DoppleSleep

Rahman, Tauhidur; Adams, Alexander T.; Ravichandran, Ruth; Zhang, Mi; Patel, Shwetak; Kientz, Julie A.; Choudhury, Tanzeem

doi:10.1145/2750858.2804280

Cited by 152 publications

(21 citation statements)

References 20 publications

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“…Researchers of the work cited in [18] designed a device based on an artificial intelligence algorithm that analyzes the signals around the person to assess their level of sleep in light, deep, or REM. Other studies [19], reveal that low-power radio waves that detect small changes in body movement caused by the patient's breathing and pulse rate can non-intrusively diagnose and study sleep problems.…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Del-Valle-Soto,

Briseño,

Valdivia

et al. 2023

Future Internet

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Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient’s state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient’s home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person’s sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network.

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Section: Related Workmentioning

confidence: 99%

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Del-Valle-Soto,

Briseño,

Valdivia

et al. 2023

Future Internet

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“…All experiments were performed when subjects were fully clothed but not covered by any blankets. Prior research has demonstrated the efficacy of Doppler radar in this frequency range when used for extracting vital signs when subjects were covered with a bed sheet or quilt [24]. One of the major challenges in radar-based unobtrusive sleep posture recognition results from the motion noise produced by the random body or involuntary limb movement.…”

Section: Human Subjects Testingmentioning

confidence: 99%

“…Previous applications of cardiopulmonary radar in the biomedical field have included monitoring cardiopulmonary-related motion characteristics of a single [16][17] and multiple subjects [18][19][20]. Prior research also successfully demonstrated sleep monitoring [21] with CW [22][23][24][25], FMCW [26][27], and UWB [28][29]. While sleep applications of radar have included recognition of sleep stages (i.e., REM, non-REM) [24,28], and apnea events [21,25,29], there have also been recent research efforts involving sleep posture recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Prior research also successfully demonstrated sleep monitoring [21] with CW [22][23][24][25], FMCW [26][27], and UWB [28][29]. While sleep applications of radar have included recognition of sleep stages (i.e., REM, non-REM) [24,28], and apnea events [21,25,29], there have also been recent research efforts involving sleep posture recognition. Sleep posture recognition for chronic sleep disorder patients has been examined using Doppler radar with wavelet packet decomposition and a deep neural network [30].…”

Section: Introductionmentioning

confidence: 99%

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Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

et al. 2021

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While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject's changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects.

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“…Some works use wireless signals to detect the respiration rate. For example, Abdelnasser et al leveraged the WiFi signals [6–10], Lazaro et al used the UWB signals [11, 12], and Rahman et al used a microwave radar to detect respiration [13–15]. These systems require deploying extra wireless transceivers to transmit and receive wireless signals, which makes the system expensive.…”

Section: Introductionmentioning

confidence: 99%

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Zhang

Wei

2018

Computational and Mathematical Methods in Medicine

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Respiration monitoring is helpful in disease prevention and diagnosis. Traditional respiration monitoring requires users to wear devices on their bodies, which is inconvenient for them. In this paper, we aim to design a noncontact respiration rate detection system utilizing off-the-shelf smartphones. We utilize the single-frequency ultrasound as the media to detect the respiration activity. By analyzing the ultrasound signals received by the built-in microphone sensor in a smartphone, our system can derive the respiration rate of the user. The advantage of our method is that the transmitted signal is easy to generate and the signal analysis is simple, which has lower power consumption and thus is suitable for long-term monitoring in daily life. The experimental result shows that our system can achieve accurate respiration rate estimation under various scenarios.

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DoppleSleep

Cited by 152 publications

References 20 publications

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

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