Abstract:Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to in… Show more
“…Although WiFi based techniques can measure human vital signs with off-the-shelf WiFi devices, the accuracy is easily affected by the surrounding environment, because of broadcasting nature and long range of WiFi transmissions. To address this issue, some RFID based systems like TagBreathe are developed to track human respiration by analyzing the RFID response data collected at an RFID reader [19]. Since the passive UHF RFID tags are low-cost and are easily attachable to human body, the RFID system can monitor human vital signs at a low cost and is resilient to interference from the unstable environment.…”
Section: Related Researchmentioning
confidence: 99%
“…The main challenge for extracting the breathing signal from the RFID phase measurements is how to mitigate the discontinuity in phase data, which is caused by channel hopping. One way to eliminate the channel hopping influence, as proposed for the TagBreathe system [19], is to group the signals collected from the same channel and to use the estimated displacement in each channel to track the breathing signal. As discussed earlier, this method may not work well for RFID systems in the US, since the reader must hop among 50 different frequencies, following the FCC requirement.…”
Section: Preliminaries Of Rfid Snsingmentioning
confidence: 99%
“…Some RFID based systems are proposed to achieve low cost as well as reducing the influence of unstable surroundings. Multiple RFID based techniques have been developed for object tracking [14], orientation estimation [15], drones [16]- [18], and especially, for respiration monitoring [19]. Such existing works mainly make use of the RFID phase information collected from the RFID reader on different channels.…”
Section: Introductionmentioning
confidence: 99%
“…Such existing works mainly make use of the RFID phase information collected from the RFID reader on different channels. One such typical techniques for smart healthcare is called TagBreathe, which monitors the respiration signal of a patient by grouping the RFID responses collected from the same channel and using a estimated displacement in each channel [19]. This method may not be well suited for operation with Ultra High Frequency (UHF) RFID devices in the US, which all adopt frequency hopping over 50 channels, 200 ms per channel, according to FCC requirements.…”
With the rapid development of intelligent health sensing in the Internet of Things (IoT), vital sign monitoring (e.g., respiration) and abnormal respiration detection have attracted increasing attention. Considering the challenging and the cost of collecting labeled training data from patients with breathing related diseases, we develop the AutoTag system, an unsupervised recurrent variational autoencoder-based method for respiration rate estimation and abnormal breathing detection with off-the-shelf RFID tags. Moreover, for real-time breath monitoring, a novel method is proposed to cancel the distortion on measured phase values caused by channel hopping for FCC-complaint RFID systems. The efficacy of the proposed system is demonstrated by the extensive experiments conducted in two indoor environments, while the impact of various design and environmental factors is also evaluated.INDEX TERMS Apnea, deep learning, radio-frequency identification (RFID), recurrent variational autoencoder, respiration monitoring.
“…Although WiFi based techniques can measure human vital signs with off-the-shelf WiFi devices, the accuracy is easily affected by the surrounding environment, because of broadcasting nature and long range of WiFi transmissions. To address this issue, some RFID based systems like TagBreathe are developed to track human respiration by analyzing the RFID response data collected at an RFID reader [19]. Since the passive UHF RFID tags are low-cost and are easily attachable to human body, the RFID system can monitor human vital signs at a low cost and is resilient to interference from the unstable environment.…”
Section: Related Researchmentioning
confidence: 99%
“…The main challenge for extracting the breathing signal from the RFID phase measurements is how to mitigate the discontinuity in phase data, which is caused by channel hopping. One way to eliminate the channel hopping influence, as proposed for the TagBreathe system [19], is to group the signals collected from the same channel and to use the estimated displacement in each channel to track the breathing signal. As discussed earlier, this method may not work well for RFID systems in the US, since the reader must hop among 50 different frequencies, following the FCC requirement.…”
Section: Preliminaries Of Rfid Snsingmentioning
confidence: 99%
“…Some RFID based systems are proposed to achieve low cost as well as reducing the influence of unstable surroundings. Multiple RFID based techniques have been developed for object tracking [14], orientation estimation [15], drones [16]- [18], and especially, for respiration monitoring [19]. Such existing works mainly make use of the RFID phase information collected from the RFID reader on different channels.…”
Section: Introductionmentioning
confidence: 99%
“…Such existing works mainly make use of the RFID phase information collected from the RFID reader on different channels. One such typical techniques for smart healthcare is called TagBreathe, which monitors the respiration signal of a patient by grouping the RFID responses collected from the same channel and using a estimated displacement in each channel [19]. This method may not be well suited for operation with Ultra High Frequency (UHF) RFID devices in the US, which all adopt frequency hopping over 50 channels, 200 ms per channel, according to FCC requirements.…”
With the rapid development of intelligent health sensing in the Internet of Things (IoT), vital sign monitoring (e.g., respiration) and abnormal respiration detection have attracted increasing attention. Considering the challenging and the cost of collecting labeled training data from patients with breathing related diseases, we develop the AutoTag system, an unsupervised recurrent variational autoencoder-based method for respiration rate estimation and abnormal breathing detection with off-the-shelf RFID tags. Moreover, for real-time breath monitoring, a novel method is proposed to cancel the distortion on measured phase values caused by channel hopping for FCC-complaint RFID systems. The efficacy of the proposed system is demonstrated by the extensive experiments conducted in two indoor environments, while the impact of various design and environmental factors is also evaluated.INDEX TERMS Apnea, deep learning, radio-frequency identification (RFID), recurrent variational autoencoder, respiration monitoring.
“…Human activity detection is a crucial part in human activity monitoring. Traditionally, camera systems or on-body sensors, such infrared sensors or accelerometer/gyroscopes, and FMCW radar [1]- [4], have been utilized to detect human activity, however, these methods comes with drawbacks [5]- [8]. For video monitoring, extensive amount of computational power is required to process the video received, and cameras have security and privacy concerns…”
This paper explores antenna's polarization for Radio-Frequency Identification (RFID) based non-contact human activity detection. For the first time, a cross circular polarization configuration between reader antenna and tag antenna is proposed to increase the sensing range and spatial sensitivity. Compared to conventional RFID configurations-linearly polarized (LP) tag and circularly polarized (CP) reader, a cross CP configuration can improve the signal-to-noise ratio (SNR) which leads to 230% increase in the detection area over traditional approach in our experiment. As the result of the improvement of spatial sensitivity, the proposed approach can detect subtle and small body movements at almost 4.5m from the reader, such as head movements or even respiration, which enables a low-cost, easy-to-use, non-contact and non-intrusive monitoring of the elderly and disabled peoples in places like hospitals and assisted living homes.
The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.
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