Monitoring body positions and movements during sleep using WISPs

Hoque, Enamul; Dickerson, Robert; Stankovic, John A.

doi:10.1145/1921081.1921088

Cited by 101 publications

(39 citation statements)

References 15 publications

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“…In [17], the authors used pressure sensor matrix to monitor the elders sleep behavior in bed. Enamul Hoque et al [11] presented a sleep monitoring system based on active RFID-based sensors equipped with accelerometers. They evaluated their system for six nights on one subject.…”

Section: Related Workmentioning

confidence: 99%

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Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Eldib

Deboeverie

Philips

et al. 2015

Human Behavior Understanding

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Abstract. Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 × 30 pixels) for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10 months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error (MAE) of 22.91 minutes and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights.

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Section: Related Workmentioning

confidence: 99%

“…Also, no visual sensor has been installed inside the bedroom for sleep analysis. In [16,4,9,11], they performed sleep analysis on small datasets (several days) and the datasets were captured in lab environments. On the contrary, we perform long-term sleep analysis on 10 months of real-data recordings in a real service flat setup.…”

Section: Related Workmentioning

confidence: 99%

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Eldib

Deboeverie

Philips

et al. 2015

Human Behavior Understanding

View full text Add to dashboard Cite

show abstract

“…The authors show one of the applications of their system which detects sleep apnea. [7] presents a sleep monitoring system based on the WISP platform-active RFID-based sensors equipped with accelerometers. The system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress.…”

Section: Related Workmentioning

confidence: 99%

“…Sleep is the best medicine, the traditional Chinese medicine praises and advocates highly. Most of existing works [12,7] related to sleep quality are concerned about how to effectively measure sleep quality in clinic. And, a few works [19,16] demonstrate the relationship between people's sleep quality and their contexts.…”

Section: Introductionmentioning

confidence: 99%

Will You Have a Good Sleep Tonight? Sleep Quality Prediction with Mobile Phone

bai¹,

Xu²,

Ma³

et al. 2012

Proceedings of the 7th International Conference on Body Area Networks

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Understanding the relationship between sleep and daily life can provide insights into a healthy life style since the sleep quality is one of the most important indicators of people's health status. This paper studies the extent to which a person's sleep quality can be predicted by his/her daily context information. A combination of the machine learning technology and medical knowledge is used to study the relation between context and sleep quality, so that sleep quality can be predicted in real time according to the relation.We propose a novel sleep quality predicting framework from user context data, without requiring users to wear special devices. We develop a data collecting and analyzing prototype system called SleepMiner, which uses on-phone data such as mobile sensor data and communication data to extract human contexts. Then the relationship between context data and sleep quality is analyzed and a learning model based on factor graph model is proposed to predict sleep quality. From experimental results we demonstrate that it is possible to accurately infer sleep quality (around 78%) from user context information. A set of solutions are proposed to address the practical problems of Android phone in data collection, making SleepMiner work with minimal impact on the phone's resources. We finally carry out experiments to evaluate our design in effectiveness and efficiency.

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“…Similar models have been derived for concrete in [6]. [7], measuring strain in carbon fiber composites [8], and measuring body positions and movements during sleep [9]. However, most applications have used the WISP in air.…”

Section: Analytical Model For Losses Inside Lossy Mediamentioning

confidence: 97%

Structural Health Monitoring Inside Concrete and Grout Using the Wireless Identification and Sensing Platform (WISP)

Cepero¹

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Professor Stavros GeorgakopoulosThis research investigates the implementation of battery-less RFID sensing platforms inside lossy media, such as, concrete and grout. Both concrete and novel grouts can be used for nuclear plant decommissioning as part of the U.S. Department of Subsequently, the measured complex permittivity is used to formulate analytical Debye models. Also, the transmission and propagation losses of a uniform plane wave inside grout are calculated. Our results show that wireless sensors will perform better in vi concrete than grout. In addition, the maximum axial and radial ranges for WISP are experimentally determined. Our work illustrates the feasibility of battery-less wireless sensors that are embedded inside concrete and grout. Also, our work provides information that can be used to optimize the power management, sampling rate, and antenna design of such sensors.

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Monitoring body positions and movements during sleep using WISPs

Cited by 101 publications

References 15 publications

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Will You Have a Good Sleep Tonight? Sleep Quality Prediction with Mobile Phone

Structural Health Monitoring Inside Concrete and Grout Using the Wireless Identification and Sensing Platform (WISP)

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