Skip to main content
SpringerLink
Log in

Real-Time Pervasive Monitoring for Postoperative Care

  • Conference paper
4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 13))

Abstract

Post surgical care is an important part of the surgical recovery process. With the introduction of minimally invasive surgery (MIS), the recovery time of patients has been shortened significantly. This has led to a shift of postoperative care from hospital to home environment. To prevent the occurrence of adverse events, the care of these patients is mainly relied on routine visits by home-care nurses. This type of episodic examination can only capture a snapshot of the overall recovery process, and many early signs of potential complication can go undetected. The development of Body Sensor Networks (BSNs) has enabled the use of miniaturised wireless sensors for continuous monitoring of postoperative patients. This paper examines the potential of processing-on-node algorithms for further reducing the wireless bandwidth, and therefore the overall power consumption of the sensors. The accuracy and robustness of the technique are demonstrated with lab experiments and a preliminary clinical case study.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Patti, M.G., et al. (1999) Minimally Invasive Surgery for Achalasia: An 8-Year Experience With 168 Patients. Annals of Surgery. 230(4): p. 587.

    Article  Google Scholar 

  2. Cohn, L.H., et al. (1997) Minimally Invasive Cardiac Valve Surgery Improves Patient Satisfaction While Reducing Costs of Cardiac Valve Replacement and Repair. Annals of Surgery. 226(4): p. 421–428.

    Article  Google Scholar 

  3. Delaney, C.P., et al. (2001) ‘Fast track’ postoperative management protocol for patients with high comorbidity undergoing complex abdominal and pelvic colorectal surgery. British Journal of Surgery. 88: p. 1533–1538.

    Article  Google Scholar 

  4. Bokey, E.L., et al. (1995) Postoperative morbidity and mortality following resection of the colon and rectum for cancer. Diseases of the Colon & Rectum. 38(5): p. 480–487.

    Article  Google Scholar 

  5. McClane, S., A. Senagore, and P. Marcello (2000) Experience-based postoperative care in laparoscopic-assisted colectomy reduces length of stay. Diseases of the Colon & Rectum. 43: p. A54.

    Article  Google Scholar 

  6. Bardram, L., et al. (1995) Recovery after laparoscopic colonic surgery with epidural analgesia, and early oral nutrition and mobilisation. Lancet. 345(8952): p. 763–764.

    Article  Google Scholar 

  7. Binderow, S.R., et al. (1994) Must early postoperative oral intake be limited to laparoscopy? Diseases of the Colon & Rectum. 37(6): p. 584–589.

    Article  Google Scholar 

  8. Reissman, P., et al. (1995) Is early oral feeding safe after elective colorectal surgery? A prospective randomized trial. Annals of Surgery. 222(1): p. 73–77.

    Article  Google Scholar 

  9. Boschert, S. (2004) Early discharge OK in laparoscopic hysterectomy: avoiding an overnight stay-gynecology. Ob. Gyn News: p. 8.

    Google Scholar 

  10. Booth, J.E., et al. (2004) A trial of early discharge with homecare compared to conventional hospital care for patients undergoing coronary artery bypass grafting. Heart. 90: p. 1344–1345.

    Article  Google Scholar 

  11. Engelman, R., et al. (1994) Fast-track recovery of the coronary bypass patient. The Annals of Thoracic Surgery. 58(6): p. 1742–1746.

    Article  Google Scholar 

  12. Myles, P., et al. (2000) Validity and reliability of a postoperative quality of recovery score: the QoR-40. British Journal of Anaesthesia. 84(1): p. 11–15.

    Google Scholar 

  13. Inoue, Y., et al. (2003) Is laparoscopic colorectal surgery less invasive than classical open surgery? Surgical Endoscopy. 17(8): p. 1269–1273.

    Article  Google Scholar 

  14. Inoue, Y., et al. (2003) A New Parameter for Assessing Postoperative Recovery of Physical Activity Using an Accelerometer. Surgery Today 2003. 33: p. 645–650.

    Article  Google Scholar 

  15. Aziz, O., et al. (2006). Pervasive Body Sensor Network: An Approach to Monitoring the Post-operative Surgical Patient In The International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006). MIT, Boston, USA.

    Google Scholar 

  16. Yang, G.-Z., et al. (2004). From Sensor Networks to Behaviour Profiling: A Homecare Perspective of Intelligent Building. In The IEE Seminar for Intelligent Buildings.

    Google Scholar 

  17. Lo, B. and G.-Z. Yang. (2005). Architecture for Body Sensor Networks. In The Perspective in Pervasive Computing. IEE Savoy Place: pp. 23–28.

    Google Scholar 

  18. Rhee, S., et al. (1998). The Ring Sensor: a New Ambulatory Wearable Sensor for Twenty-Four Hour Patient Monitoring. In The 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Hong Kong.

    Google Scholar 

  19. Linz, T., et al. (2005). Fully untegrated EKG shirt based on embroidered electrical interconnections with conductive yarn and miniaturized flexible electronics. In International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006). MIT Boston USA.

    Google Scholar 

  20. Venkatasubramanian, K., et al. (2005). Ayushman: A Wireless Sensor Network Based Health Monitoring Infrastructure and Testbed. In IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). Marina del Rey, CA, USA.

    Google Scholar 

  21. Amft, O., H. Junker, and G. Troster. (2005). Detection of eating and drinking arm gestures using inertial body-worn sensors. In Ninth IEEE International Symposium on Wearable Computers: pp. 160–163.

    Google Scholar 

  22. S. Thiemjarus, et al. (2004). Feature Selection for Wireless Sensor Networks. In the 1st International Workshop on Wearable and Implantable Body Sensor Networks. Imperial, London, UK.

    Google Scholar 

  23. Minnen, D., et al. (2005). Recognizing and Discovering Human Actions from On-Body Sensor Data. In IEEE International Conference on Multimedia and Expo, 2005 (ICME 2005): pp. 1545–1548.

    Google Scholar 

  24. Bao, L. and S.S. Intille. (2004). Activity Recognition from User-Annotated Acceleration Data. In The 2nd International Conference on Pervasive Computing PERVASIVE 2004: pp. 1–17.

    Google Scholar 

  25. Ravi, N., et al. (2005). Activity Recognition from Accelerometer Data. In The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference. Pittsburgh, Pennsylvania, USA: pp. 1541–1546.

    Google Scholar 

  26. Caros, J.S.i., et al. (2005). Very Low Complexity Algorithm for Ambulatory Activity Classification. In 3rd European Medical and Biological Conference EMBEC 2005.

    Google Scholar 

  27. Parkka, J., et al. (2006) Activity Classification Using Realistic Data From Wearable Sensors. IEEE Transactions on Information Technology in Biomedicine. 10(1): p. 119–128.

    Article  Google Scholar 

  28. Lester, J., et al. (2005). A Hybrid Discriminative/ Generative Approach for Modeling Human Activities. In The Nineteenth International Joint Conference on Artificial Intelligence. Edinburgh, Scotland.

    Google Scholar 

  29. Nurmi, P. and P. Floreen. (2005). Online feature selection for contextual time series data. In Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop (SLSFS05).

    Google Scholar 

  30. Thiemjarus, S., et al. (2004). Feature Selection for Wireless Sensor Networks. In the 1st International Workshop on Wearable and Implantable Body Sensor Networks. Imperial, London, UK.

    Google Scholar 

  31. Mathie, M.J., et al. (2004) Classification of basic daily movements using a triaxial accelerometer. Biomedical and Life Sciences, Engineering and Medicine. 42(5): p. 679–687.

    Google Scholar 

  32. Lester, J., T. Choudhury, and G. Borriello. (2006). A Practical Approach to Recognizing Physical Activities. In PERVASIVE 2006. Dublin Ireland: pp. 1–16.

    Google Scholar 

  33. DeVaul, R.W. and S. Dunn (2001) Real-Time Motion Classification for Wearable Computing Applications. MIT Media Laboratory

    Google Scholar 

  34. Karantonis, D.M., et al. (2006) Implementation of a realtime human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans on Information Technology in Biomedicine. 10(1): p. 156–167.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, UK

    Benny Lo, Louis Atallah, Mohammed El ElHew & Guang-Zhong Yang

  2. Department of Biosurgery and Surgical Technology, Imperial College London, London

    Omer Aziz & Ara Darzi

Authors
  1. Benny Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louis Atallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omer Aziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed El ElHew
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ara Darzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guang-Zhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benny Lo .

Editor information

Editors and Affiliations

  1. Helmholtz Institute of Biomedical Engineering, RWTH Aachen University, Pauwelstr. 20, 52074, Aachen, Germany

    Steffen Leonhardt (Chair of Medical Information Technology) (Chair of Medical Information Technology)

  2. Connectivity Systems Group, Philips Research Europe, Weisshausstr. 2, 52066, Aachen, Germany

    Thomas Falck

  3. RWTH Aachen University, Kackertstr. 9, 52072, Aachen, Germany

    Petri Mähönen (Chair of Wireless Networks) (Chair of Wireless Networks)

Rights and permissions

Reprints and permissions

Copyright information

© 2007 International Federation for Medical and Biological Engineering

About this paper

Cite this paper

Lo, B., Atallah, L., Aziz, O., El ElHew, M., Darzi, A., Yang, GZ. (2007). Real-Time Pervasive Monitoring for Postoperative Care. In: Leonhardt, S., Falck, T., Mähönen, P. (eds) 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007). IFMBE Proceedings, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70994-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70994-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70993-0

  • Online ISBN: 978-3-540-70994-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation