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A Novel IOT-Based Medicine Consumption System for Elders

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Abstract

This paper introduces an IoT based medicine consumption which enables the elderly person to take the right medicine at the right time. Usually elder people need to take medicine several times daily. And there is a high chance to forget the medication time. Sometimes they also struggle to remember the name of the medicine. Our system tackles this problem and ensures proper monitoring of medicine pill consumption. The system provides convenient hardware and software support to serve the medicine in a user friendly way. It contains an effective image preprocessing pipeline along with Optical Character Recognition (OCR) to detect names from medicine strips. This is also robust in detecting the medicine even if part of the strip is torn. To match the detected text with the prescribed medicine in the database Bi-directional Long Short-Term Memory (BiLSTM) networks are used. The proposed system is lighting invariant as users might consume medicine at different times of the day. Testing was performed in three different conditions—normal, medium and low lighting. The preprocessing technique plays a crucial role to detect the medicine in every lighting condition. The system also has an easy to use interface which is built using modern web technologies to enter or update the medicine consumption time. The proposed system is better than existing solutions, as it contains an array of different features including medicine dispensation, storing records, medicine detection, medicine consumption history, etc.

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References

  1. Population ages 65 and above (% of total population). https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS. Accessed 20 Apr 2022.

  2. Ageing and health. https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS. Accessed 20 Apr 2022.

  3. Shree SRB, Shekar PC, Arjun A, Manoj GR, Nithin A, Raj RS. Automated medication dispensing system. In: 2014 Eleventh International Conference on wireless and optical communications networks (WOCN), 2014; p. 1–6. https://doi.org/10.1109/WOCN.2014.6923079.

  4. Pandey PS, Raghuwanshi SK, Tomar GS. The real time hardware of smart medicine dispenser to reduce the adverse drugs reactions. In: 2018 International Conference on advances in computing and communication engineering (ICACCE), 2018; p. 413–18. https://doi.org/10.1109/ICACCE.2018.8441709.

  5. Ahadani MA, De Silva LC, Petra I, Hameed MFA, Wong TS. Low cost robotic medicine dispenser. Proc Eng. 2012;41:202–9. https://doi.org/10.1016/j.proeng.2012.07.163 (International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)).

    Article  Google Scholar 

  6. Antoun W, Abdo A, Al-Yaman S, Kassem A, Hamad M, El-Moucary C. Smart medicine dispenser (smd). In: 2018 IEEE 4th Middle East Conference on biomedical engineering (MECBME), 2018; p. 20–3. https://doi.org/10.1109/MECBME.2018.8402399.

  7. Madankar A, Agrawal A, Yede V. Iot based advance pill reminder system for distinct patients. In: 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2021; p. 161–65. https://doi.org/10.1109/I-SMAC52330.2021.9640866.

  8. Umamaheswari S, Rahul Babu MV, Dharun Prasanth D, Nandha Bharathi H, Alagumeenaakshi M. Smart medical dispenser with pulse meter. In: 2021 International Conference on advancements in electrical, electronics, communication, computing and automation (ICAECA), 2021; p. 1–5. https://doi.org/10.1109/ICAECA52838.2021.9675762.

  9. Unsharp Filter. https://homepages.inf.ed.ac.uk/rbf/HIPR2/unsharp.htm/. Accessed 20 Apr 2022.

  10. Zhou X, Yao C, Wen H, Wang Y, Zhou S, He W, Liang J. East: an efficient and accurate scene text detector. In: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5551–5560. https://doi.org/10.1109/CVPR.2017.283.

  11. EasyOCR. https://github.com/JaidedAI/EasyOCR. Accessed 20 Apr 2022.

  12. Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 2019;31(7):1235–70. https://doi.org/10.1162/neco_a_01199.

    Article  MathSciNet  MATH  Google Scholar 

  13. Solidworks. https://www.solidworks.com/. Accessed 20 Apr 2022.

  14. Bulk SMS BD. http://login.bulksmsbd.com/apidocs.php. Accessed 20 Apr 2022.

  15. Django. https://www.djangoproject.com/. Accessed 20 Apr 2022.

  16. SQL. https://www.w3schools.com/sql/. Accessed 20 Apr 2022.

  17. React. https://reactjs.org/. Accessed 20 Apr 2022.

  18. Smith, R.: An overview of the tesseract ocr engine. In: Ninth International Conference on document analysis and recognition (ICDAR 2007), 2007;2:629–633. https://doi.org/10.1109/ICDAR.2007.4376991.

  19. Solidworks. https://www.tensorflow.org/. Accessed 20 Apr 2022.

  20. Nwankpa C, Ijomah W, Gachagan A, Marshall S. Activation functions: comparison of trends in practice and research for deep learning, 2018, arXiv:1811.03378.

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Funding

This project got the “Special Grant in Research”, funded by the ICT Division, Government of the People’s Republic of Bangladesh in the fiscal year 2020-2021 (G. O. No.—179). We gratefully thank ICT Division for this grant.

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Author notes

  1. Raiyaan Abdullah, Riad Ahmed and Lafifa Jamal have contributed equally to this work.

Authors and Affiliations

  1. Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh

    Raiyaan Abdullah, Riad Ahmed & Lafifa Jamal

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  1. Raiyaan Abdullah
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  2. Riad Ahmed
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  3. Lafifa Jamal
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Correspondence to Lafifa Jamal.

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Abdullah, R., Ahmed, R. & Jamal, L. A Novel IOT-Based Medicine Consumption System for Elders. SN COMPUT. SCI. 3, 471 (2022). https://doi.org/10.1007/s42979-022-01367-8

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