Research Article
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Design of Wearable Patient Lying Position Tracking and Warning System to Prevent Pressure Injury

Year 2022, Volume: 11 Issue: 4, 1073 - 1083, 31.12.2022
https://doi.org/10.17798/bitlisfen.1171266

Abstract

Within the scope of this study, a wearable lying position tracking system equipped with IMU sensors has been developed to prevent the formation of pressure injuries in bedridden patients. Three IMU sensors were placed on the patient's chest, one on the right upper leg and the other on the left upper leg, and the angular orientation expressions of the limbs were calculated. Datasets were created for three different hospitalization positions, and machine learning and deep neural network models were used to classify the patient's hospitalization type. The success of the classifiers was compared by calculating the accuracy, sensitivity, specificity, precision and F1 score values. The average accuracy values in the lying position classification were obtained as 99.506%, 99.977%, 99.972%, 99.838%, and 99.967% respectively, using Linear discriminant analysis, K-Nearest neighbor, Decision Tree, Support Vector Machine and Random Forest classification methods. The highest accuracy rate was obtained as a result of the K-Nearest neighbor method with high variation. The time that the person remained fixed in the determined lying position was also calculated, and if it remained longer than the specified time, an audible warning signal was generated to change the position. Thus, it has been tried to prevent the person to apply pressure by lying on a single muscle group and tissue for a long time and to prevent the formation of pressure injuries over time.

Supporting Institution

Fırat Üniversitesi

Project Number

MF 21.14

Thanks

This study was supported by Fırat University within the scope of MF 21.14 Graduate Scıentıfıc Research Project.

References

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  • [25] S. Xia, L. Pei, Z. Zhang, W. Yu, R. C. Qui, Learning Disentangled Representation for Mixed- Reality Human Activity Recognition with a Single IMU Sensor. IEEE Transactions On Instrumentation and Measurement, vol. 70, 2514314, 2021.
  • [26] P. Blank, J. Hoßbach, D. Schuldhaus, B. M. Eskofier, Sensor-based stroke detection and stroke type classification in table tennis. In Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September 2015, pp. 93-100, 2015.
  • [27] N. G. Punchihewa, G. Yamako, Y. Fukao, E. Chosa, Identification of key events in baseball hitting using inertial measurement units. J. Biomech., 87, pp. 157-160, 2019.
  • [28] R. Ma, D. Yan, H. Peng, T. Yang, X. Sha, Y. Zhao, L. Liu, Basketball movements recognition using a wrist wearable inertial measurement unit. In Proceedings of the 2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS), Shenzhen, China, 5–7 December 2018, pp. 73-76, 2018.
  • [29] T. Kautz, B. H. Groh, J. Hannink, U. Jensen, H. Strubberg, B. M. Eskofier, Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Min. Knowl. Discov., 31, pp. 1678-1705, 2017.
  • [30] Z. Zhang, D. Xu, Z. Zhou, J. Mai, Z. He, Q. Wang, IMU-based underwater sensing system for swimming stroke classification and motion analysis. In Proceedings of the 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS), Beijing, China, 17–19 October 2017, pp. 268-272, 2017.
  • [31] R. Vleugels, B. V. Herbruggen, J. Fontaine, E. Poorter, Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics. Sensors, 21, 4650, 2021.
  • [32] M. Pal, G. M. Foody, Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience Remote Sensing, 48, pp. 2297-2307, 2010.
  • [33] O. Yaman, T. Tuncer, B. Tasar, DES-Pat: A novel DES pattern-based propeller recognition method using underwater acoustical sounds. Appl. Acoust., 175, 107859, 2021.
  • [34] A. Tharwat, T. Gaber, A. İbrahim, A. E. Hassanien, Linear discriminant analysis: A detailed tutorial. AI Communications, pp. 1-22, 2017.
Year 2022, Volume: 11 Issue: 4, 1073 - 1083, 31.12.2022
https://doi.org/10.17798/bitlisfen.1171266

Abstract

Project Number

MF 21.14

References

  • [1] D. C. Klonoff, The increasing incidence of diabetes in the 21st century. Journal of Diabetes Science and Technology, vol. 3(1), pp. 1-2, 2009.
  • [2] B. Pieper, Pressure ulcers: impact, etiology, and classification. Wound Management, vol. 110, pp. 124-139, 2015.
  • [3] S. D. Horn, S. A. Bender, M. L. Ferguson, J. S. Randall, N. Bergstrom, G. Taler, A. S. Cook, S. S. Sharkey, A.C. Voss, The National Pressure Ulcer Long-Term Care Study: pressure ulcer development in long-term care residents. Journal of the American Geriatrics Society, vol. 52(3), pp. 359-367, 2004.
  • [4] L. E. Edsberg, J. M. Black, M. Goldberg, L. McNichol, L. Moore, M. Sieggreen, Revised National Pressure Ulcer Advisory Panel pressure injury staging system: revised pressure injury staging system. Journal of Wound, Ostomy and Continence Nursing, vol. 43, pp. 585-597, 2016.
  • [5] B. Ay, B. Taşar, Z. Utlu, K. Ay, G. Aydın, Deep transfer learning-based visual classification of pressure injuries stages. Neural Computing and Applications, vol. 34, pp. 16157-16168, 2022.
  • [6] Q. Jiang, X. Li, X. Qu, Y. Liu, L. Zhang, C. Su, X. Guo, Y. Chen, Y. Zhu, J. Jia, S. Bo, L. Liu, R. Zhang, L. Xu, L. Wu, H. Wang, J. Wang, The incidence, risk factors and characteristics of pressure ulcers in hospitalized patients in China. International Journal of Clinical and Experimental Pathology, vol. 7(5), pp. 2587-2594, 2014.
  • [7] G. Brown, Long-term outcomes of full-thickness pressure ulcers: healing and mortality. Ostomy Wound Manage, vol. 49(10), pp. 42-50, 2003.
  • [8] D. Berlowitz, L. C. Vandeusen, V. Parker, et. al., Preventing pressure ulcers in hospitals. Agency for healthcare research & Quality, 2018.
  • [9] J. J. S Agreda, J. E. T. I. Bou, J. Posnett, J. V. Soriano, L. S. Miguel, M. M. Santos, The Burden of Pressure Ulcers in Spain. Wounds a Compend Clin Res Pract, vol. 19(7), pp. 201-206, 2007.
  • [10] A. Tubaishat, P. Papanikolaou, D. Anthony, L. Habiballah, Pressure Ulcers Prevalence in the Acute Care Setting: A Systematic Review, 2000-2015. Clinical Nursing Research, vol. 27(6), pp. 643-659, 2018.
  • [11] S. Zahia, Z. M. B. Garcia, X. Sevillano, A. Gonzalez, P. J. Kim, A. Elmaghraby, Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods. Artificial Intelligence in Medicine, vol. 102, 101742, 2020.
  • [12] B. Taşar, A. B. Tatar, Ö. Nazlı, O. Kalkan, Remote Control of Unmanned Ground Vehicle via Myo-Electrical Signals. Düzce University Journal of Science & Technology, vol. 8(1), pp. 233-245, 2020.
  • [13] M. J. Mathie, A. C. F. Coster, N. H. Lovell, B. G. Celler, Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, vol. 25(2), pp. 1-20, 2004.
  • [14] M. J. Mathie, B. G. Celler, N. H. Lovell, A. C. F. Coster, Classification of basic daily movements using a triaxial accelerometer. Medical & Biological Engineering & Computing, vol. 42(5), pp. 679-687, 2004.
  • [15] L. Bao, S. S. Intille, Activity recognition from user-annotated acceleration data. in Ferscha A, Mattern F (Eds.). Pervasive Computing, New York, USA. Springer-Verlag Berlin Heidelberg Press, pp. 1-17, 2004.
  • [16] P. H. Veltink, H. B. J. Bussmann, W. De Wries, W. L. J. Martens, R. C. Van Lummel, Detection of static and dynamic activities using uniaxial accelerometers. IEEE Transactions on Rehabilitation Engineering, vol. 4(4), pp. 375-385, 1996.
  • [17] K. Kiani, C. J. Snijders, E. S. Gelsema, Computerized analysis of daily life motor activity for ambulatory monitoring. Technology and Health Care, vol. 5(4), pp. 307-318, 1997.
  • [18] F. Foerster, M. Smeja, J. Fahrenberg, Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Computers in Human Behavior, vol. 15(5), pp. 571-583, 1999.
  • [19] D. M. Karatonis, M. R. Narayanan, M. Mathie, N. H. Lovell, B. G. Celler, Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology, vol. B10(1), pp. 156-157, 2006.
  • [20] F. R. Allen, E. Ambikairajah, N. H. Lovell, B. G. Celler, Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiological Measurement, vol. 27(10), pp. 935-951, 2006.
  • [21] B. Barshan, W. H. F. Durrant, Inertial navigation systems for mobile robots. IEEE Trans. Robotics Automation, vol. 11(3), pp. 328-342, 1995.
  • [22] B. Barshan, W. H. F. Durrant, Evaluation of a solid-state gyroscope for robotics applications. IEEE Transaction Instrumentation Measurement, vol. 44(1), pp. 61-67, 1995.
  • [23] B. Barshan, A. Yurtman, Classifying daily and sports activities invariantly to the positioning of wearable motion sensor units. IEEE Internet Things J., vol. 7, pp. 4801-4815, 2020.
  • [24] B. Barshan, M. C. Yüksek, Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal, vol. 57(11), pp. 649-667, 2014.
  • [25] S. Xia, L. Pei, Z. Zhang, W. Yu, R. C. Qui, Learning Disentangled Representation for Mixed- Reality Human Activity Recognition with a Single IMU Sensor. IEEE Transactions On Instrumentation and Measurement, vol. 70, 2514314, 2021.
  • [26] P. Blank, J. Hoßbach, D. Schuldhaus, B. M. Eskofier, Sensor-based stroke detection and stroke type classification in table tennis. In Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September 2015, pp. 93-100, 2015.
  • [27] N. G. Punchihewa, G. Yamako, Y. Fukao, E. Chosa, Identification of key events in baseball hitting using inertial measurement units. J. Biomech., 87, pp. 157-160, 2019.
  • [28] R. Ma, D. Yan, H. Peng, T. Yang, X. Sha, Y. Zhao, L. Liu, Basketball movements recognition using a wrist wearable inertial measurement unit. In Proceedings of the 2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS), Shenzhen, China, 5–7 December 2018, pp. 73-76, 2018.
  • [29] T. Kautz, B. H. Groh, J. Hannink, U. Jensen, H. Strubberg, B. M. Eskofier, Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Min. Knowl. Discov., 31, pp. 1678-1705, 2017.
  • [30] Z. Zhang, D. Xu, Z. Zhou, J. Mai, Z. He, Q. Wang, IMU-based underwater sensing system for swimming stroke classification and motion analysis. In Proceedings of the 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS), Beijing, China, 17–19 October 2017, pp. 268-272, 2017.
  • [31] R. Vleugels, B. V. Herbruggen, J. Fontaine, E. Poorter, Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics. Sensors, 21, 4650, 2021.
  • [32] M. Pal, G. M. Foody, Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience Remote Sensing, 48, pp. 2297-2307, 2010.
  • [33] O. Yaman, T. Tuncer, B. Tasar, DES-Pat: A novel DES pattern-based propeller recognition method using underwater acoustical sounds. Appl. Acoust., 175, 107859, 2021.
  • [34] A. Tharwat, T. Gaber, A. İbrahim, A. E. Hassanien, Linear discriminant analysis: A detailed tutorial. AI Communications, pp. 1-22, 2017.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Ali Erdem Koşun 0000-0002-5221-2879

Mehmet Yakup Atçı 0000-0003-0676-8224

Ahmet Burak Tatar 0000-0001-5848-443X

Alper Kadir Tanyıldızı 0000-0003-3324-5445

Beyda Taşar 0000-0002-4689-8579

Project Number MF 21.14
Early Pub Date December 31, 1899
Publication Date December 31, 2022
Submission Date September 5, 2022
Acceptance Date October 27, 2022
Published in Issue Year 2022 Volume: 11 Issue: 4

Cite

IEEE A. E. Koşun, M. Y. Atçı, A. B. Tatar, A. K. Tanyıldızı, and B. Taşar, “Design of Wearable Patient Lying Position Tracking and Warning System to Prevent Pressure Injury”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 11, no. 4, pp. 1073–1083, 2022, doi: 10.17798/bitlisfen.1171266.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS