Respiratory Rate Monitoring in Clinical Environments with a Contactless Ultra-Wideband Impulse Radar-based Sensor System

Respiratory rate is an extremely important but poorly monitored vital sign for medical conditions. Current modalities for respiratory monitoring are suboptimal. This paper presents a proof of concept of a new algorithm using a contactless ultra-wideband (UWB) impulse radar-based sensor to detect respiratory rate in both a laboratory setting and in a two-subject case study in the Emergency Department. This novel approach has shown correlation with manual respiratory rate in the laboratory setting and shows promise in Emergency Department subjects. In order to improve respiratory rate monitoring, the UWB technology is also able to localize subject movement throughout the room. This technology has potential for utilization both in and out of the hospital environments to improve monitoring and to prevent morbidity and mortality from a variety of medical conditions associated with changes in respiratory rate

An Ultrasonic Contactless Sensor for Breathing Monitoring

International audienceThe monitoring of human breathing activity during a long period has multiple fundamental applications in medicine. In breathing sleep disorders such as apnea, the diagnosis is based on events during which the person stops breathing for several periods during sleep. In polysomnography, the standard for sleep disordered breathing analysis, chest movement and airflow are used to monitor the respiratory activity. However, this method has serious drawbacks. Indeed, as the subject should sleep overnight in a laboratory and because of sensors being in direct contact with him, artifacts modifying sleep quality are often observed. This work investigates an analysis of the viability of an ultrasonic device to quantify the breathing activity, without contact and without any perception by the subject. Based on a low power ultrasonic active source and transducer, the device measures the frequency shift produced by the velocity difference between the exhaled air flow and the ambient environment, i.e., the Doppler effect. After acquisition and digitization, a specific signal processing is applied to separate the effects of breath from those due to subject movements from the Doppler signal. The distance between the source and the sensor, about 50 cm, and the use of ultrasound frequency well above audible frequencies, 40 kHz, allow monitoring the breathing activity without any perception by the subject, and therefore without any modification of the sleep quality which is very important for sleep disorders diagnostic applications. This work is patented (patent pending 2013-7-31 number FR.13/57569). OPE

Embroidered wearable antenna-based sensor for real-time breath monitoring

© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper we present the design and the validation of a novel fully embroidered meander dipole antenna-based sensor integrated into a commercially available T-shirt for real-time breathing monitoring using the technique based on chest well movement analysis. The embroidered antenna-based sensor is made of a silver-coated nylon thread. The proposed antenna-sensor is integrated into a cotton T-shirt and placed on the middle of the human chest. The breathing antenna-based sensor was designed to operate at 2.4 GHz. The sensing mechanism of the system is based on the resonant frequency shift of the meander dipole antenna-sensor induced by the chest expansion and the displacement of the air volume in the lungs during breathing. The resonant frequency shift was continuously measured using a Vector Network Analyzer (VNA) to a remote PC via LAN interface in real-time. A program was developed via Matlab to collect respiration data information using a PC host via LAN interface to be able to transfer data with instrumentation over TCP/IP. The measurements were carried out to monitor the breathing of a female volunteer for various positions (standing and sitting) with different breathing patterns: eupnea (normal respiration), apnea (absence of breathing), hypopnea (shaloow breathing) and hyperpnea (deep breathing). The measured resonance frequency shift to 2.98 GHz, 3.2 GHz and 2 GHz for standing position and 2.84 GHz, 2.95 GHz and 2.15 GHz for sitting position, for eupnea, hyperpnea and hypopnea, respectively. The area of the textile sensor is 45 x 4.87 mm2 , reducing the surface consumtion significatively with regard to other reported breath wearable sensors for health monitoring.This work was supported by the Spanish Government MINECO under project TEC2016-79465-R.Peer ReviewedPostprint (author's final draft

Collimated beam FMCW radar for vital sign patient monitoring

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Patient monitoring of vital signals such as breathing rhythm and heart beat rate can be done remotely by the use of a radar system. This approach is advantageous since it does not require any contact with the patient. Obviously contactless monitoring results in a more comfortable situation for the patient, and in occasions it is almost mandatory as in the case of heavy burnt or newborn patients. Moreover, additional information such movement patterns are also available. A 120 GHz FMCW radar is described with special focus on the design, construction and testing of a specific reflector antenna for the system. The system is based on a commercial radar chipset that includes its own antennas. The challenge has been to design the optimum reflector and to build it and test it in a cost effective way. The reflector has been 3D printed and a near-field testing technique has been implemented to assess its performance. The results show that the system is able to measure the vital signs at distances beyond one meter.Postprint (author's final draft

Development of a Novel Contactless Mechanocardiograph Device

A novel method of detecting mechanical movement of the heart, Mechanocardiography (MCG), with no connection to the subject's body is presented. This measurement is based on radar technology and it has been proven through this research work that the acquired signal is highly correlated to the phonocardiograph signal and acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. The heart rate and respiration rate have been extracted from the acquired signal as two possible physiological monitoring applications of the radar-based MCG device

Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation

[EN] Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis. Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most highly-valued devices. Software apps have also been compared with regards to the market. Expert opinion: Thanks to technological advances, the reliability and accuracy of sensors has been significantly increased in recent years. According to validation studies, some actigraphs present a sensibility higher than 90%. However, the market analysis reveals that many hardware devices have not been validated, and especially software devices should be studied before their clinical use.Ibáñez, V.; Silva, J.; Navarro, E.; Cauli, O. (2019). Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation. Expert Review of Medical Devices. 16(12):1041-1052. https://doi.org/10.1080/17434440.2019.1693890S104110521612El-Sayed, I. H. (2012). Comparison of four sleep questionnaires for screening obstructive sleep apnea. Egyptian Journal of Chest Diseases and Tuberculosis, 61(4), 433-441. doi:10.1016/j.ejcdt.2012.07.003FIRAT, H., YUCEEGE, M., DEMIR, A., & ARDIC, S. (2012). Comparison of four established questionnaires to identify highway bus drivers at risk for obstructive sleep apnea in Turkey. Sleep and Biological Rhythms, 10(3), 231-236. doi:10.1111/j.1479-8425.2012.00566.xPataka, A., Daskalopoulou, E., Kalamaras, G., Fekete Passa, K., & Argyropoulou, P. (2014). Evaluation of five different questionnaires for assessing sleep apnea syndrome in a sleep clinic. Sleep Medicine, 15(7), 776-781. doi:10.1016/j.sleep.2014.03.012Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep questionnaires and diaries. Sleep Medicine, 42, 90-96. doi:10.1016/j.sleep.2017.08.026Ong, A. A., & Gillespie, M. B. (2016). Overview of smartphone applications for sleep analysis. World Journal of Otorhinolaryngology-Head and Neck Surgery, 2(1), 45-49. doi:10.1016/j.wjorl.2016.02.001Kolla, B. P., Mansukhani, S., & Mansukhani, M. P. (2016). Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices, 13(5), 497-506. doi:10.1586/17434440.2016.1171708Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep assessment methods. PeerJ, 6, e4849. doi:10.7717/peerj.4849Bianchi, M. T. (2018). Sleep devices: wearables and nearables, informational and interventional, consumer and clinical. Metabolism, 84, 99-108. doi:10.1016/j.metabol.2017.10.008Blackwell, T., Redline, S., Ancoli-Israel, S., Schneider, J. L., Surovec, S., Johnson, N. L., … Stone, K. L. (2008). Comparison of Sleep Parameters from Actigraphy and Polysomnography in Older Women: The SOF Study. Sleep, 31(2), 283-291. doi:10.1093/sleep/31.2.283Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., … Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1). doi:10.1186/2046-4053-4-1Wu, H., Kato, T., Numao, M., & Fukui, K. (2017). Statistical sleep pattern modelling for sleep quality assessment based on sound events. Health Information Science and Systems, 5(1). doi:10.1007/s13755-017-0031-zLee, J., Hong, M., & Ryu, S. (2015). Sleep Monitoring System Using Kinect Sensor. International Journal of Distributed Sensor Networks, 2015, 1-9. doi:10.1155/2015/875371Patel, P., Kim, J. Y., & Brooks, L. J. (2016). Accuracy of a smartphone application in estimating sleep in children. Sleep and Breathing, 21(2), 505-511. doi:10.1007/s11325-016-1425-xCHEN, K. Y., & BASSETT, D. R. (2005). The Technology of Accelerometry-Based Activity Monitors: Current and Future. Medicine & Science in Sports & Exercise, 37(Supplement), S490-S500. doi:10.1249/01.mss.0000185571.49104.82Öberg, P. Å., Togawa, T., & Spelman, F. A. (Eds.). (2004). Sensors in Medicine and Health Care. doi:10.1002/3527601414Godfrey, A., Conway, R., Meagher, D., & ÓLaighin, G. (2008). Direct measurement of human movement by accelerometry. Medical Engineering & Physics, 30(10), 1364-1386. doi:10.1016/j.medengphy.2008.09.005Yang, C.-C., & Hsu, Y.-L. (2010). A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors, 10(8), 7772-7788. doi:10.3390/s100807772Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., … Buxton, O. M. (2013). Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography. Sleep, 36(11), 1747-1755. doi:10.5665/sleep.3142Meltzer, L. J., Wong, P., Biggs, S. N., Traylor, J., Kim, J. Y., Bhattacharjee, R., … Marcus, C. L. (2016). Validation of Actigraphy in Middle Childhood. Sleep, 39(6), 1219-1224. doi:10.5665/sleep.5836De Souza, L., Benedito-Silva, A. A., Pires, M. L. N., Poyares, D., Tufik, S., & Calil, H. M. (2003). Further Validation of Actigraphy for Sleep Studies. Sleep, 26(1), 81-85. doi:10.1093/sleep/26.1.81Sivertsen, B., Omvik, S., Havik, O. E., Pallesen, S., Bjorvatn, B., Nielsen, G. H., … Nordhus, I. H. (2006). A Comparison of Actigraphy and Polysomnography in Older Adults Treated for Chronic Primary Insomnia. Sleep, 29(10), 1353-1358. doi:10.1093/sleep/29.10.1353Paquet, J., Kawinska, A., & Carrier, J. (2007). Wake Detection Capacity of Actigraphy During Sleep. Sleep, 30(10), 1362-1369. doi:10.1093/sleep/30.10.1362Sitnick, S. L., Goodlin-Jones, B. L., & Anders, T. F. (2008). The Use of Actigraphy to Study Sleep Disorders in Preschoolers: Some Concerns about Detection of Nighttime Awakenings. Sleep, 31(3), 395-401. doi:10.1093/sleep/31.3.395Natale, V., Plazzi, G., & Martoni, M. (2009). Actigraphy in the Assessment of Insomnia: A Quantitative Approach. Sleep, 32(6), 767-771. doi:10.1093/sleep/32.6.767Nakazaki, K., Kitamura, S., Motomura, Y., Hida, A., Kamei, Y., Miura, N., & Mishima, K. (2014). Validity of an algorithm for determining sleep/wake states using a new actigraph. Journal of Physiological Anthropology, 33(1), 31. doi:10.1186/1880-6805-33-31Matsuo, M., Masuda, F., Sumi, Y., Takahashi, M., Yamada, N., Ohira, M. H., … Kadotani, H. (2016). Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders. Frontiers in Neurology, 7. doi:10.3389/fneur.2016.00110Pigeon, W. R., Taylor, M., Bui, A., Oleynk, C., Walsh, P., & Bishop, T. M. (2018). Validation of the Sleep-Wake Scoring of a New Wrist-Worn Sleep Monitoring Device. Journal of Clinical Sleep Medicine, 14(06), 1057-1062. doi:10.5664/jcsm.7180Toon, E., Davey, M. J., Hollis, S. L., Nixon, G. M., Horne, R. S. C., & Biggs, S. N. (2016). Comparison of Commercial Wrist-Based and Smartphone Accelerometers, Actigraphy, and PSG in a Clinical Cohort of Children and Adolescents. Journal of Clinical Sleep Medicine, 12(03), 343-350. doi:10.5664/jcsm.5580Lee, X. K., Chee, N. I. Y. N., Ong, J. L., Teo, T. B., van Rijn, E., Lo, J. C., & Chee, M. W. L. (2019). Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations. Journal of Clinical Sleep Medicine, 15(09), 1337-1346. doi:10.5664/jcsm.7932Roomkham, S., Hittle, M., Cheung, J., Lovell, D., Mignot, E., & Perrin, D. (2019). Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph. F1000Research, 8, 754. doi:10.12688/f1000research.19020.1De Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C. (2015). Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiology International, 32(7), 1024-1028. doi:10.3109/07420528.2015.1054395Montgomery-Downs, H. E., Insana, S. P., & Bond, J. A. (2011). Movement toward a novel activity monitoring device. Sleep and Breathing, 16(3), 913-917. doi:10.1007/s11325-011-0585-yDe Zambotti, M., Baker, F. C., & Colrain, I. M. (2015). Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents. Sleep, 38(9), 1461-1468. doi:10.5665/sleep.4990Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1). doi:10.1186/s12966-015-0314-1Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., & Gillin, J. C. (1992). Automatic Sleep/Wake Identification From Wrist Activity. Sleep, 15(5), 461-469. doi:10.1093/sleep/15.5.461Boyne, K., Sherry, D. D., Gallagher, P. R., Olsen, M., & Brooks, L. J. (2012). Accuracy of computer algorithms and the human eye in scoring actigraphy. Sleep and Breathing, 17(1), 411-417. doi:10.1007/s11325-012-0709-zSadeh, A., Sharkey, M., & Carskadon, M. A. (1994). Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. Sleep, 17(3), 201-207. doi:10.1093/sleep/17.3.201Shin, M., Swan, P., & Chow, C. M. (2015). The validity of Actiwatch2 and SenseWear armband compared against polysomnography at different ambient temperature conditions. Sleep Science, 8(1), 9-15. doi:10.1016/j.slsci.2015.02.003Quante, M., Kaplan, E. R., Cailler, M., Rueschman, M., Wang, R., Weng, J., … Redline, S. (2018). Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nature and Science of Sleep, Volume 10, 13-20. doi:10.2147/nss.s151085Meltzer, L. J., Hiruma, L. S., Avis, K., Montgomery-Downs, H., & Valentin, J. (2015). Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents. Sleep, 38(8), 1323-1330. doi:10.5665/sleep.4918Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., … Brand, H. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 12(1). doi:10.1186/1471-2458-12-80Baron, K. G., Abbott, S., Jao, N., Manalo, N., & Mullen, R. (2017). Orthosomnia: Are Some Patients Taking the Quantified Self Too Far? Journal of Clinical Sleep Medicine, 13(02), 351-354. doi:10.5664/jcsm.647

Wireless communication platform based on an embroidered antenna-sensor for real-time breathing detection

Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2 , with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from -80 dBm to -72 dBm, -88 dBm to -79 dBm and -85 dBm to -80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.This work was supported by the Spanish Government-MICINN under Projects TED2021- 131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

Measurements Performance of a Bioradar for Human Respiration Monitoring

Abstract Breathing pattern monitoring of humans is very important especially during long isolation in space missions. In particular, several factors can induce some breathing anomalies during the sleep, which may cause apnea episodes; an early diagnosis of such episodes is crucial for the application of an efficient therapy. Continuous wave bioradars operating in the microwave frequency range are effective contactless tools for monitoring the respiratory activity. These active systems emit a low power electromagnetic wave at a single frequency, which is reflected by the human chest. Based on the phase difference between the incident and reflected signals, it is possible to estimate and monitor the respiratory rate. In this paper, a metrological characterization of the bioradar methodology is presented. To this end, bioradar results are compared with the ground truth data recorded by a spirometer, which is a standard medical device that measures the air volume inhaled and exhaled by the subject