Annals of Biomedical Engineering

, Volume 39, Issue 9, pp 2299–2312 | Cite as

Body-Worn Sensor Design: What Do Patients and Clinicians Want?

Article

Abstract

User preferences need to be taken into account in order to be able to design devices that will gain acceptance both in a clinical and home setting. Sensor systems become redundant if patients or clinicians do not want to work with them. The aim of this systematic review was to determine both patients’ and clinicians’ preferences for non-invasive body-worn sensor systems. A search for relevant articles and conference proceedings was performed using MEDLINE, EMBASE, Current Contents Connect, and EEEI explore. In total 843 papers were identified of which only 11 studies were deemed suitable for inclusion. A range of different clinically relevant user groups were included. The key user preferences were that a body-worn sensor system should be compact, embedded and simple to operate and maintain. It also should not affect daily behavior nor seek to directly replace a health care professional. It became apparent that despite the importance of user preferences, they are rarely considered and as such there is a lack of high-quality studies in this area. We therefore would like to encourage researchers to focus on the implications of user preferences when designing wearable sensor systems.

Keywords

Wearable sensors User preferences Medical devices User-centered design MEMS Biosensors 

References

  1. 1.
    Bonato, P. Wearable sensors and systems. IEEE Eng. Med. Biol. Mag. 29(3):25–36, 2010.PubMedCrossRefGoogle Scholar
  2. 2.
    Brownsell, S. J., D. A. Bradley, R. Bragg, P. Catlin, and J. Carlier. Do community alarm users want telecare? J. Telemed. Telecare. 6(4):199–204, 2000.PubMedCrossRefGoogle Scholar
  3. 3.
    Carter, J., and M. Rosen. Unobtrusive sensing of activities of daily living: a preliminary report. In: Proceedings of the First Joint BMES/EMBS Conference, Oct 13–16, 1999. IEEE Press, 1999, p. 678.Google Scholar
  4. 4.
    Chae, H., W. Cho, S. Kim, and K. Han. Usability studies on sensor smart clothing. In: Human-Computer Interaction Ambient, Ubiquitous and Intelligent Interaction, edited by J. Jacko. Berlin/Heidelberg: Springer, 2009, pp. 725–730.CrossRefGoogle Scholar
  5. 5.
    Dunne, L. E., S. Brady, B. Smyth, and D. Diamond. Initial development and testing of a novel foam-based pressure sensor for wearable sensing. J. Neuroeng. Rehabil. 2(1):4, 2005.PubMedCrossRefGoogle Scholar
  6. 6.
    Durfee, W., J. Carey, D. Nuckley, and J. Deng. Design and implementation of a home stroke telerehabilitation system. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009:2422–2425, 2009.PubMedGoogle Scholar
  7. 7.
    Fensli, R., and E. Boisen. Human factors affecting the patient’s acceptance of wireless biomedical sensors. In: Biomedical Engineering Systems and Technologies, edited by A. Fred, J. Filipe, and H. Gamboa. Berlin, Heidelberg: Springer, 2009, pp. 402–412.Google Scholar
  8. 8.
    Fensli, R., J. Dale, P. O’Reilly, J. O’Donoghue, D. Sammon, and T. Gundersen. Towards improved healthcare performance: examining technological possibilities and patient satisfaction with wireless body area networks. J. Med. Syst. 34(4):767–775, 2009.Google Scholar
  9. 9.
    Fensli, R., P. E. Pedersen, T. Gundersen, and O. Hejlesen. Sensor acceptance model—measuring patient acceptance of wearable sensors. Methods Inf. Med. 47(1):89–95, 2008.PubMedGoogle Scholar
  10. 10.
    Fraile, J., J. Bajo, J. Corchado, and A. Abraham. Applying wearable solutions in dependent environments. IEEE Trans. Inf. Technol. Biomed. 14(6):1459–1467, 2010.Google Scholar
  11. 11.
    Gamarnik, V., P. Shu, J. Malke, C. Chiu, K. Ben, J. Montes, et al. An integrated motion capture system for evaluation of neuromuscular disease patients. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009:218–221, 2009.PubMedGoogle Scholar
  12. 12.
    Gemperle, F., C. Kasabach, J. Stivoric, M. Bauer, and R. Martin. Design for wearability. In: Digest of Papers Second International Symposium on Wearable Computers, 1998, pp. 116–122.Google Scholar
  13. 13.
    Giansanti, D., S. Morelli, G. Maccioni, and G. Costantini. Toward the design of a wearable system for fall-risk detection in telerehabilitation. Telemed. J. E. Health 15(3):296–299, 2009.PubMedCrossRefGoogle Scholar
  14. 14.
    Giansanti, D., Y. Tiberi, G. Silvestri, and G. Maccioni. Toward the integration of novel wearable step-counters in gait telerehabilitation after stroke. Telemed. J. E. Health 15(1):105–111, 2009.PubMedCrossRefGoogle Scholar
  15. 15.
    Haggard, P., and D. M. Wolpert. Disorders of body schema. In: Higher-order Motor Disorders: From Neuroanatomy and Neurobiology to Clinical Neurology, edited by H.-J. Freund. Oxford: Oxford University Press, 2005, pp. 261–272.Google Scholar
  16. 16.
    Hong, X., C. Nugent, W. Liu, J. Ma, S. McClean, B. Scotney, et al. Uncertain information management for ADL monitoring in smart homes. In: Intelligent Patient Management, edited by S. McClean, P. Millard, E. El-Darzi, and C. Nugent. Berlin/Heidelberg: Springer, 2009, pp. 315–332.Google Scholar
  17. 17.
    Kumar, S., K. Kambhatla, F. Hu, M. Lifson, and Y. Xiao. Ubiquitous computing for remote cardiac patient monitoring: a survey. Int. J. Telemed. Appl. 2008:459185, 2008.Google Scholar
  18. 18.
    Kuper, A., L. Lingard, and W. Levinson. Critically appraising qualitative research. BMJ. 337:a1035, 2008.Google Scholar
  19. 19.
    Lukowicz, P., T. Kirstein, and G. Troster. Wearable systems for health care applications. Methods Inf. Med. 43(3):232–238, 2004.PubMedGoogle Scholar
  20. 20.
    Maluf-Filho, F. The importance of evidence-based medicine concepts for the clinical practitioner. Arq. Gastroenterol. 46(2):87–89, 2009.PubMedCrossRefGoogle Scholar
  21. 21.
    Mihailidis, A., A. Cockburn, C. Longley, and J. Boger. The acceptability of home monitoring technology among community-dwelling older adults and baby boomers. Assist. Technol. 20(1):1–12, 2008.PubMedCrossRefGoogle Scholar
  22. 22.
    Mountain, G., S. Wilson, C. Eccleston, S. Mawson, J. Hammerton, T. Ware, et al. Developing and testing a telerehabilitation system for people following stroke: issues of usability. J. Eng. Des. 21(2):223–236, 2010.CrossRefGoogle Scholar
  23. 23.
    Noury, N., A. Galay, J. Pasquier, and M. Ballussaud. Preliminary investigation into the use of Autonomous Fall Detectors. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008:2828–2831, 2008.PubMedGoogle Scholar
  24. 24.
    Pantelopoulos, A., and N. G. Bourbakis. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man. Cybern. C Appl. Rev. 40(1):1–12, 2010.CrossRefGoogle Scholar
  25. 25.
    Perry, M. A., P. A. Hendrick, L. Hale, G. D. Baxter, S. Milosavljevic, S. G. Dean, et al. Utility of the RT3 triaxial accelerometer in free living: an investigation of adherence and data loss. Appl. Ergon. 41(3):469–476, 2010.PubMedCrossRefGoogle Scholar
  26. 26.
    Prochazka, A., M. Gauthier, M. Wieler, and Z. Kenwell. The bionic glove: an electrical stimulator garment that provides controlled grasp and hand opening in quadriplegia. Arch. Phys. Med. Rehabil. 78(6):608–614, 1997.PubMedCrossRefGoogle Scholar
  27. 27.
    Rowlands, A. V. Accelerometer assessment of physical activity in children: an update. Pediatr. Exerc. Sci. 19(3):252–266, 2007.PubMedGoogle Scholar
  28. 28.
    Sawyer, C. Do it by design: an introduction to human factors in medical devices. FDA, 1997. http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm094957.htm.
  29. 29.
    Schleyer, T. K. L., T. P. Thyvalikakath, and J. Hong. What is user-centered design? J. Am. Dent. Assoc. 138(8):1081–1082, 2007.PubMedGoogle Scholar
  30. 30.
    Steele, R., A. Lo, C. Secombe, and Y. K. Wong. Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Inf. 78(12):788–801, 2009.CrossRefGoogle Scholar
  31. 31.
    Timmermans, A. A. A., H. A. M. Seelen, R. P. J. Geers, P. K. Saini, S. Winter, J. te Vrugt, et al. Sensor-based arm skill training in chronic stroke patients: results on treatment outcome, patient motivation, and system usability. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3):284–292, 2010.PubMedCrossRefGoogle Scholar
  32. 32.
    van Coevering, P., L. Harnack, K. Schmitz, J. Fulton, D. A. Galuska, and S. Gao. Feasibility of using accelerometers to measure physical activity in young adolescents. Med. Sci. Sports Exerc. 37(5):867–871, 2005.PubMedCrossRefGoogle Scholar
  33. 33.
    Veltink, P. H., H. B. Bussmann, W. de Vries, W. L. Martens, and R. C. Van Lummel. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehabil. Eng. 4(4):375–385, 1996.PubMedCrossRefGoogle Scholar
  34. 34.
    Wilson, S., R. Davies, T. Stone, J. Hammerton, P. Ware, S. Mawson, N. Harris, C. Eccleston, H. Zheng, N. Black, and G. Mountain. Developing a telemonitoring system for stroke rehabilitation. In: Proceedings of Ergonomics Society Annual Conference, 2007.Google Scholar

Copyright information

© Biomedical Engineering Society 2011

Authors and Affiliations

  1. 1.Medical Engineering Solutions in Osteoarthritis Centre of Excellence, Charing Cross HospitalImperial College LondonLondonUK
  2. 2.Human Performance Group, Department of Surgery and Cancer, Faculty of Medicine, Charing Cross HospitalImperial College LondonLondonUK

Personalised recommendations