IJAT Vol.12 No.4 pp. 542-552
doi: 10.20965/ijat.2018.p0542


Visualization of Acquisition Experience in Sternal Compression Maneuver Using Kinect Sensoring: For Co-Creation of Medical Technique Experiential Values

Nao Sato*1,†, Kenju Akai*2, Makoto Hirose*3, Satoru Okamoto*4, and Kenji Karino*5

*1Clinical SkillUp Center, Shimane University Hospital
89-1 Enya, Izumo, Shimane 693-8501, Japan

Corresponding author

*2Center for Community-based Healthcare Research and Education, Shimane University, Izumo, Japan

*3Department of Information Engineering, National Institute of Technology, Matsue College, Matsue, Japan

*4Interdisciplinary Graduate School of Science and Engineering, Shimane University, Matsue, Japan

*5Master’s Course for Instructor of Medical Simulation, Graduate School of Medical Research, Shimane University, Izumo, Japan

October 9, 2017
April 19, 2018
Online released:
July 3, 2018
July 5, 2018
Kinect, sensoring, clinical skill improvement, CPR, skill education

In this study, we compare and verify data that have been rendered visual by Kinect sensoring with data obtained by conventional devices. This is in order to promote the co-creation of experiential values in the acquisition of a sternal compression technique in basic life support in the context of the improvement of the clinical skills of physicians and healthcare professionals. We find that Kinect sensoring is sufficiently accurate to evaluate measurements of the rate and depth of sternal compression; it is comparable to accelerometers and sternal compression-dedicated sensoring devices. This provides a platform for the co-creation of experiential values for the improvement of clinical skills based on the acquisition of medical techniques using the Kinect sensor, which is low in cost and easy to use. It also provides a platform for the exchange of sensor-captured information between the instructor and trainee. It is hoped that this will lead to the co-creation of values useful for the development of educational services in life-saving medical techniques.

Cite this article as:
N. Sato, K. Akai, M. Hirose, S. Okamoto, and K. Karino, “Visualization of Acquisition Experience in Sternal Compression Maneuver Using Kinect Sensoring: For Co-Creation of Medical Technique Experiential Values,” Int. J. Automation Technol., Vol.12, No.4, pp. 542-552, 2018.
Data files:
  1. [1] Analysis of the Labor Economy, Ministry of Health, Labour and Welfare, 2016 (in Japanese), [Accessed May 2, 2017]
  2. [2] T. Arai and Y. Shimomura, “Service Engineering: How to accelerate servitalization for products,” Hitotsubashi Business Review, Vol.54, No.2, pp. 52-69, 2006 (in Japanese).
  3. [3] K. Tomoda, “Development of Otolaryngology surgery education at Ergonomics,” Oto-Rhino-Laryngological Society of Japan, Vol.4, pp. 330-336, 2016 (in Japanese).
  4. [4] Y. Hashimoto and S. Ishiguro, “Co-creation of ‘experience value’ by E-learning: Skill education service,” Introduction for Serviceology: Service Innovation by Value Co-creation (edited by T. Murakami, T. Arai, and JST Reserch Institute of Science and Technology for Society), University of Tokyo Press, pp. 117-142, 2017 (in Japanese).
  5. [5] P. A. Meaney, B. J. Bobrow, M. E. Mancini, J. Christenson, A. R. de Caen, F. Bhanji, B. S. Abella, M. E. Kleinman, D. P. Edelson, R. A. Berg, T. P. Aufderheide, V. Menon, and M. Leary, “Cardiopulmonary resuscitation quality: improving cardiac resuscitation outcomes both inside and outside the hospital: a consensus statement from the American Heart Association,” Circulation, Vol.128, No.4, pp. 417-435, 2013.
  6. [6] Japan Resuscitation Council Resuscitation Guidelines 2015 (in Japanese), [Accessed May 2, 2017]
  7. [7] K. Ueda, H. Asama, and T. Takenaka, “Value of Artifacts and Service Study,” J. of Japanese Society for Artificial Intelligence, Vol.23, No.6, pp. 728-735, 2008 (in Japanese).
  8. [8] K. Aoki, K. Akai, K. Ujiie, T. Shinmura, and N. Nishino, “An actual purchasing experiment for investigating the effects of eco-information on consumers’ environmental consciousness and attitudes towards agricultural products,” Int. J. Automation Technol., Vol.8, No.5, pp. 688-697, 2014.
  9. [9] J. Nakagawa, Q. An, Y. Ishikawa, K. Yanai, W. Wen, H. Yamakawa, and H. Asama, “Extraction and evaluation of proficiency in bed care motion for education service of nursing skill,” Serviceology for Designing the Future, Springer, pp. 217-227, 2016.
  10. [10] K. Yanai, Q. An, Y. Ishikawa, J. Nakagawa, W. Wen, H. Yamakawa, and H. Asama, “Visualization of Muscle Activity During Squat Motion for Skill Education,” Serviceology for Designing the Future, Springer, pp. 205-215, 2016.
  11. [11] N. Kawarazaki and T. Yoshidome, “Communication Robot Based on Image Processing and Voice Recognition,” Int. J. Automation Technol., Vol.5, No.6, pp. 900-907, 2011.
  12. [12] N. Koceska, S. Koceski, V. Sazdovski, and D. Ciambrone, “Robotic Assistant for Elderly Care – Development and Evaluation,” Int. J. Automation Technol., Vol.11, No.3, pp. 425-432, 2017.
  13. [13] Highlights of the 2015 American Heart Association Guidelines Update For CPR and ECC (in Japanese), [Accessed May 2, 2017]
  14. [14] T. Ikeyama, Y. Shiima, T. Shiga, S. Takeda, S. Dohi, and A. Nishisaki “The Debriefing Assessment for Simulation in Healthcare (DASH™) Japanese translation,” Medical Education, Vol.45, No.4, pp. 293-295, 2014 (in Japanese).
  15. [15] S. K. Beckers, M. H. Skorning, M. Fries, J. Bickenbach, S. Beuerlein, M. Derwall, and R. Rossaint, “CPREzy™ improves performance of external chest compressions in simulated cardiac arrest,” Resuscitation, Vol.72, No.1, pp. 100-107, 2007.
  16. [16] S. Hashimoto, “A program of Kinect with a Processing: The 1st,” J. of Information Processing, Vol.53, No.8, pp. 817-822, 2012 (in Japanese).
  17. [17] S. Hashimoto, “A program of Kinect with a Processing: The 2nd,” J. of Information Processing, Vol.53, No.9, pp. 949-954, 2012 (in Japanese).
  18. [18] A. R. John, M. Manivannan, and T. V. Ramakrishnan, “Computer-Based CPR Simulation Towards Validation of AHA/ERC Guidelines,” Cardiovascular Engineering and Technology, Vol.8, No.2, pp. 229-235, 2017.
  19. [19] J. D. Wilson, J. Khan-Perez, D. Marley, S. Buttress, M. Walton, B. Li, and B. Roy, “Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant (MIRA) software?,” J. of Shoulder and Elbow Surgery, Vol.26, No.12, pp. e382-e389, 2017.
  20. [20] A. Krikscionaitiene, K. Stasaitis, M. Dambrauskiene, Z. Dambrauskas, E. Vaitkaitiene, P. Dobozinskas, and D. Vaitkaitis, “Can lightweight rescuers adequately perform CPR according to 2010 resuscitation guideline requirements?,” Emergency Medical J., Vol.30, No.2, pp. 159-160, 2013.
  21. [21] A. Ashton, A. McCluskey, C. L. Gwinnutt, and A. M. Keenan, “Effect of rescuer fatigue on performance of continuous external chest compressions over 3 min,” Resuscitation, Vol.55, No.2, pp. 151-155, 2002.
  22. [22] E. Contri, S. Cornara, A. Somaschini, C. Dossena, M. Tonani, F. Epis, and E. Baldi, “Complete chest recoil during laypersons’ CPR: Is it a matter of weight?,” American J. of Emergency Medicine, Vol.35, No.9, pp. 1266-1268, 2017.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jul. 19, 2018