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Monitoring Bodily Oscillation With RFID Tags

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TLDR
This paper designs a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson’s disease.
Abstract
Traditional systems for monitoring and diagnosing patients’ health conditions often require either dedicated medical devices or complicated system deployment, which incurs high cost. The networking research community has recently taken a different technical approach of building health-monitoring systems at relatively low cost based on wireless signals. However, the radio frequency signals carry various types of noise and have time-varying properties that often defy the existing methods in more demanding conditions with other body movements, which makes it difficult to model and analyze the signals mathematically. In this paper, we design a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson’s disease. Our system includes a series of noise-removal steps, targeting at noise from different sources. More importantly, it introduces two sliding window-based methods to deal with time-varying signal properties from channel dynamics and irregular body movement. The proposed system can measure bodily oscillation rates of multiple persons simultaneously. Extensive experiments show that our system can produce accurate measurement results with errors less than 0.4 oscillations per second when it is applied to monitor hand tremor, even when the individuals are moving.

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Citations
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Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

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Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

TL;DR: A multisignal frequency domain detection and recognition method that can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal is proposed.
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Cost-Effective Wireless Sensors for Detection of Package Opening and Tampering

TL;DR: Three different battery-free, cost-effective, Radio-Frequency Identification (RFID)-based solutions for the detection of tampering or package opening using a printed switch that shunts the antenna of the RFID tag are presented.
Proceedings ArticleDOI

Security Monitoring System Using Magnetically-Activated RFID Tags

TL;DR: A battery-free solution that leverages any off-the-shelf passive radio frequency identification (RFID) tag for real-time entry detection and simulation and experimental results for such magnetically-actuated RFID opening sensor are presented.
References
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Tool release: gathering 802.11n traces with channel state information

TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Journal ArticleDOI

American Cancer Society Guidelines for the Early Detection of Cancer

TL;DR: The public is urged to take action to protect themselves and their loved ones from disease-causing substances.
Proceedings ArticleDOI

Sensor networks for medical care

TL;DR: The experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue, which provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring are described.
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