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Contactless monitoring of human behaviors in bed using RSSI signals

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Abstract

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizure sleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4 GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people.

Graphical Abstract

The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals

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References

  1. Sun X, Qiu L, Wu Y, Tang Y, Cao G (2017) Sleepmonitor: monitoring respiratory rate and body position during sleep using smartwatch. Proc ACM Interact Mob Wearable Ubiquitous Technol 1(3):1–22

    Article  Google Scholar 

  2. Nam Y, Kim Y, Lee J (2016) Sleep monitoring based on a tri-axial accelerometer and a pressure sensor. Sensors 16(5):750

    Article  PubMed  PubMed Central  Google Scholar 

  3. Yue S, Yang Y, Wang H, Rahul H, Katabi D (2020) Bodycompass: monitoring sleep posture with wireless signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(2):1–25

    Article  Google Scholar 

  4. Liu X, Cao J, Tang S, Wen J (2014) Wi-sleep: Contactless sleep monitoring via wifi signals. In: International symposium on real-time systems, IEEE, pp. 346–355

  5. Hsu CY, Ahuja A, Yue S, Hristov R, Kabelac Z, Katabi D (2017) Zero-effort in-home sleep and insomnia monitoring using radio signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 1(3):1–18

    Article  Google Scholar 

  6. Louter M, Aarden WC, Lion J, Bloem BR, Overeem S (2012) Recognition and diagnosis of sleep disorders in Parkinson’s disease. J Neurol 259(10):2031–2040

    Article  PubMed  PubMed Central  Google Scholar 

  7. Liu JJ, Xu W, Huang MC, Alshurafa N, Sarrafzadeh M, Raut N, Yadegar B (2014) Sleep posture analysis using a dense pressure sensitive bedsheet. Pervasive Mob Comput 10:34–50

    Article  Google Scholar 

  8. Jeng PY, Wang LC, Hu CJ, Wu D (2021) A wrist sensor sleep posture monitoring system: an automatic labeling approach. Sensors 21(1):258

    Article  PubMed  PubMed Central  Google Scholar 

  9. Viriyavit W, Sornlertlamvanich V (2020) Bed position classification by a neural network and bayesian network using noninvasive sensors for fall prevention. J Sens 2020: 1–14

  10. Ruan W, Yao L, Sheng QZ, Falkner N, Li X, Gu T (2015) Tagfall: towards unobstructive fine-grained fall detection based on uhf passive rfid tags. In: Proceedings of the 12th international conference on mobile and ubiquitous systems: computing, networking and services, pp. 140–149

  11. Alwan M, Dalal S, Mack D, Kell S, Turner B, Leachtenauer J, Felder R (2006) Impact of monitoring technology in assisted living: outcome pilot. IEEE Trans Inf Technol Biomed 10(1):192–198

    Article  PubMed  Google Scholar 

  12. Cheung JCW, Tam EWC, Mak AHY, Chan TTC, Lai WPY, Zheng YP (2021) Night-time monitoring system (eNightLog) for elderly wandering behavior. Sensors 21(3):704

    Article  PubMed  PubMed Central  Google Scholar 

  13. Barsocchi P (2012) Position recognition to support bedsores prevention. IEEE J Biomed Health Inform 17(1):53–59

    Article  PubMed  Google Scholar 

  14. Ammae O, Korpela J, Maekawa T (2018) Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique. Futur Gener Comput Syst 78:616–625

    Article  Google Scholar 

  15. Youssef M, Mah M, Agrawala A (2007) Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th annual ACM international conference on mobile computing and networking, pp. 222–229

  16. Wilson J, Patwari N (2010) Radio tomographic imaging with wireless networks. IEEE Trans Mob Comput 9(5):621–632

    Article  Google Scholar 

  17. Konings D, Alam F, Noble F, Lai EM (2019) SpringLoc: a device-free localization technique for indoor positioning and tracking using adaptive RSSI spring relaxation. IEEE Access 7:56960–56973

    Article  Google Scholar 

  18. Konings D, Alam F, Noble F, Lai EM (2018) Device-free localization systems utilizing wireless RSSI: a comparative practical investigation. IEEE Sens J 19(7):2747–2757

    Article  Google Scholar 

  19. Liu J, Chen Y, Wang Y, Chen X, Cheng J, Yang J (2018) Monitoring vital signs and postures during sleep using WiFi signals. IEEE Internet Things J 5(3):2071–2084

    Article  Google Scholar 

  20. Gu Y, Zhang Y, Li J, Ji Y, An X, Ren F (2018) Sleepy: wireless channel data driven sleep monitoring via commodity WiFi devices. IEEE Trans Big Data 6(2):258–268

    Article  Google Scholar 

  21. Yang X, Shah SA, Ren A, Zhao N, Zhao J, Hu F, ... Alomainy A (2018) Monitoring of patients suffering from REM sleep behavior disorder. IEEE J Electromagn RF Microw Med Biol 2(2):138–143

  22. Lee, H. J., Lee, S. H., Ha, K. S., Jang, H. C., Chung, W. Y., Kim, J. Y., ... & Yoo, D. H. (2009). Ubiquitous healthcare service using Zigbee and mobile phone for elderly patients. International Journal of Medical Informatics, 78(3), 193–198.

  23. Deylami MN, Jovanov E (2013) A distributed scheme to manage the dynamic coexistence of IEEE 802.15. 4-based health-monitoring WBANs. IEEE J Biomed Health Informatics 18(1):327–334

    Article  Google Scholar 

  24. Zhao F, Cao Z, Xiao Y, Mao J, Yuan J (2018) Real-time detection of fall from bed using a single depth camera. IEEE Trans Autom Sci Eng 16(3):1018–1032

    Article  Google Scholar 

  25. Tang K, Kumar A, Nadeem M, Maaz I (2021) CNN-based smart sleep posture recognition system. IoT 2(1):119–139

    Article  Google Scholar 

  26. Diao H, Chen C, Yuan W, Amara A, Tamura T, Fan J, ... Chen W (2021) Deep residual networks for sleep posture recognition with unobtrusive miniature scale smart mat system. IEEE Trans Biomed Circuits Syst 15(1):111–121

  27. Valero M, Clemente J, Li F, Song W (2021) Health and sleep nursing assistant for real-time, contactless, and non-invasive monitoring. Pervasive Mob Comput 75:101422

    Article  Google Scholar 

  28. Li F, Valero M, Clemente J, Tse Z, Song W (2020) Smart sleep monitoring system via passively sensing human vibration signals. IEEE Sens J 21(13):14466–14473

    Article  Google Scholar 

  29. Chen Z, Wang Y (2021) Remote recognition of in-bed postures using a thermopile array sensor with machine learning. IEEE Sens J 21(9):10428–10436

    Article  Google Scholar 

  30. Shah SA, Zhao N, Ren A, Zhang Z, Yang X, Yang J, Zhao W (2016) Posture recognition to prevent bedsores for multiple patients using leaking coaxial cable. IEEE Access 4:8065–8072

    Article  Google Scholar 

  31. Hu X, Naya K, Li P, Miyazaki T, Wang K, Sun Y (2018) Noninvasive sleeping posture recognition and body movement detection based on RFID. In: International conference on internet of things (iThings) and green computing and communications (GreenCom) and cyber, physical and social computing (CPSCom) and smart data (Smart- Data), IEEE, pp. 1817–1820

  32. Z1 Datasheet Zolertia (2010) [online] Available: https://zolertia.sourceforge.net/wiki/images/e/e8/Z1_RevC_Datasheet.pdf

  33. CC2420 Datasheet Chipcon Instruments from Texas Instruments (2012) [online] Available: http://www.ti.com/lit/ds/symlink/cc2420.pdf

  34. Santiprapan P, Sengchuai K, Jindapetch N, Saito H, Booranawong A (2021) Development of an adaptive device-free human detection system for residential lighting load control. Comput Electr Eng 93:107233

    Article  Google Scholar 

  35. Booranawong A, Thammachote P, Sasiwat Y, Auysakul J, Sengchuai K, Buranapanichkit D, ... Saito H (2022) Real-time tracking of a moving target in an indoor corridor of the hospital building using RSSI signals received from two reference nodes. Med Biol Eng Comput 60:439–458

  36. Al-Nashash H, Lvov B (1997) Sudden infant death syndrome detector. Technol Health Care 5(6):461–469

    Article  CAS  PubMed  Google Scholar 

  37. Khan S, Qamar R, Zaheen R, Al-Ali AR, Al Nabulsi A, Al-Nashash H (2019) Internet of things based multi-sensor patient fall detection system. Healthc Technol Lett 6(5):132–137

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank Mr. Nattawut Na Songkhla for experimental support.

Funding

This work was supported by the Faculty of Engineering, Prince of Songkla University, Thailand, and the Division of Computer Engineering, The University of Aizu, Japan.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, 90110, Thailand

    Peeradon Thammachote, Chawakorn Intongkum, Kiattisak Sengchuai, Nattha Jindapetch, Pornchai Phukpattaranont & Apidet Booranawong

  2. Division of Computer Engineering, The University of Aizu, Aizu-Wakamatsu, 965-8580, Japan

    Hiroshi Saito

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  1. Peeradon Thammachote
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Contributions

Study conception and design: all authors; material preparation and data collection: PT, CI, KS, and AB; analysis and interpretation of results: PT, CI, KS, and AB; drafting of manuscript: PT and AB; critical revision of manuscript: NJ, PP, HS, and AB. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Apidet Booranawong.

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Thammachote, P., Intongkum, C., Sengchuai, K. et al. Contactless monitoring of human behaviors in bed using RSSI signals. Med Biol Eng Comput 61, 2561–2579 (2023). https://doi.org/10.1007/s11517-023-02847-6

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