Skip to main content
SpringerLink
Log in

Monitoring and Classification of Human Sleep Postures, Seizures, and Falls From Bed Using Three-Axis Acceleration Signals and Machine Learning

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

A system for monitoring and classifying human activity and sleep postures in bed using three-axis acceleration signals is presented in this paper. In this low-cost system, a three-axis accelerometer sensor using a single GY-521 sensor is placed on the abdominal muscles of the human body to measure human activity and sleep posture signals. The sensor is connected and communicates with the Arduino Mega for processing. Focused activities and sleep postures in bed, including (a) sleeping on his back, (b) turning around to sleep on his side, (c) sleeping on his side, (d) turning around and falling from the bed, (e) lying on the ground, and (f) seizure sleeping, are tested and evaluated. Finally, signal feature extraction using thirty-five features from three groups of calculations and classification using a K-nearest neighbors (KNN) algorithm are applied. Experiments are conducted in a laboratory, and results indicate that the proposed system could automatically monitor different human postures in bed and falls in real-time. Acceleration signals measured from activities and sleep postures have their own patterns and characteristics. Additionally, the average classification accuracy using the best four features obtained 93% for the postures a) to f) of 97.5%, 73.9%, 98.4%, 88.7%, 98.4%, and 91.1%, respectively. Here, before falls, falls, and seizures can be accurately detected. Our system and results can thus be used to support caretakers, physicians, and medical staff in the evaluation, planning, and treatment of the elderly and patients in healthcare systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Availability of Data and Materials

Data generated or analyzed during this study are included in this published article.

Abbreviations

KNN:

K-nearest neighbors

AI:

Artificial intelligence

ToF:

Time-of-flight

SVM:

Support vector machine

PCA:

Principal component analysis

MRMR:

Minimal redundancy-maximum relevance

EEG:

Electroencephalogram

sEMG:

Surface electromyography

EKG:

Electrocardiogram

NIRS:

Near infrared spectroscopy

ACM:

Accelerometer

EDA:

Electrodermal activity

RMS:

Root mean square

SD:

Standard deviation

TPR:

True positive rate

FNR:

False negative rate

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Chen Z, Wang Y. Remote recognition of in-bed postures using a thermopile array sensor with machine learning. IEEE Sens J. 2021;21(9):10428–36.

    Article  Google Scholar 

  4. Lee, J., Hong, M., & Ryu, S. (2015). Sleep monitoring system using kinect sensor. International Journal of Distributed Sensor Networks.

  5. Li, X., Gong, Y., Jin, X., & Shang, P. (2023). Sleep posture recognition based on machine learning: A systematic review. Pervasive and Mobile Computing, 101752.

  6. Lin BS, Peng CW, Lee IJ, Hsu HK, Lin BS. System based on artificial intelligence edge computing for detecting bedside falls and sleep posture. IEEE J Biomed Health Inform. 2023;27(7):3549–58.

    Article  Google Scholar 

  7. Wright K, Singh S. Reducing falls in dementia inpatients using vision-based technology. J Patient Saf. 2022;18(3):177.

    Article  Google Scholar 

  8. Thammachote, P., Intongkum, C., Sengchuai, K., Jindapetch, N., Phukpattaranont, P., Saito, H., & Booranawong, A. (2023). Contactless monitoring of human behaviors in bed using RSSI signals. Medical & Biological Engineering & Computing, 1–19.

  9. Nuksawn, L., Nantajeewarawat, E., & Thiemjarus, S. (2015, November). Real-time sensor-and camera-based logging of sleep postures. In 2015 International Computer Science and Engineering Conference (ICSEC) (pp. 1–6). IEEE.

  10. Li, Y. Y., Lei, Y. J., Chen, L. C. L., & Hung, Y. P. (2018, January). Sleep posture classification with multi-stream CNN using vertical distance map. In 2018 International Workshop on Advanced Image Technology (IWAIT) (pp. 1–4). IEEE.

  11. Li YY, Wang SJ, Hung YP. A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification. Sensors. 2022;22(5):2014.

    Article  Google Scholar 

  12. Stern L, Roshan Fekr A. In-bed posture classification using deep neural network. Sensors. 2023;23(5):2430.

    Article  Google Scholar 

  13. Cheng X, Hu F, Yang B, Wang F, Olofsson T. Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study. Indoor Air. 2022;32(12): e13175.

    Article  Google Scholar 

  14. Roshini, A., & Kiran, K. V. D. (2022). An enhanced posture prediction-Bayesian network algorithm for sleep posture recognition in wireless body area networks. International Journal of Telemedicine and Applications, 2022.

  15. Mohammadi SM, Enshaeifar S, Hilton A, Dijk DJ, Wells K. Transfer learning for clinical sleep pose detection using a single 2D IR camera. IEEE Trans Neural Syst Rehabil Eng. 2020;29:290–9.

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Matar G, Lina JM, Kaddoum G. Artificial neural network for in-bed posture classification using bed-sheet pressure sensors. IEEE J Biomed Health Inform. 2019;24(1):101–10.

    Article  Google Scholar 

  18. Wang, Z., Sui, Z., Zhang, A., Wang, R., Zhang, Z., Lin, F., et al. (2021). A piezoresistive array based force sensing technique for sleeping posture and respiratory rate detection for SAS patients. IEEE Sensors Journal.

  19. Diao, H., Chen, C., Liu, X., Yuan, W., Amara, A., Tamura, T., et al. (2022). Real-time and cost-effective smart mat system based on frequency channel selection for sleep posture recognition in IoMT. IEEE Internet of Things Journal, 9(21), 21421–21431.

  20. Viriyavit W, Sornlertlamvanich V. Bed position classification by a neural network and bayesian network using noninvasive sensors for fall prevention. Journal of Sensors. 2020;2020:1–14.

    Article  Google Scholar 

  21. Diao, H., Chen, C., Yuan, W., Amara, A., Tamura, T., Fan, J., et al. (2021). Deep residual networks for sleep posture recognition with unobtrusive miniature scale smart mat system. IEEE Transactions on Biomedical Circuits and Systems, 15(1), 111–121.

  22. Huchaiah MD, Kasubi JW. Predicting of sleep behaviour in smart homes based on multi-residents using machine learning techniques. SN Computer Science. 2021;2(4):244.

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Pannurat N, Thiemjarus S, Nantajeewarawat E, Anantavrasilp I. Analysis of optimal sensor positions for activity classification and application on a different data collection scenario. Sensors. 2017;17(4):774.

    Article  Google Scholar 

  25. Leone A, Rescio G, Caroppo A, Siciliano P, Manni A. Human postures recognition by accelerometer sensor and ML architecture integrated in embedded platforms: Benchmarking and performance evaluation. Sensors. 2023;23(2):1039.

    Article  Google Scholar 

  26. Yoon, H., Hwang, S., Jung, D., Choi, S., Joo, K., Choi, J., et al. (2015, August). Estimation of sleep posture using a patch-type accelerometer based device. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4942–4945). IEEE.

  27. Doheny, E. P., Lowery, M. M., Russell, A., & Ryan, S. (2020, July). Estimation of respiration rate and sleeping position using a wearable accelerometer. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4668–4671). IEEE.

  28. Lokavee, S., Tantrakul, V., Pengjiam, J., & Kerdcharoen, T. (2021, January). A sleep monitoring system using force sensor and an accelerometer sensor for screening sleep apnea. In 2021 13th International Conference on Knowledge and Smart Technology (KST) (pp. 208–213). IEEE.

  29. Yue S, Yang Y, Wang H, Rahul H, Katabi D. BodyCompass: Monitoring sleep posture with wireless signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2020;4(2):1–25.

    Article  Google Scholar 

  30. Yüzer AH, Sümbül H, Nour M, Polat K. A different sleep apnea classification system with neural network based on the acceleration signals. Appl Acoust. 2020;163: 107225.

    Article  Google Scholar 

  31. Slim, S. O., Atia, A., Elfattah, M. M., & Mostafa, M. S. M. (2019). Survey on human activity recognition based on acceleration data. International Journal of Advanced Computer Science and Applications, 10(3).

  32. Ulate-Campos A, Coughlin F, Gaínza-Lein M, Fernández IS, Pearl PL, Loddenkemper T. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure. 2016;40:88–101.

    Article  Google Scholar 

  33. Matsushima A, Yoshida K, Genno H, Ikeda SI. Principal component analysis for ataxic gait using a triaxial accelerometer. J Neuroeng Rehabil. 2017;14(1):1–7.

    Article  Google Scholar 

  34. Jones PJ, Catt M, Davies MJ, Edwardson CL, Mirkes EM, Khunti K, Rowlands AV. Feature selection for unsupervised machine learning of accelerometer data physical activity clusters–A systematic review. Gait Posture. 2021;90:120–8.

    Article  Google Scholar 

  35. Wang Z, Wu D, Chen J, Ghoneim A, Hossain MA. A triaxial accelerometer-based human activity recognition via EEMD-based features and game-theory-based feature selection. IEEE Sens J. 2016;16(9):3198–207.

    Article  Google Scholar 

  36. Liu S, Gao RX, John D, Staudenmayer JW, Freedson PS. Multisensor data fusion for physical activity assessment. IEEE Trans Biomed Eng. 2011;59(3):687–96.

    Google Scholar 

  37. Kaghyan S, Sarukhanyan H. Activity recognition using k-nearest neighbor algorithm on smartphone with tri-axial accelerometer. International Journal of Informatics Models and Analysis (IJIMA). ITHEA International Scientific Society, Bulgaria. 2012;1:146–56.

    Google Scholar 

  38. Burgos CP, Gärtner L, Ballester MAG, Noailly J, Stöcker F, Schönfelder M, Tassani S. In-ear accelerometer-based sensor for gait classification. IEEE Sens J. 2020;20(21):12895–902.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Faculty of Engineering, Prince of Songkla University, Thailand.

Funding

Faculty of Engineering, Prince of Songkla University.

Author information

Authors and Affiliations

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

    Chawakorn Intongkum, Yoschanin Sasiwat, Kiattisak Sengchuai, Apidet Booranawong & Pornchai Phukpattaranont

Authors
  1. Chawakorn Intongkum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoschanin Sasiwat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kiattisak Sengchuai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Apidet Booranawong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pornchai Phukpattaranont
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, CI, and AB; methodology, CI, YS, KS, and AB; investigation, CI, YS, KS, and AB; writing—original draft preparation, CI, KS, and AB; writing— review and editing, CI, KS, AB, and PP; supervision, AB, and PP; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Apidet Booranawong.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

All authors have agreed to submit the paper for publication. Additionally, Mr. Chawakorn Intongkum, who is the first author of this paper and also performed the experiments, provided informed consent for the publication of his images in Fig. 3.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Intongkum, C., Sasiwat, Y., Sengchuai, K. et al. Monitoring and Classification of Human Sleep Postures, Seizures, and Falls From Bed Using Three-Axis Acceleration Signals and Machine Learning. SN COMPUT. SCI. 5, 104 (2024). https://doi.org/10.1007/s42979-023-02426-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02426-4

Keywords

Search

Navigation