LungTrack: towards contactless and zero dead-zone respiration monitoring with commodity RFIDs

International audienceRespiration rate sensing plays a critical role in elderly care and patient monitoring. The latest research has explored the possibility of employing Wi-Fi signals for respiration sensing without attaching a device to the target. A critical issue with these solutions includes that good monitoring performance could only be achieved at certain locations within the sensing range, while the performance could be quite poor at other "dead zones." In addition, due to the contactless nature, it is challenging to monitor multiple targets simultaneously as the reflected signals are often mixed together. In this work, we present our system, named LungTrack, hosted on commodity RFID devices for respiration monitoring. Our system retrieves subtle signal fluctuations at the receiver caused by chest displacement during respiration without need for attaching any devices to the target. It addresses the dead-zone issue and enables simultaneous monitoring of two human targets by employing one RFID reader and carefully positioned multiple RFID tags, using an optimization technique. Comprehensive experiments demonstrate that LungTrack can achieve a respiration monitoring accuracy of greater than 98% for a single target at all sensing locations (within 1 st − 5 th Fresnel zones) using just one RFID reader and five tags, when the target's orientation is known a priori. For the challenging scenario involve two human targets, LungTrack is able to achieve greater than 93% accuracy when the targets are separated by at least 10 cm

A survey on sleep assessment methods

[EN] Purpose. A literature review is presented that aims to summarize and compare curren methods to evaluate sleep. Methods. Current sleep assessment methods have been classified according to different. criteria; e.g., objective (polysomnography actigraphy) vs.subjective (sleep questionnaires, diaries...), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers' opinions about current sleep apps. Results. A taxonomy that classifies the sleep, detection methods. IA deseriPtion of each method that includes the tendencies of their underlying technologies lanalyzed in accordance with the literature. A comparison in terms, of precision of existing validation studies and reports. Discussion. In order of accuracy, sleep detection methods may be arranged as follows: Questionnaire < Sleep diary < Contactless devices < Contact devices < Polysotnnography A literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%-96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the Patients Perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.Ibáñez, V.; Silva, J.; Cauli, O. (2018). A survey on sleep assessment methods. PeerJ. 6:1-26. https://doi.org/10.7717/peerj.4849S1266Baandrup, L., & Jennum, P. (2015). A validation of wrist actigraphy against polysomnography in patients with schizophrenia or bipolar disorder. Neuropsychiatric Disease and Treatment, 2271. doi:10.2147/ndt.s88236Bhat, S., Ferraris, A., Gupta, D., Mozafarian, M., DeBari, V. A., Gushway-Henry, N., … Chokroverty, S. (2015). Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography. Journal of Clinical Sleep Medicine, 11(07), 709-715. doi:10.5664/jcsm.4840Bobes, J., González, M. P., Vallejo, J., Sáiz, J., Gibert, J., Ayuso, J. L., & Rico, F. (1998). 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C., & Colrain, I. M. (2015). Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents. Sleep, 38(9), 1461-1468. doi:10.5665/sleep.4990De 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:10.3109/07420528.2015.1054395Douglass, A. B., Bomstein, R., Nino-Murcia, G., Keenan, S., Miles, L., Zarcone, V. P., … Dement, W. C. (1994). The Sleep Disorders Questionnaire I: Creation and Multivariate Structure of SDQ. Sleep, 17(2), 160-167. doi:10.1093/sleep/17.2.160El-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.2012.07.003Evenson, 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/s12966-015-0314-1FIRAT, 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.xWARD FLEMONS, W., & REIMER, M. A. (1998). Development of a Disease-specific Health-related Quality of Life Questionnaire for Sleep Apnea. American Journal of Respiratory and Critical Care Medicine, 158(2), 494-503. doi:10.1164/ajrccm.158.2.9712036Flemons, W. W., Whitelaw, W. A., Brant, R., & Remmers, J. E. (1994). Likelihood ratios for a sleep apnea clinical prediction rule. American Journal of Respiratory and Critical Care Medicine, 150(5), 1279-1285. doi:10.1164/ajrccm.150.5.7952553Ibáñez, V., Silva, J., & Cauli, O. (2018). A survey on sleep questionnaires and diaries. 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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. 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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. 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Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio

Diagnosis of the Hypopnea syndrome in the early stage

Hypopnea syndrome is a chronic respiratory disease that is characterized by repetitive episodes of breathing disruptions during sleep. Hypopnea syndrome is a systemic disease that manifests respiratory problems; however, more than 80% of Hypopnea syndrome patients remain undiagnosed due to complicated polysomnography. Objective assessment of breathing patterns of an individual can provide useful insight into the respiratory function unearthing severity of Hypopnea syndrome. This paper explores a novel approach to detect incognito Hypopnea syndrome as well as provide a contactless alternative to traditional medical tests. The proposed method is based on S-Band sensing technique (including a spectrum analyzer, vector network analyzer, antennas, software-defined radio, RF generator, etc.), peak detection algorithm and Sine function fitting for the observation of breathing patterns and characterization of normal or disruptive breathing patterns for Hypopnea syndrome detection. The proposed system observes the human subject and changes in the channel frequency response caused by Hypopnea syndrome utilizing a wireless link between two monopole antennas, placed 3 m apart. Commercial respiratory sensors were used to verify the experimental results. By comparing the results, it is found that for both cases, the pause time is more than 10 s with 14 peaks. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate Hypopnea syndrome monitoring in a patient-friendly and flexible environment

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