Advertisement

Multimedia Tools and Applications

, Volume 75, Issue 22, pp 14351–14365 | Cite as

Study of medical device innovation design strategy based on demand analysis and process case base

  • Xin Guo
  • Jie Wang
  • Wu Zhao
  • Kai Zhang
  • Chen Wang
Article

Abstract

Process innovation is of very great significance for a medical device enterprise to improve its ability to solve problems, lower cost and enhance patients’ comfort. In order to inspire designers to realize innovation design based on the actual conditions of the medical device enterprise, this paper has proposed a concept of process innovation design oriented Web-based process case base system model based on demand mining and conversion. The system conducts demand mining based on product enterprise competitiveness model and features of existence-presentation model, utilizes TQCSE and 5W2H1E analysis approaches to assist the medical device designers in locking demands, taking QFD iterative construction as an example, constructs a demand conversion model featuring transition from engineering features to process features, takes innovative methods as logic mainline, and utilizes browser/sever to erect process case search and exhibition models through realization technique and application flow. This paper has demonstrated case base backstage realization and management methods, showcased system interface and demonstrated its effectiveness in process design based on actual medical device cases.

Keywords

Demand analysis Process innovation design Process case Innovation approaches Medical device 

Notes

Acknowledgments

This work was supported by NSFC (Natural Science Foundation of China) NO.51175357, NSFC NO.51435011 and Project on Innovative Method from the Ministry of Science and Technology of China NO.2013IM030500.

References

  1. 1.
    Adrien P, Joseph S, Donald H (2000) A soft-systems methodology approach for product and process innovation[J]. IEEE Trans Eng Manag 47(3):379–392CrossRefGoogle Scholar
  2. 2.
    Che L, Shahidehpour M (2014) Dc microgrids: economic operation and enhancement of resilience by hierarchical control[J]. Smart Grid, IEEE Trans 5(5):2517–2526CrossRefGoogle Scholar
  3. 3.
    Chen Z, Huang W, Lv Z et al. (2015) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection[J]. Multimed Tools Applic 1–15Google Scholar
  4. 4.
    Dang S, Ju J, Matthews D (2014) Efficient solar power heating system based on lenticular condensation[C]. Inform Sci, Electron Electric Eng (ISEEE), 2014 Int Conf IEEE 2:736–739CrossRefGoogle Scholar
  5. 5.
    Fey VR, Rivin EI (1999) Guided technology evolution(TRIZ technology forecasting)[J]. TRIZ J 1.Google Scholar
  6. 6.
    Goldie PA, Matyas TA, Evans OM et al (1996) Maximum voluntary weight bearing by the affected and unaffected legs instanding following stroke[J]. Clin Biomech 11(6):333–342CrossRefGoogle Scholar
  7. 7.
    Gu W, Lv Z, Hao M et al (2015) Change detection method for remote sensing images based on an improved Markov random field[J]. Multimed Tools Applic 1–16Google Scholar
  8. 8.
    Herrmann A, Huber F, Braunstein C (2000) Market-driven product and service design: bridging the gap between customer needs, quality management, and customer satisfaction[J]. Int J Prod Econ 66(1):77–96CrossRefGoogle Scholar
  9. 9.
    Jiang D, Hu G (2009) GARCH model-based large-scale IP traffic matrix estimation[J]. IEEE Commun Lett 13(1):52–54CrossRefGoogle Scholar
  10. 10.
    Jiang D, Xu Z, Chen Z et al (2011) Joint time–frequency sparse estimation of large-scale network traffic[J]. Comput Netw 55(15):3533–3547CrossRefGoogle Scholar
  11. 11.
    Jiang D, Xu Z, Zhang P et al (2014) A transform domain-based anomaly detection approach to network-wide traffic[J]. J Netw Comput Appl 40:292–306CrossRefGoogle Scholar
  12. 12.
    Jiang D, Ying X, Han Y et al. (2015) Collaborative multi-hop routing in cognitive wireless networks[J]. Wireless Person Commun 1–23Google Scholar
  13. 13.
    Li X, Lv Z, Hu J et al (2015) Traffic management and forecasting system based on 3d gis[J]. IEEE Int Sympos Cluster, Cloud Grid Comput (CCGrid)Google Scholar
  14. 14.
    Li Y, Tang J, Luo X (2009) An integrated method of rough set, Kano’s model and AHP for rating customer requirements’ final importance[J]. Expert Syst Applic 36(3):7045–7053CrossRefGoogle Scholar
  15. 15.
    Li X, Zhao W, Zheng Y et al. (2014) Innovative product design based on comprehensive customer requirements of different cognitive levels[J]. Scientific World JGoogle Scholar
  16. 16.
    Li T, Zhou X, Wang K et al. (2015) A convergence of key‐value storage systems from clouds to supercomputers[J]. Concurr Comput: Pract ExperienceGoogle Scholar
  17. 17.
    Lin Y, Yang J, Lv Z et al (2015) A self-assessment stereo capture model applicable to the internet of things[J]. Sensors 15(8):20925–20944CrossRefGoogle Scholar
  18. 18.
    Lv Z, Halawani A, Fen S et al. (2015) Touch-less interactive augmented reality game on vision based wearable device[J]. Person Ubiquitous ComputGoogle Scholar
  19. 19.
    Lv Z, Halawani A, Feng S (2014) Multimodal hand and foot gesture interaction for handheld devices[J]. ACM Trans Multimed Comput, Commun Applic (TOMM) 11(1):10Google Scholar
  20. 20.
    Lv Z, Li X, Hu J et al. (2015) Virtual geographic environment based coach passenger flow forecasting[J]. IEEE Comput Intell Virtual Environ Measure Syst Applic (CIVEMSA)Google Scholar
  21. 21.
    Lv Z, Tek A, Da Silva F et al (2013) Game on, science-how video game technology may help biologists tackle visualization challenges[J]. PLoS One 8(3):57990CrossRefGoogle Scholar
  22. 22.
    Savransky SD (2000) Engineering of creativity: introduction to TRIZ methodology of inventive problem solving[M]. CRC PressGoogle Scholar
  23. 23.
    Seliger G (2001) Product innovation-industrial approach[J]. CIRP Ann-Manufact Technol 50(07):425–443CrossRefGoogle Scholar
  24. 24.
    Su T, Wang W, Lv Z et al (2016) Rapid Delaunay triangulation for randomly distributed point cloud data using adaptive Hilbert curve[J]. Comput Graph 54:65–74CrossRefGoogle Scholar
  25. 25.
    Suh NP (1990) The principles of design[M]. Oxford University Press, New YorkGoogle Scholar
  26. 26.
    Umeda Y, Ishii M, Yoshioka M (1996) Supporting conceptual design based on the function-behavior-state modeler[J]. Artif Intell Eng, Design, Anal Manufac 10(04):275–288CrossRefGoogle Scholar
  27. 27.
    Utter back, JM, Abernathy WJ (1975) A dynamic model of process & product innovation[J]. Omega (3):639–656Google Scholar
  28. 28.
    Wang JJY, Huang JZ, Sun Y et al (2015) Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization[J]. Expert Syst Applic 42(3):1278–1286CrossRefGoogle Scholar
  29. 29.
    Wang K, Qiao K, Sadooghi I, et al. (2015) Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales[J]. Concurr Computat: Pract ExperienceGoogle Scholar
  30. 30.
    Wang Y, Su Y, Agrawal G et al. (2015) A novel approach for approximate aggregations over arrays[C]. Proceedings of the 27th International Conference on Scientific and Statistical Database Management. ACM 4Google Scholar
  31. 31.
    Wang C, Zhao W, Wang J (2015) The optimizing strategy of systematic process innovation based on QFD TRIZ and AHP. Appl Math Inform Sci 9(3):1593–1604Google Scholar
  32. 32.
    Xin Z, Yansong X (2009) Preliminary explore on development strategies of colleges and universities; proceedings of the computational intelligence and software engineering[C], CISE2009 Int Conf, IEEEGoogle Scholar
  33. 33.
    Yan L, Li X-l, Zhao W, Wang J (2005) Research on product creative design with cognitive psychology[J]. Comput Integr Manuf Syst 11(09):1201–1207Google Scholar
  34. 34.
    Yan L, Wang J (2007) Design creativity in produce innovation[J]. Int J Adv Manufac Technol 33(3–4):213–222Google Scholar
  35. 35.
    Yang J, Chen B, Zhou J et al (2015) A low-power and portable biomedical device for respiratory monitoring with a stable power source[J]. Sensors 15(8):19618–19632CrossRefGoogle Scholar
  36. 36.
    Yang J, Liu Y, Meng Q et al. (2015) Objective evaluation criteria for stereo camera shooting quality under different shooting parameters and shooting distances[J]Google Scholar
  37. 37.
    Zhang X, Xu Z, Henriquez C et al. (2013) Spike-based indirect training of a spiking neural network-controlled virtual insect[C]. Decision Contrl (CDC), 2013 I.E. 52nd Ann Conf IEEE 6798–6805Google Scholar
  38. 38.
    Zhang S, Zhang X, Ou X et al. (2014) After we knew it: empirical study and modeling of cost-effectiveness of exploiting prevalent known vulnerabilities across iaas cloud[C]. Proceedings of the 9th ACM symposium on information, computer and communications security. ACM 317–328Google Scholar
  39. 39.
    Zou R-l, Zhao J, Xun X-l, Hu X-f (2015) Structure design and calculation analysis for double gasbags baesd on body weight support treadmill training[J]. J Med Biomech 6(3):226–232Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Manufacturing Science and EngineeringSichuan UniversityChengduChina

Personalised recommendations