistic multi-user
communication scenarios. Our extensive evaluations clearly demonstrate that
MUSE-Fi is able to successfully handle multi-person sensing with respect to
three typical applications: respiration monitoring, gesture detection, and
activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202
A CSI-Based Human Activity Recognition Using Deep Learning
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities
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Leveraging Electromagnetic Polarization in a Two-Antenna Whiteboard in the Air
Wireless sensing, tracking, and drawing technologies are enabling exciting new possibilities for human-machine interaction. They primarily rely on measurements of backscattered phase, amplitude, and Doppler signal distortions, and often require many measurements of these quantities---in time, or from multiple antennas. In this paper we present the design and implementation of PolarDraw, the first whiteboard in the air that sends differentially-polarized wireless signals to glean more precise tracking information from a tag. Leveraging information received from each polarization angle, our novel algorithms infer orientation and position of an RFID-tagged pen using just two antennas, when the user writes in the air or on a physical whiteboard. An experimental comparison in a cluttered indoor office environment compares two-antenna PolarDraw with recent state-of-the-art object tracking systems that use double the number of antennas, demonstrating comparable centimeter-level tracking accuracy and character recognition rates (88--94%), thus making a case for the use of polarization in many other tracking systems
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