Skip to main content

Advertisement

Springer Nature Link
Log in

Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The development of wireless body area sensor networks is imperative for modern telemedicine. However, attackers and cybercriminals are gradually becoming aware in attacking telemedicine systems, and the black market value of protected health information has the highest price nowadays. Security remains a formidable challenge to be resolved. Intelligent home environments make up one of the major application areas of pervasive computing. Security and privacy are the two most important issues in the remote monitoring and control of intelligent home environments for clients and servers in telemedicine architecture. The personal authentication approach that uses the finger vein pattern is a newly investigated biometric technique. This type of biometric has many advantages over other types (explained in detail later on) and is suitable for different human categories and ages. This study aims to establish a secure verification method for real-time monitoring systems to be used for the authentication of patients and other members who are working in telemedicine systems. The process begins with the sensor based on Tiers 1 and 2 (client side) in the telemedicine architecture and ends with patient verification in Tier 3 (server side) via finger vein biometric technology to ensure patient security on both sides. Multilayer taxonomy is conducted in this research to attain the study’s goal. In the first layer, real-time remote monitoring studies based on the sensor technology used in telemedicine applications are reviewed and analysed to provide researchers a clear vision of security and privacy based on sensors in telemedicine. An extensive search is conducted to identify articles that deal with security and privacy issues, related applications are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on mHealth in telemedicine based on sensors. A total of 3064 papers are collected from 2007 to 2017. The retrieved articles are filtered according to the security and privacy of telemedicine applications based on sensors. Nineteen articles are selected and classified into two categories. The first category, which accounts for 57.89% (n = 11/19), includes surveys on telemedicine articles and their applications. The second category, accounting for 42.1% (n = 8/19), includes articles on the three-tiered architecture of telemedicine. The collected studies reveal the essential need to construct another taxonomy layer and review studies on finger vein biometric verification systems. This map-matching for both taxonomies is developed for this study to go deeply into the sensor field and determine novel risks and benefits for patient security and privacy on client and server sides in telemedicine applications. In the second layer of our taxonomy, the literature on finger vein biometric verification systems is analysed and reviewed. In this layer, we obtain a final set of 65 articles classified into four categories. In the first category, 80% (n = 52/65) of the articles focus on development and design. In the second category, 12.30% (n = 8/65) includes evaluation and comparative articles. These articles are not intensively included in our literature analysis. In the third category, 4.61% (n = 3/65) includes articles about analytical studies. In the fourth category, 3.07% (n = 2/65) comprises reviews and surveys. This study aims to provide researchers with an up-to-date overview of studies that have been conducted on (user/patient) authentication to enhance the security level in telemedicine or any information system. In the current study, taxonomy is presented by explaining previous studies. Moreover, this review highlights the motivations, challenges and recommendations related to finger vein biometric verification systems and determines the gaps in this research direction (protection of finger vein templates in real time), which represent a new research direction in this area.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Doumbouya, M. B., Kamsu-Foguem, B., Kenfack, H., and Foguem, C., A framework for decision making on teleexpertise with traceability of the reasoning. IRBM 36(1):40–51, 2015.

    Google Scholar 

  2. Kalid, N. et al., Based on real time remote health monitoring systems: A new approach for prioritization ‘large scales data’ patients with chronic heart diseases using body sensors and communication technology. J. Med. Syst. 42(4):69, 2018.

    PubMed  Google Scholar 

  3. Iqbal, S. et al., Real-time-based E-health systems: Design and implementation of a lightweight key management protocol for securing sensitive information of patients. Health Technol. (Berl)., 2018.

  4. Kalid, N. et al., Based real time remote health monitoring systems: A review on patients prioritization and related ‘big data’ using body sensors information and communication technology. J. Med. Syst. 42(2):30, 2017.

    PubMed  Google Scholar 

  5. Hamdi, O., Chalouf, M. A., Ouattara, D., and Krief, F., eHealth: Survey on research projects, comparative study of telemonitoring architectures and main issues. J. Netw. Comput. Appl. 46:100–112, 2014.

    Google Scholar 

  6. Abdulnabi, M. et al., A distributed framework for health information exchange using smartphone technologies. J. Biomed. Inform. 69:230–250, 2017.

    PubMed  Google Scholar 

  7. Rajan, S. P., Review and investigations on future research directions of mobile based telecare system for cardiac surveillance. Rev. Mex. Trastor. Aliment. 13(4):454–460, 2015.

    Google Scholar 

  8. Negra, R., Jemili, I., and Belghith, A., Wireless body area networks : Applications and technologies. Procedia - Procedia Comput. Sci. 83:1274–1281, 2016.

    Google Scholar 

  9. Salman, O. H. et al., Novel methodology for triage and prioritizing using ‘big data’ patients with chronic heart diseases through telemedicine environmental. Int. J. Inf. Technol. Decis. Mak. 16(05):1211–1245, Sep. 2017.

    Google Scholar 

  10. Xiao, Y. and Chen, H., Mobile telemedicine: A computing and networking perspective. Auerbach Publications, 2008.

  11. Sene, A., Kamsu-Foguem, B., and Rumeau, P., Telemedicine framework using case-based reasoning with evidences. Comput. Methods Programs Biomed. 121(1):21–35, 2015.

    CAS  PubMed  Google Scholar 

  12. Zaidan, A. A. et al., Multi-criteria analysis for OS-EMR software selection problem: A comparative study. Decis. Support Syst. 78:15–27, 2015.

    Google Scholar 

  13. Kiah, M. L. M. et al., Open source EMR software: Profiling, insights and hands-on analysis. Comput. Methods Programs Biomed. 117(2):360–382, 2014.

    CAS  PubMed  Google Scholar 

  14. Zaidan, A. A. et al., Challenges, alternatives, and paths to sustainability: Better public health promotion using social networking pages as key tools. J. Med. Syst. 39(2):7, 2015.

    CAS  PubMed  Google Scholar 

  15. Zaidan, B. B. et al., A security framework for Nationwide health information exchange based on telehealth strategy. J. Med. Syst. 39(5):1–19, 2015.

    Google Scholar 

  16. Hayajneh, T., Mohd, B. J., Imran, M., Almashaqbeh, G., and Vasilakos, A. V., Secure authentication for remote patient monitoring with wireless medical sensor networks. Sensors (Basel). 16(4):424, 2016.

    PubMed  PubMed Central  Google Scholar 

  17. Hussain, M. et al., Conceptual framework for the security of mobile health applications on android platform. Telemat. Inform. 35(5):1335–1354, 2018.

    Google Scholar 

  18. Hussain, M. et al., A security framework for mHealth apps on android platform. Comput. Secur. 75:191–217, 2018.

    Google Scholar 

  19. Alanazi, H. O. et al., Meeting the security requirements of electronic medical records in the ERA of high-speed computing. J. Med. Syst. 39(1):165, 2015.

    CAS  PubMed  Google Scholar 

  20. Kiah, M. L. M., Nabi, M. S., Zaidan, B. B., and Zaidan, A. A., An enhanced security solution for electronic medical records based on AES hybrid technique with SOAP/XML and SHA-1. J. Med. Syst. 37(5):9971, 2013.

    PubMed  Google Scholar 

  21. Hamdan, O. A. et al., Securing electronic medical records transmissions over unsecured communications: An overview for better medical governance. J. Med. Plants Res. 4(19):2059–2074, 2010.

    Google Scholar 

  22. Mohd Asaari, M. S., Suandi, S. A., and Rosdi, B. A., Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Syst. Appl. 41(7):3367–3382, 2014.

    Google Scholar 

  23. Da Wu, J., and Liu, C. T., Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Syst. Appl. 38(5):5423–5427, 2011.

    Google Scholar 

  24. M. Hussain et al., “The rise of keyloggers on smartphones: A survey and insight into motion-based tap inference attacks,” Pervasive and Mobile Computing, vol. 25. Elsevier, pp. 1–25, 01-Jan-2016.

  25. L. Dong, G. Yang, Y. Yin, F. Liu, and X. Xi, “Finger vein verification based on a personalized best patches map Lumei,” 2012.

    Google Scholar 

  26. Da Wu, J., and Ye, S. H., Driver identification using finger-vein patterns with Radon transform and neural network. Expert Syst. Appl. 36(3 PART 2):5793–5799, 2009.

    Google Scholar 

  27. Masys, D., Baker, D., and Butros, A., Giving patients access to their medical records via the internet. Am. Med. 9(2):181–191, 2002.

    Google Scholar 

  28. Rosdi, B. A., Shing, C. W., and Suandi, S. A., Finger vein recognition using local line binary pattern. Sensors 11(12):11357–11371, 2011.

    PubMed  Google Scholar 

  29. Suzuki, H., Suzuki, M., Urabe, T., and Obi, T., Secure biometric image sensor and authentication scheme based on compressed sensing. Appl. Opt. 52(33):8161–8168, 2013.

    PubMed  Google Scholar 

  30. Qin, H., He, X., Yao, X., and Li, H., Finger-vein verification based on the curvature in radon space. Expert Syst. Appl. 82:151–161, 2017.

    Google Scholar 

  31. Zaidan, A. A. et al., A survey on communication components for IoT-based technologies in smart homes. Telecommun. Syst. 69(1):1–25, 2018.

    Google Scholar 

  32. Z. T. Al-qaysi et al., “A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations,” Comput. Methods Programs Biomed., vol. 164, Elsevier, pp. 221–237, 2018.

  33. Albahri, A. S. et al., Real-time fault-tolerant mhealth system: comprehensive review of healthcare services, opens issues, challenges and methodological aspects. J. Med. Syst. 42(8):137, 2018.

    CAS  PubMed  Google Scholar 

  34. Zaidan, A. A. and Zaidan, B. B., A review on intelligent process for smart home applications based on IoT: Coherent taxonomy, motivation, open challenges, and recommendations, Artif. Intell. Rev., 1–25, 2018.

  35. Hamada, M. et al., A systematic review for human EEG brain signals based emotion classification, feature extraction, brain condition, Group Comparison. J. Med. Syst. 42(9):162, 2018.

    PubMed  Google Scholar 

  36. M. A. Alsalem et al., “A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations,” Comput. Methods Programs Biomed., vol. 158. Elsevier, pp. 93–112, 2018.

  37. Brian, R. M., and Ben-Zeev, D., Mobile health (mHealth) for mental health in Asia: Objectives, strategies, and limitations. Asian J. Psychiatr. 10(2014):96–100, 2014.

    PubMed  Google Scholar 

  38. Iwaya, L. H. et al., Mobile health in emerging countries: A survey of research initiatives in Brazil. Int. J. Med. Inform. 82(5):283–298, 2013.

    CAS  PubMed  Google Scholar 

  39. Obi, T., Ishmatova, D., and Iwasaki, N., Promoting ICT innovations for the ageing population in Japan. Int. J. Med. Inform. 82(4):e47–e62, 2013.

    PubMed  Google Scholar 

  40. Review of Mobile Health Technology for Military Mental Health. Mil. Med. 179, no. 8, 865–878, 2014.

  41. Adams, Z. W., McClure, E. A., Gray, K. M., Danielson, C. K., Treiber, F. A., and Ruggiero, K. J., Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. J. Psychiatr. Res. 85:1–14, 2017.

    Google Scholar 

  42. Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre Díez, I., López-Coronado, M., and Saleem, K., Mobile-health: A review of current state in 2015. J. Biomed. Inform. 56:265–272, 2015.

    PubMed  Google Scholar 

  43. Point, C., Accreditation, E., and Benton, D., Health care delivery. J. Nurs. Regul. 7(4):S12–S16, 2017.

    Google Scholar 

  44. Schulmeister, L., Technology and the transformation of oncology care. Semin. Oncol. Nurs. 32(2):99–109, 2016.

    PubMed  Google Scholar 

  45. Reeder, B., Meyer, E., Lazar, A., Chaudhuri, S., Thompson, H. J., and Demiris, G., Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. Int. J. Med. Inform. 82(7):565–579, 2013.

    PubMed  PubMed Central  Google Scholar 

  46. Albahri, O. S. et al., Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: Taxonomy, open challenges, motivation and recommendations. J. Med. Syst. 42(5):80, 2018.

    CAS  PubMed  Google Scholar 

  47. A. Lounis, A. Hadjidj, A. Bouabdallah, and Y. Challal, Healing on the cloud: Secure cloud architecture for medical wireless sensor networks. Future Gen. Comp. Syst. 55: 266–277 (2016)

    Google Scholar 

  48. Saleem, K., Derhab, A., Al-Muhtadi, J., and Shahzad, B., Human-oriented design of secure machine-to-machine communication system for e-healthcare society. Comput. Human Behav. 51(Part B):977–985, 2015.

    Google Scholar 

  49. A. S. Albahri et al., “Real-time fault-tolerant mhealth system: comprehensive review of healthcare services, opens issues, challenges and methodological aspects,” Journal of Medical Systems, vol. 42, no. 8. Springer, p. 137, 2018.

  50. Albahri, O. S. et al., Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. J. Med. Syst. 42(9):164, 2018.

    CAS  PubMed  Google Scholar 

  51. Zhang, K., Liang, X., Baura, M., Lu, R., and Sherman Shen, X., PHDA: A priority based health data aggregation with privacy preservation for cloud assisted WBANs. Inf. Sci. (Ny). 284:130–141, 2014.

    Google Scholar 

  52. Moreno, S., Quintero, A., Ochoa, C., Bonfante, M., Villareal, R., and Pestana, J., Remote monitoring system of vital signs for triage and detection of anomalous patient states in the emergency room. 2016 XXI symposium on signal processing, images and artificial vision (STSIVA), 2016, no. 1–5, 2015.

  53. Baehr, D., McKinney, S., Quirk, A., and Harfoush, K., On the practicality of elliptic curve cryptography for medical sensor networks. 2014 11th annual high capacity optical networks and emerging/enabling technologies (photonics for energy). 41–45, 2014.

  54. de la Piedra, A., Braeken, A., Touhafi, A., and Wouters, K., Secure event logging in sensor networks. Comput. Math. with Appl. 65(5):762–773, 2013.

    Google Scholar 

  55. Hedin, D. S., Kollmann, D. T., Gibson, P. L., Riehle, T. H., and Seifert, G. J., Distance bounded energy detecting ultra-wideband impulse radio secure protocol. 2014 36th annual international conference of the IEEE engineering in medicine and biology society, 2014. 6619–6622, 2014.

  56. Soufiene, B. O., Bahattab, A. A., Trad, A., and Youssef, H., Lightweight and confidential data aggregation in healthcare wireless sensor networks. Trans. Emerg. Telecommun. Technol. 27(4):576–588, 2016.

    Google Scholar 

  57. Benmansour, T., Ahmed, T., and Moussaoui, S., Performance evaluation of IEEE 802.15.6 MAC in monitoring of a cardiac patient. 2016 IEEE 41st conference on local computer networks workshops (LCN workshops). 241–247, 2016.

  58. Hmood, A. K. et al., On the accuracy of hiding information metrics: Counterfeit protection for education and important certificates. Int. J. Phys. Sci. 5(7):1054–1062, 2010.

    Google Scholar 

  59. Naji, A. W. et al., Novel approach for cover file of hidden data in the unused area two within EXE file using distortion techniques and advance encryption standard. Proc. World Acad. Sci. Eng. Technol. 56(5):498–502, 2010.

    Google Scholar 

  60. Naji, A. W. et al., Novel framework for hidden data in the image page within executable file using computation between advanced encryption standard and distortion techniques. Int. J. Comput. Sci. Inf. Secur. 3(1):1–6, 2009.

    Google Scholar 

  61. Zaidan, A. A. et al., Novel approach for high (secure and rate) data hidden within triplex space for executable file. Sci. Res. Essays 5(15), 1965.

  62. Zaidan, A. A. et al., Novel multi-cover steganography using remote sensing image and general recursion neural cryptosystem. Int. J. Phys. Sci. 5:1776–1786, 2010.

    Google Scholar 

  63. Salem, Y. et al., A review on multimedia communications cryptography. Res. J. Inform. Technol 3:146–152, 2011.

    Google Scholar 

  64. Abomhara, M. et al., Suitability of using symmetric key to secure multimedia data: An overview. J. Appl. Sci. 10(15):1656–1661, 2010.

    Google Scholar 

  65. Lin, Y. H., Jan, I. C., Ko, P. C. I., Chen, Y. Y., Wong, J. M., and Jan, G. J., A wireless PDA-based physiological monitoring system for patient transport. IEEE Trans. Inf. Technol. Biomed. 8(4):439–447, 2004.

    PubMed  Google Scholar 

  66. Farahani, B. et al., Towards fog-driven IoT eHealth : Promises and challenges of IoT in medicine and healthcare. Futur. Gener. Comput. Syst. 78:659–676, 2017.

    Google Scholar 

  67. Czaja, S. J., and Lee, C. C., The impact of aging on access to technology. Univers. Access Inf. Soc. 5(4):341–349, 2007.

    Google Scholar 

  68. F. J. S. Thilo, S. Hahn, R. J. G. Halfens, and J. M. G. A. Schols, “Usability of a wearable fall detection prototype from the perspective of older people - a real field testing approach,” J. Clin. Nurs., 2018.

  69. Medani, A. et al., Review of mobile short message service security issues and techniques towards the solution. Sci. Res. Essays 6(6):1147–1165, 2011.

    Google Scholar 

  70. Al-bakri, S. H. et al., Securing peer-to-peer mobile communications using public key cryptography : New security strategy. Int. J. Phys. Sci. 6(4):930–938, 2011.

    Google Scholar 

  71. Bolle, R. M., Connell, J., Pankanti, S., Ratha, N. K., and Senior, A. W., Guide to biometrics. Springer Science & Business Media, 2013.

  72. Shea, J. J., Handbook of fingerprint recognition [book review], vol. 20, no. 5. Springer Science & Business Media, 2004.

  73. Q. M. Yas et al., A systematic review on smartphone skin Cancer apps: Coherent taxonomy, motivations, open challenges and recommendations, and new research direction. J. Circuits, Syst. Comput., vol. 27, no. 05, p. 1830003, 2018.

    Google Scholar 

  74. Hussain, M. et al., The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Comput. Methods Programs Biomed. 122(3):393–408, 2015.

    PubMed  Google Scholar 

  75. Alsalem, M. A. et al., Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. J. Med. Syst. 42(11):204, 2018.

    CAS  PubMed  Google Scholar 

  76. Zaidan, A. A. et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution. Health Technol. (Berl). 8(4):223–238, 2018.

    Google Scholar 

  77. Alaa, M. et al., A review of smart home applications based on internet of things. J. Netw. Comput. Appl. 97(1):48–65, 2017.

    Google Scholar 

  78. V. P. N. Sugandhi, M. Mathankumar, “Real time authentication system using advanced finger vein recognition technique,” Int. Conf. Commun. Signal Process. April 3–5, 2014, India, pp. 1183–1187, 2014.

  79. J. Chavez-Galaviz, J. Ruiz-Rojas, and A. Garcia-Gonzalez, “embedded biometric cryptosystem based on finger vein patterns,” 2015 12th Int. Conf. Electr. Eng. Comput. Sci. Autom. Control. CCE 2015, pp. 1–6, 2015.

  80. Nandhinipreetha, A., and Radha, N., Multimodal biometric template authentication of finger vein and signature using visual cryptography. 2016 Int. Conf. Comput. Commun. Inform. ICCCI 2016:7–10, 2016.

    Google Scholar 

  81. Murakami, T., Ohki, T., and Takahashi, K., Optimal sequential fusion for multibiometric cryptosystems. Inf. Fusion 32:93–108, 2016.

    Google Scholar 

  82. Wu, Z., Tian, L., Li, P., Wu, T., Jiang, M., and Wu, C., Generating stable biometric keys for flexible cloud computing authentication using finger vein. Inf. Sci. (Ny). 0:1–17, 2016.

    Google Scholar 

  83. D. Jagadiswary and D. Saraswady, “Biometric authentication using fused multimodal biometric,” Procedia - Procedia Comput. Sci., vol. 85, no. Cms, pp. 109–116, 2016.

  84. Jialiang Peng, X. N., Li, Q., and Abd El-Latif, A. A., Finger multibiometric cryptosystems: Fusion strategy and template security. J. Biomed. Opt. 19(2):020901, 2014.

    Google Scholar 

  85. Fayyaz, M., Hajizadeh-Saffar, M., Sabokrou, M., Hoseini, M., and Fathy, M., A novel approach for finger vein verification based on self-taught learning. Iran. Conf. Mach. Vis. Image Process. MVIP 2016:88–91, 2016.

    Google Scholar 

  86. Qin, H., and El-Yacoubi, M. A., Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans. Inf. Forensics Secur. 12(8):1816–1829, 2017.

    Google Scholar 

  87. Zhang, F., Guo, S., and Qian, X., Segmentation for finger vein image based on PDEs denoising. Proc. - 2010 3rd Int. Conf. Biomed. Eng. Informatics, BMEI 2010 2(Bmei):531–535, 2010.

    Google Scholar 

  88. Gupta, P., and Gupta, P., An accurate finger vein based verification system. Digit. Signal Process. A Rev. J. 38:43–52, 2015.

    Google Scholar 

  89. Liu, T., Xie, J., Yan, W., Li, P., and Lu, H., Finger-vein pattern restoration with direction-variance-boundary constraint search. Eng. Appl. Artif. Intell. 46:131–139, 2015.

    Google Scholar 

  90. Liu, Z., Yin, Y., Wang, H., Song, S., and Li, Q., Finger vein recognition with manifold learning. J. Netw. Comput. Appl. 33(3):275–282, 2010.

    Google Scholar 

  91. Da Wu, J., and Liu, C. T., Finger-vein pattern identification using SVM and neural network technique. Expert Syst. Appl. 38(5):5423–5427, 2011.

    Google Scholar 

  92. Qin, H., and El-Yacoubi, M. A., Finger-vein quality assessment by representation learning from binary images. Lect. Notes Comput. Sci. (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9489:421–431, 2015.

    Google Scholar 

  93. Li, Z., Sun, D., Di, L., and Hao, L., Two modality-based bi-finger vein verification system. Int. Conf. Signal Process. Proceedings, ICSP, 1690–1693, 2010.

  94. Parthiban, K., Wahi, A., Sundaramurthy, S., and Palanisamy, C., Finger vein extraction and authentication based on gradient feature selection algorithm. 5th Int. Conf. Appl. Digit. Inf. Web Technol. ICADIWT 2014:143–147, 2014.

    Google Scholar 

  95. Fateme Saadat, M. N., A Multibiometric Finger Vein Verification System Based On Score Level Fusion Strategy. Second Int. Congr. Technol. Commun. Knowl. (ICTCK 2015), 11–12, 2015 - Mashhad Branch, Islam. Azad Univ. Mashhad, Iran, no. Ictck, pp. 11–12, 2015.

  96. Tang, D., Huang, B., Li, R., and Li, W., A person retrieval solution using finger vein patterns. Proc - Int Conf Pattern Recogn. 1306–1309, 2010.

  97. William, A., Ong, T. S., Lau, S. H., and Goh, M. K. O., Finger vein verification using local histogram of hybrid texture descriptors. IEEE 2015 Int. Conf. Signal Image Process. Appl. ICSIPA 2015 - Proc., 304–308, 2016.

  98. Lu, Y., Wu, S., Fang, Z., Xiong, N., Yoon, S., and Park, D. S., Exploring finger vein based personal authentication for secure IoT. Futur. Gener. Comput. Syst. 77:149–160, 2017.

    Google Scholar 

  99. Pflug, A., Hartung, D., and Busch, C., Feature extraction from vein images using spatial information and chain codes. Inf. Secur. Tech. Rep. 17(1–2):26–35, 2012.

    Google Scholar 

  100. Xi, X., Yang, L., and Yin, Y., Learning discriminative binary codes for finger vein recognition. Pattern Recognit. 66:26–33, 2017.

    Google Scholar 

  101. Yang, J., Shi, Y., and Yang, J., Personal identification based on finger-vein features. Comput. Human Behav. 27(5):1565–1570, 2011.

    Google Scholar 

  102. Dong, L., Yang, G., Yin, Y., Xi, X., Yang, L., and Liu, F., Finger vein verification with vein Textons. Int. J. Pattern Recognit. Artif. Intell. 29(04):1556003, 2015.

    Google Scholar 

  103. Song, W., Kim, T., Kim, H. C., Choi, J. H., Kong, H. J., and Lee, S. R., A finger-vein verification system using mean curvature. Pattern Recognit. Lett. 32(11):1541–1547, 2011.

    Google Scholar 

  104. Wu, J.-D., Liu, C.-T., Tsai, Y.-J., Liu, J.-C., and Chang, Y.-W., Development of neural network techniques for finger-vein pattern classification. Second Int. Conf. Digit. IMAGE Process. 7546:75460F, 2010.

    Google Scholar 

  105. Huang, H., Liu, S., Zheng, H., Ni, L., Zhang, Y., and Li, W., DeepVein: Novel finger vein verification methods based on deep convolutional neural networks. 2017 IEEE Int. Conf. Identity, Secur. Behav. Anal. ISBA 2017(5), 2017.

  106. Ong, T. S., Teng, J. H., Muthu, K. S., and Teoh, A. B. J., Multi-instance finger vein recognition using minutiae matching. Proc. 2013 6th Int. Congr. Image Signal Process. CISP 2013 3(Cisp):1730–1735, 2013.

    Google Scholar 

  107. J. Peng, N. Wang, A. a. A. El-Latif, Q. Li, and X. Niu, “Finger-vein verification using Gabor filter and SIFT feature matching. 2012 Eighth Int. Conf. Intell. Inf. Hiding Multimed. Signal Process., pp. 45–48, 2012.

  108. Tang, D., Huang, B., Li, R., Li, W., and Li, X., Finger vein verification using occurrence probability matrix (OPM), Proc. Int. Jt. Conf. Neural Netw. 21–26, 2012.

  109. Liu, F., Yang, G., Yin, Y., and Wang, S., Singular value decomposition based minutiae matching method for finger vein recognition. Neurocomputing 145:75–89, 2014.

    Google Scholar 

  110. Wang, J., Xiao, J., Lin, W., and Luo, C., Discriminative and generative vocabulary tree: With application to vein image authentication and recognition. Image Vis. Comput. 34:51–62, 2015.

    CAS  Google Scholar 

  111. Noori Hoshyar, A., and Sulaiman, R., Vein matching using artificial neural network in vein authentication systems. Conf. Graph. Image Process. (ICGIP 2011) 8285(Icgip):82850Z, 2011.

    Google Scholar 

  112. Peng, J., El-Latif, A. A. A., Li, Q., and Niu, X., Multimodal biometric authentication based on score level fusion of finger biometrics. Optik (Stuttg). 125(23):6891–6897, 2014.

    Google Scholar 

  113. Razzak, M. I., and Yusof, R., Multimodal face and finger veins biometric authentication. Sci. Res. Essays 5(17):2529–2534, 2010.

    Google Scholar 

  114. Cheng, Y., Chen, H., and Cheng, B., Special point representations for reducing data space requirements of finger-vein recognition applications. Multimed. Tools Appl. 76(278), 2016.

    Google Scholar 

  115. P. C. M. Khalil-Hani Eng, FPGA-based embedded system implementation of finger vein Biometrics. 2010 IEEE Symp. Ind. Electron. Appl. (ISIEA 2010), Oct. 3–5, 2010, Penang, Malaysia FPGA-Based, no. Isiea, pp. 700–705, 2010.

  116. Khalil-Hani, M. and Eng, P. C., Personal verification using finger vein biometrics in FPGA-based system-on-Chip. 2011 7th Int. Conf. Electr. Electron. Eng., p. II-171-II-176, 2011.

  117. Khalil-Hani, M. and Lee, Y. H., FPGA embedded hardware system for finger vein biometric recognition. IECON Proc. (Industrial Electron. Conf., 2273–2278, 2013.

  118. Raghavendra, R., Raja, K. B., Surbiryala, J., and Busch, C., A low-cost multimodal biometric sensor to capture finger vein and fingerprint. IJCB 2014 - 2014 IEEE/IAPR Int. Jt. Conf. Biometrics, 2014.

  119. Raghavendra, R., Surbiryala, J., Raja, K. B., and Busch, C., Novel finger vascular pattern imaging device for robust biometric verification. IST 2014–2014 IEEE Int. Conf. Imaging Syst. Tech. Proc., 148–152, 2014.

  120. Xin, Y., Liu, Z., Zhang, H., and Zhang, H., Finger vein verification system based on sparse representation. Appl. Opt. 51(25):6252–6258, 2012.

    PubMed  Google Scholar 

  121. Yang, W., Huang, X., Zhou, F., and Liao, Q., Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion q. Inf. Sci. (Ny). 268:20–32, 2014.

    Google Scholar 

  122. Jadhav, M., and Nerkar, P. M., Implementation of an embedded hardware of FVRS on FPGA. Proc. - IEEE Int. Conf. Inf. Process. ICIP 2015:48–53, 2016.

    Google Scholar 

  123. Hartung, D., Martin, S., and Busch, C., Quality estimation for vascular pattern recognition. 2011 Int. Conf. Hand-based biometrics, ICHB 2011 - Proc. 258–263, 2011.

  124. Qin, H., and El Yacoubi, M. A., Deep representation for finger-vein image quality assessment. IEEE Trans. Circuits Syst. Video Technol. 8215(c):1, 2017.

    Google Scholar 

  125. Tang, D., Huang, B., Li, W., and Li, X., A method of evolving finger vein template. Proc. - 2012 Int. Symp. Biometrics Secur. Technol. ISBAST 2012:96–101, 2012.

    Google Scholar 

  126. He, M. et al., Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recogn. 43(5):1789–1800, 2010.

    Google Scholar 

  127. Horng, S. J., Chen, Y. H., Run, R. S., Chen, R. J., Lai, J. L., and Sentosal, K. O., An improved score level fusion in multimodal biometric systems. Parallel Distrib. Comput. Appl. Technol. PDCAT Proc. 239–246, 2009.

  128. Damavandinejadmonfared, S., Mobarakeh, A. K., Suandi, S. A., and Rosdi, B. A., Evaluate and determine the most appropriate method to identify finger vein. Procedia Eng. 41(Iris):516–521, 2012.

    Google Scholar 

  129. Raghavendra, R., Raja, K. B., Surbiryala, J., and Busch, C., Finger vascular pattern imaging - a comprehensive evaluation. 2014 Asia-Pacific signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA 2014, 2014.

  130. Zheng, H., Ni, L., Xian, R., Liu, S., and Li, W., BMDT: An optimized method for biometric menagerie detection. 2015 IEEE 7th Int. Conf. Biometrics Theory, Appl. Syst. BTAS 2015, 2015.

  131. Ye, Y., Zheng, H., Ni, L., Liu, S., and Li, W., A study on the individuality of finger vein based on statistical analysis. 2016 Int. Conf. Biometrics ICB 2016:1–5, 2016.

    Google Scholar 

  132. Yang, L., Yang, G., Yin, Y., and Xi, X., Exploring soft biometric trait with finger vein recognition. Neurocomputing 135:218–228, 2014.

    CAS  Google Scholar 

  133. Waluś, M., Bernacki, K., and Konopacki, J., Impact of NIR wavelength lighting in image acquisition on finger vein biometric system effectiveness. Opto-Electronics Rev. 25(4):263–268, 2017.

    Google Scholar 

  134. Ibrahim, M. M. S., Al-namiy, F. S., Beno, M., and Rajaji, L., Biometric authentication for secured transaction using finger vein technology. Seiscon. 760–763, 2011.

  135. Goudelis, G., Tefas, A., and Pitas, I., Emerging biometric modalities: A survey. J. Multimodal User Interfaces 2(3):217–235, 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

    A. H. Mohsin, A. A. Zaidan, B. B. Zaidan, A. S. Albahri, O. S. Albahri, M. A. Alsalem & K. I. Mohammed

Authors
  1. A. H. Mohsin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. A. A. Zaidan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. B. B. Zaidan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. A. S. Albahri
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. O. S. Albahri
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. M. A. Alsalem
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. K. I. Mohammed
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to A. A. Zaidan.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its subsequent amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohsin, A.H., Zaidan, A.A., Zaidan, B.B. et al. Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review. J Med Syst 42, 238 (2018). https://doi.org/10.1007/s10916-018-1104-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1104-5

Keywords