A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags

Hussain, Zawar; Sagar, Subhash; Zhang, Wei Emma; Sheng, Quan Z.

doi:10.1145/3360774.3360797

Cited by 21 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these liquid height estimation methods are too coarse to accurately detect the slight liquid height change in a short time interval. The periodic motion schemes mainly monitor the respiration and heartbeat of human subjects [3,13,14,16,25,26,28,32,34,35]. Since the water in a human body is up to 60% [2], the respiration and heartbeat can be viewed as the periodic motion of liquid.…”

Section: Related Workmentioning

confidence: 99%

DropMonitor

Lin

Xie

Wang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

As an important indicator of the infusion monitoring for clinical treatment, the drip rate is expected to be monitored in an accurate and real-time manner. However, state-of-the-art drip rate monitoring schemes either suffer from high maintenance or incur high hardware cost. In this paper, we propose DropMonitor, an RFID-based approach to perform the mm-level sensing for infusion drip rate monitoring. By attaching a pair of batteryless RFID tags on the drip chamber, we can estimate the drip rate by capturing the RF-signals reflected from the vibrating liquid surface caused by the falling droplets. Particularly, we use the sensing tag to perceive the liquid surface vibration in the drip chamber and further derive the drip rate for infusion monitoring. Moreover, to sufficiently mitigate the multi-path interference from the surrounding human activities, we use the reference tag to perceive the multi-path signals from the indoor environment. By computing the difference of RF-signals from tag pairs, we cancel the multi-path interference and extract the drip-rate-related signals. We have implemented a prototype system and evaluated its performance in real applications. The experiment results show that DropMonitor can accurately estimate the infusion drip rate, and the average relative error of drip rate estimation is below 1% for conventional cases. In this way, considering the essential sampling rates of each tag, DropMonitor is able to monitor the drip rate for over a dozen of infusion bottles/bags in parallel with one COTS RFID system.

show abstract

Section: Related Workmentioning

confidence: 99%

DropMonitor

Lin

Xie

Wang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

show abstract

“…Many studies have been conducted to analyze and understand the quality and behaviour of sleep. It has become a separate branch of medicine and has gained a lot of attention recently [78,27].…”

Section: Introductionmentioning

confidence: 99%

“…The detection of these disorders is very challenging as the patients are unaware of these disorders. One other group of studies focuses on monitoring vital signs such as respiration and heart rate during sleep [27]. Vital signs are correlated with sleep behavior and can provide important information about sleep quality and some disorders.…”

Section: Introductionmentioning

confidence: 99%

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Hussain,

Sheng,

Zhang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep which is having negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior. The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring. In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest works done using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on 10 key factors, to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also present some publicly available datasets for different categories of sleep monitoring. In the end, we discuss several open issues and provide future research directions in the area of sleep monitoring.

show abstract

“…Nowadays, numerous sensors are being developed and used to assist healthcare applications. These sensors are often used for monitoring various human activities such as sleep [5], medication adherence [1,2], and many other activities of daily life [6,13]. Using a sensor-based approach for monitoring the toothbrushing activity is a challenging task because the changes caused in toothbrushing activity are very subtle.…”

Section: Introductionmentioning

confidence: 99%

Toothbrushing data and analysis of its potential use in human activity recognition applications

Hussain

Waterworth

Aldeer

et al. 2020

Proceedings of the Third Workshop on Data: Acquisition to Analysis

Self Cite

View full text Add to dashboard Cite

In this paper, we describe and analyze a time-series dataset from toothbrushing activity using brush-attached and wearable sensors. The data was collected from 17 participants when they brushed their teeth over one week in 5 different locations. The dataset consists of 62 toothbrushing sessions for each of the brush-attached and wearable sensor approaches, using both electric and manual brushes. The average duration of each session is 2 minutes. One sensor device was attached to the handle of the brush while the other was worn by the participants as a wrist-watch. We collected the data from a 3-axis accelerometer and a 3-axis gyroscope at a 200 Hz sampling rate. Most of the data has been labelled. We investigated the characteristics of the data using spectral analysis and performed a pre-processing pipeline in order to generate features used to train a Support Vector Machine Classifier. We were able to identify which part of the jaw was being brushed with 98.6% accuracy. CCS CONCEPTS • Information systems → Data mining; • Computer systems organization → Embedded and cyber-physical systems; • Theory of computation → Machine learning theory.

show abstract

A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags

Cited by 21 publications

References 30 publications

DropMonitor

DropMonitor

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Toothbrushing data and analysis of its potential use in human activity recognition applications

Contact Info

Product

Resources

About