Skip to main content

Advertisement

SpringerLink
Log in

Study of Energy-Efficient Biomedical Data Compression Methods in the Wireless Body Area Networks (WBANs) and Remote Healthcare Networks

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Wireless Body Area Network (WBAN) is a wireless network of short-range communication protocols for remote healthcare monitoring with the possibility of giving freedom of human body movements. Sensor nodes (motes) are usually located under the skin, implanted deep in the body, or ingested, as in the rare case of smart pills for medical and non-medical usage. Since the necessary connections of wearable and implantable devices are wireless, and the components use low batteries, processing and transferring the critical data can deplete the nodes’ power. In most cases, it is impossible to exchange or re-power batteries. Holter monitoring, loop recorders, and wireless capsule endoscopy are some of the WBAN’s applications for saving and transferring medical data. Wireless capsule endoscopy is the application for image transmission in WBANs, and the image compression in common is a specific segment of capsule endoscopy. Since the better compression of images increases the frame rate and typically improves the diagnosis process, selecting the compression algorithm should be relevant. The considerable scope of this comprehensive study pays attention to the various biomedical data compression approaches in WBANs. This paper focuses on power-efficient schemes of remote healthcare networks. In this survey article, the energy-based biomedical data compression approaches of WBANs and remote healthcare networks are accurately classified based on lossy, lossless, and hybrid techniques; later, a comprehensive comparison of each specific method’s power consumption is presented.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. M. Masdari and S. Ahmadzadeh, A survey and taxonomy of the authentication schemes in Telecare Medicine Information Systems, Journal of Network and Computer Applications, Vol. 87, pp. 1–19, 2017.

    Article  Google Scholar 

  2. C. Zhao, Z. Wang, H. Li, X. Wu, S. Qiao and J. Sun, A new approach for medical image enhancement based on luminance-level modulation and gradient modulation, Biomedical Signal Processing and Control, Vol. 48, pp. 189–196, 2019.

    Article  Google Scholar 

  3. A. Dumka and A. Sah, Smart ambulance system using concept of big data and internet of things, In: Healthcare Data Analytics and Management, Elsevier, pp. 155-176, 2019.

  4. M. R. Reddy, M. A. Deepika, D. Anusha, J. Iswariya and K. Ravichandran, A new approach for image compression using efficient coding Technique and BPN for medical images, in: International Conference on ISMAC in Computational Vision and Bio-Engineering. pp. 283–290, Springer, 2018.

    Google Scholar 

  5. K. E. Paul and V. S. Saraswathibai, Maximum accurate medical image demosaicing using WRGB based Newton Gregory interpolation method, Measurement, Vol. 135, pp. 935–942, 2019.

    Article  Google Scholar 

  6. J. Anitha, P. E. Sophia and V. H. C. de Albuquerque, Performance enhanced ripplet transform based compression method for medical images, Measurement, Vol. 144, pp. 203–213, 2019.

    Article  Google Scholar 

  7. P. Chitra and M. M. S. Rani, Differential Coding-Based Medical Image Compression, In: Computer Aided Intervention and Diagnostics in Clinical and Medical Images, Springer, pp. 11–19, 2019.

  8. V. Forutan and C.-F. Chiasserini, “Study of techniques for reliable data transmission in wireless sensor networks,” 2020.

  9. A. A. Abdellatif, A. Mohamed, and C.-F. Chiasserini, Device and method for compressing a data stream, ed: Google Patents, 2019.

  10. M. Elkholy, M. M. Hosny, and H. M. F. El-Habrouk, Studying the effect of lossy compression and image fusion on image classification, Alexandria Engineering Journal, Vol. 58, pp. 143–149, 2019.

  11. D. Hutchison and J. P. Sterbenz, Architecture and design for resilient networked systems, Computer Communications, Vol. 131, pp. 13–21, 2018.

    Article  Google Scholar 

  12. D. Nakashima, et al., Finite element analysis of compression fractures at the thoracolumbar junction using models constructed from medical images, Experimental and Therapeutic Medicine, Vol. 15, No. 4, pp. 3225–3230, 2018.

    Google Scholar 

  13. K. Sheeba and M. A. Rahiman, Gradient based fractal image compression using Cayley table, Measurement, Vol. 140, pp. 126–132, 2019.

    Article  Google Scholar 

  14. S. J. Devaraj, Emerging Paradigms in Transform-Based Medical Image Compression for Telemedicine Environment, In: Telemedicine Technologies, Elsevier, pp. 15-29, 2019.

  15. E. Ahn, A. Kumar, M. Fulham, D. Feng, and J. Kim, Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis, Medical image analysis, 2019.

  16. J. Calhoun, F. Cappello, L. N. Olson, M. Snir and W. D. Gropp, Exploring the feasibility of lossy compression for PDE simulations, The International Journal of High Performance Computing Applications, Vol. 33, No. 2, pp. 397–410, 2019.

    Article  Google Scholar 

  17. J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, An energy efficient IoT data compression approach for edge machine learning, Future Generation Computer Systems, 2019.

  18. S. Arunkumar, V. Subramaniyaswamy, V. Vijayakumar, N. Chilamkurti and R. Logesh, SVD-based robust image steganographic scheme using RIWT and DCT for secure transmission of medical images, Measurement, Vol. 139, pp. 426–437, 2019.

    Article  Google Scholar 

  19. P. N. T. Ammah and E. Owusu, Robust medical image compression based on wavelet transform and vector quantization, Informatics in Medicine Unlocked, Vol. 15, pp. 100183, 2019.

    Article  Google Scholar 

  20. D. Tao, S. Di, H. Guo, Z. Chen and F. Cappello, Z-checker: A framework for assessing lossy compression of scientific data, The International Journal of High Performance Computing Applications, Vol. 33, No. 2, pp. 285–303, 2019.

    Article  Google Scholar 

  21. E. A. Zanaty et al., Medical Image Compression Based on Combining Region Growing and Wavelet Transform, Algorithms, Vol. 11, p. 12, 2019.

  22. L. N. J. Selvi, medical image compression using DEFLATE algorithm, Science, Vol. 3, No. 4–1, pp. 17–20, 2015.

    Google Scholar 

  23. D. H. Foos et al., JPEG 2000 compression of medical imagery, in Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues, 2000, Vol. 3980: International Society for Optics and Photonics, pp. 85-97.

  24. H. Wu and A. A. Abouzeid, Energy efficient distributed JPEG2000 image compression in multihop wireless networks, in Proc. of IEEE Workshop on Applications and Services in Wireless Networks, pp. 152-160, 2004.

  25. M. K. K. Niazi, et al., Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer, Artificial intelligence in medicine, Vol. 95, pp. 82–87, 2019.

    Article  Google Scholar 

  26. S.-C. Liew, S.-W. Liew and J. M. Zain, Reversible medical image watermarking for tamper detection and recovery with run length encoding compression, World Academy of Science, Engineering and Technology, Vol. 72, pp. 799–803, 2010.

    Google Scholar 

  27. F. P. Permana, Medical image watermarking with tamper detection and recovery using reversible watermarking with LSB modification and run length encoding (RLE) compression, in 2012 IEEE International Conference on Communication, Networks and Satellite (ComNetSat), IEEE, pp. 167-171, 2012.

  28. S. Boucherkha and M. Benmohamed, A Lossless Watermarking Based Authentication System For Medical Images, in International Conference on Computational Intelligence, 2004, pp. 240-243.

  29. H. Daou and F. Labeau, Pre-processing of multi-channel EEG for improved compression performance using SPIHT, in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 2232-2235, 2012.

  30. J.-H. Hsieh, K.-C. Hung, Y.-L. Lin and M.-J. Shih, A speed-and power-efficient SPIHT design for wearable quality-on-demand ECG applications, IEEE journal of biomedical and health informatics, Vol. 22, No. 5, pp. 1456–1465, 2018.

    Article  Google Scholar 

  31. Y.-Y. Chen, Medical image compression using DCT-based subband decomposition and modified SPIHT data organization, International journal of medical informatics, Vol. 76, No. 10, pp. 717–725, 2007.

    Article  Google Scholar 

  32. C. Chakraborty, Performance analysis of compression techniques for chronic wound image transmission under smartphone-enabled tele-wound network, International Journal of E-Health and Medical Communications (IJEHMC), Vol. 10, No. 2, pp. 1–20, 2019.

    Google Scholar 

  33. D.-G. Zhang, X. Wang and X.-D. Song, New medical image fusion approach with coding based on SCD in wireless sensor network, Journal of Electrical Engineering & Technology, Vol. 10, No. 6, pp. 2384–2392, 2015.

    Article  Google Scholar 

  34. Z. Wang and Y. Chen, ECG signal compression based on MSPIHT algorithm, in 2008 International Conference on Information Technology and Applications in Biomedicine, IEEE, pp. 415-419, 2008.

  35. C. J. Deepu, X. Zhang, W.-S. Liew, D. L. T. Wong and Y. Lian, An ECG-on-chip with 535 nW/channel integrated lossless data compressor for wireless sensors, IEEE Journal of Solid-State Circuits, Vol. 49, No. 11, pp. 2435–2448, 2014.

    Article  Google Scholar 

  36. C. J. Deepu and Y. Lian, A low complexity lossless compression scheme for wearable ECG sensors, in 2015 IEEE International Conference on Digital Signal Processing (DSP), IEEE, pp. 449-453, 2015.

  37. S.-R. Siao, C.-C. Hsu, M. P.-H. Lin, and S.-Y. Lee, A novel approach for ECG data compression in healthcare monitoring system, in 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014), IEEE, pp. 1-4, 2014.

  38. T. Tekeste, H. Saleh, B. Mohammad and M. Ismail, Ultra-low power QRS Detection and ECG compression architecture for IoT healthcare devices, IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 66, No. 2, pp. 669–679, 2019.

    Article  Google Scholar 

  39. N. V. Malathkar and S. K. Soni, A near lossless and low complexity image compression algorithm based on fixed threshold DPCM for capsule endoscopy, Multimedia Tools and Applications, pp. 1-16, 2020.

  40. I. Capurro, F. Lecumberry, A. Martín, I. Ramírez, E. Rovira and G. Seroussi, Efficient sequential compression of multichannel biomedical signals, IEEE journal of biomedical and health informatics, Vol. 21, No. 4, pp. 904–916, 2016.

    Article  Google Scholar 

  41. C. Deepu, X. Zhang, C. Heng, and Y. Lian, An ecg-on-chip with qrs detection lossless compression for low power wireless sensors," sensors, Vol. 2, No. 3, 2016.

  42. S. Kumar, A. L. Fred, H. A. Kumar and P. S. Varghese, Lossless compression of CT images by an improved prediction scheme using least square algorithm, Circuits, Systems, and Signal Processing, Vol. 39, No. 2, pp. 522–542, 2020.

    Article  Google Scholar 

  43. T. Shirsat and V. Bairagi, Lossless medical image compression by integer wavelet and predictive coding, ISRN Biomedical Engineering. Vol. 2013, 2013.

  44. K. C. Pathak and J. N. Sarvaiya, Lossless medical image compression using transform domain adaptive prediction for telemedicine, in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), IEEE, pp. 1026-1031, 2017.

  45. M. U. A. Ayoobkhan, E. Chikkannan and K. Ramakrishnan, Feed-forward neural network-based predictive image coding for medical image compression, Arabian Journal for Science and Engineering, Vol. 43, No. 8, pp. 4239–4247, 2018.

    Article  Google Scholar 

  46. R. Sumalatha and M. Subramanyam, Hierarchical lossless image compression for telemedicine applications, Procedia Computer Science, Vol. 54, pp. 838–848, 2015.

    Article  Google Scholar 

  47. M. Raeiatibanadkooki, S. R. Quchani, M. KhalilZade and K. Bahaadinbeigy, Compression and encryption of ECG signal using wavelet and chaotically Huffman code in telemedicine application, Journal of medical systems, Vol. 40, No. 3, pp. 73, 2016.

    Article  Google Scholar 

  48. C. S. Vincent and J. Jayaraj, N-Pattern Huffman compression algorithm for medical images in telemedicine, JCP, Vol. 11, No. 5, pp. 365–373, 2016.

    Article  Google Scholar 

  49. G.-Y. Cho, S.-J. Lee and T.-R. Lee, An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems, Journal of Medical Systems, Vol. 39, No. 1, pp. 161, 2015.

    Article  Google Scholar 

  50. V. Bairagi and A. Sapkal, Automated region-based hybrid compression for digital imaging and communications in medicine magnetic resonance imaging images for telemedicine applications, IET Science, Measurement & Technology, Vol. 6, No. 4, pp. 247–253, 2012.

    Article  Google Scholar 

  51. T.-H. Tsai and W.-T. Kuo, An efficient ECG lossless compression system for embedded platforms with telemedicine applications, IEEE Access, Vol. 6, pp. 42207–42215, 2018.

    Article  Google Scholar 

  52. A. Rajaeefar, A. Emami, S. Soroushmehr, N. Karimi, S. Samavi, and K. Najarian, Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks, arXiv preprint arXiv:1802.07781, 2018.

  53. S. Yuan and J. Hu, Research on image compression technology based on Huffman coding, Journal of Visual Communication and Image Representation, Vol. 59, pp. 33–38, 2019.

    Article  Google Scholar 

  54. P. Vijay, SEM Medical Image Processing using VLSI, 2019.

  55. C.-T. Ku, K.-C. Hung, T.-C. Wu and H.-S. Wang, Wavelet-based ECG data compression system with linear quality control scheme, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 6, pp. 1399–1409, 2010.

    Article  Google Scholar 

  56. F. Sufi and I. Khalil, Enforcing secured ecg transmission for realtime telemonitoring: A joint encoding, compression, encryption mechanism, Security and Communication Networks, Vol. 1, No. 5, pp. 389–405, 2008.

    Article  Google Scholar 

  57. K.-K. Tseng, X. He, W.-M. Kung, S.-T. Chen, M. Liao and H.-N. Huang, Wavelet-based watermarking and compression for ECG signals with verification evaluation, Sensors, Vol. 14, No. 2, pp. 3721–3736, 2014.

    Article  Google Scholar 

  58. B. Halder, S. Bose, N. Mishra, and S. Mitra, Embedding and retrieving patient's identification and compression of ECG signal, in Students' Technology Symposium (TechSym), 2014 IEEE, pp. 1–6.

  59. W.-J. Hwang, C.-F. Chine and K.-J. Li, Scalable medical data compression and transmission using wavelet transform for telemedicine applications, IEEE transactions on information technology in biomedicine, Vol. 7, No. 1, pp. 54–63, 2003.

    Article  Google Scholar 

  60. G. D. y Alvarez et al., Wireless EEG system achieving high throughput and reduced energy consumption through lossless and near-lossless compression, IEEE transactions on biomedical circuits and systems, Vol. 12, No. 1, Pp. 231–241, 2018.

  61. I. Maglogiannis, C. Doukas, G. Kormentzas and T. Pliakas, Wavelet-based compression with ROI coding support for mobile access to DICOM images over heterogeneous radio networks, IEEE Transactions on Information Technology in Biomedicine, Vol. 13, No. 4, pp. 458–466, 2009.

    Article  Google Scholar 

  62. N. S. Korde and D. A. Gurjar, Wavelet based medical image compression for telemedicine application, American Journal of Engineering Research (AJER), Vol. 3, 2014.

  63. V. K. Bairagi and A. M. Sapkal, ROI-based DICOM image compression for telemedicine, Sadhana, Vol. 38, No. 1, pp. 123–131, 2013.

    Article  Google Scholar 

  64. M. K. Baby, A. Madhu, and R. Aneesh, Biomedical Image Integrity Check for Telemedicine Applications by Hash Embedding & Wavelet Compression, in 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), 2017: IEEE, pp. 178–185.

  65. C. Doukas, I. Maglogiannis, and G. Kormentzas, Medical image compression using wavelet transform on mobile devices with ROI coding support, in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006, IEEE, pp. 3779–3784.

  66. Z. Liu et al., Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds, arXiv preprint arXiv:1904.08487, 2019.

  67. V. Aggarwal and M. S. Patterh, Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP), in 2009 International Multimedia, Signal Processing and Communication Technologies, IEEE, pp. 12–15, 2009.

  68. C. K. Jha and M. H. Kolekar, Electrocardiogram data compression using DCT based discrete orthogonal Stockwell transform, Biomedical Signal Processing and Control, Vol. 46, pp. 174–181, 2018.

    Article  Google Scholar 

  69. A. F. Hussein, S. J. Hashim, A. F. A. Aziz, F. Z. Rokhani, and W. A. W. Adnan, A real time ECG data compression scheme for enhanced bluetooth low energy ECG system power consumption, Journal of Ambient Intelligence and Humanized Computing, pp. 1-14, 2017.

  70. D. Birvinskas, V. Jusas, I. Martisius and R. Damasevicius, Fast DCT algorithms for EEG data compression in embedded systems, Computer Science and Information Systems, Vol. 12, No. 1, pp. 49–62, 2015.

    Article  Google Scholar 

  71. S. Kumar, A. L. Fred, H. A. Kumar, P. S. Varghese, and A. V. Daniel, BAT Optimization-Based Vector Quantization Algorithm for Compression of CT Medical Images, in ICTMI 2017. Springer, pp. 53–64, 2019.

  72. B. Huang, Y. Wang and J. Chen, ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization, Biomedical Signal Processing and Control, Vol. 8, No. 1, pp. 59–65, 2013.

    Article  Google Scholar 

  73. S. Padhy, L. Sharma and S. Dandapat, Multilead ECG data compression using SVD in multiresolution domain, Biomedical signal processing and control, Vol. 23, pp. 10–18, 2016.

    Article  Google Scholar 

  74. K. Nemati and K. Ramakrishnan, Hybrid lossless and lossy compression technique for ECG signals, in 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS), IEEE, pp. 450–455, 2017.

  75. G. Higgins et al., EEG compression using JPEG2000: How much loss is too much?, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, pp. 614–617, 2010.

  76. S. Lee, J. Kim and M. Lee, A real-time ECG data compression and transmission algorithm for an e-health device, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 9, pp. 2448–2455, 2011.

    Article  Google Scholar 

  77. J. Luo, Z. Chen, X. Xiang, and J. Meng, A lifting wavelet based lossless and lossy ECG compression processor for wireless sensors, IEICE Electronics Express, p. 14.20170865, 2017.

  78. C. J. Deepu, C.-H. Heng and Y. Lian, A hybrid data compression scheme for power reduction in wireless sensors for IoT, IEEE Transactions on Biomedical Circuits and Systems, Vol. 11, No. 2, pp. 245–254, 2017.

    Article  Google Scholar 

  79. R. Sheeja and J. Sutha, Soft fuzzy computing to medical image compression in wireless sensor network-based tele medicine system, Multimedia Tools and Applications, pp. 1–18, 2019.

  80. B. Brindha and G. Raghuraman, Region based lossless compression for digital images in telemedicine application, in 2013 International Conference on Communication and Signal Processing, IEEE, pp. 537–540, 2013.

  81. G. Xu, J. Han, Y. Zou and X. Zeng, A 1.5-D multi-channel EEG compression algorithm based on NLSPIHT, IEEE Signal Processing Letters, Vol. 22, No. 8, pp. 1118–1122, 2015.

    Article  Google Scholar 

  82. D. Ravichandran, M. G. Ahamad, and M. A. Dhivakar, Performance analysis of three-dimensional medical image compression based on discrete wavelet transform, in 2016 22nd International Conference on Virtual System & Multimedia (VSMM), 2016 IEEE, pp. 1-8.

  83. J. Uthayakumar, T. Vengattaraman and P. Dhavachelvan, A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks, Ad Hoc Networks, Vol. 83, pp. 149–157, 2019.

    Article  Google Scholar 

  84. M. T. Harting, et al., Telemedicine in pediatric surgery, Journal of Pediatric Surgery, Vol. 54, No. 3, pp. 587–594, 2019.

    Article  MathSciNet  Google Scholar 

  85. S. Franc, Telemedicine and Diabetes, in Handbook of Diabetes Technology. Springer, pp. 95–110, 2019.

  86. P. Movva and P. T. Rao, Novel two-fold data aggregation and MAC scheduling to support energy efficient routing in wireless sensor network, IEEE Access, Vol. 7, pp. 1260–1274, 2019.

    Article  Google Scholar 

  87. S.-W. Lee and H.-Y. Kim, An energy-efficient low-memory image compression system for multimedia IoT products, EURASIP Journal on Image and Video Processing, Vol. 2018, No. 1, pp. 87, 2018.

    Article  Google Scholar 

  88. F. A. Alaba, M. Othman, I. A. T. Hashem and F. Alotaibi, Internet of things security: A survey, Journal of Network and Computer Applications, Vol. 88, pp. 10–28, 2017.

    Article  Google Scholar 

  89. M. Díaz, C. Martín and B. Rubio, State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing, Journal of Network and Computer applications, Vol. 67, pp. 99–117, 2016.

    Article  Google Scholar 

  90. T. Sheltami, M. Musaddiq and E. Shakshuki, Data compression techniques in wireless sensor networks, Future Generation Computer Systems, Vol. 64, pp. 151–162, 2016.

    Article  Google Scholar 

  91. D.-U. Lee, H. Kim, S. Tu, M. Rahimi, D. Estrin, and J. D. Villasenor, Energy-optimized image communication on resource-constrained sensor platforms, in Proceedings of the 6th international conference on Information processing in sensor networks, 2007: ACM, pp. 216-225.

  92. M. Berahim, N. A. Samsudin and S. S. Nathan, A Review: Image Analysis Techniques to Improve Labeling Accuracy of Medical Image Classification, in: International Conference on Soft Computing and Data Mining. pp. 298–307, Springer, 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Urmia Branch, Urmia, Iran

    Safiyyeh Ahmadzadeh

Authors
  1. Safiyyeh Ahmadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Safiyyeh Ahmadzadeh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmadzadeh, S. Study of Energy-Efficient Biomedical Data Compression Methods in the Wireless Body Area Networks (WBANs) and Remote Healthcare Networks. Int J Wireless Inf Networks 30, 252–269 (2023). https://doi.org/10.1007/s10776-023-00599-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-023-00599-6

Keywords

Search

Navigation