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  1. AU="Bhavsar, Karan"
  2. AU="Machida, Haruhiko"
  3. AU="Rahaman, Md. Mizanur"
  4. AU="Henry, Carol"
  5. AU="St George-Hyslop, P"
  6. AU="Pham, Ngo Nghia"
  7. AU="Eeg-Olofsson, Karin Edebol"
  8. AU="Hosokawa, Yuri"
  9. AU="Christophi, Christopher"
  10. AU="Ren, Zhiyun"
  11. AU="Sabari, Benjamin R"
  12. AU="Sellal, Nabila"
  13. AU="Kamei, Yoshiki"
  14. AU="Htun Nyunt, Oo"
  15. AU="Lalonde, Donald H"
  16. AU=Olliaro Piero L AU=Olliaro Piero L
  17. AU="Fortney, J J"

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  1. Artikel ; Online: An Efficient Service-based System for Hierarchical Human Activity Sensing.

    Pawar, Bhaskar / Bhattacharya, Sakyajit / Sharma, Varsha / Bhavsar, Karan / Ghose, Avik

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Band 2023, Seite(n) 1–4

    Abstract: In this paper, we propose an end-to-end system, based on SEnsing as Service (SEAS) model, which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. Thus, reducing the cost ... ...

    Abstract In this paper, we propose an end-to-end system, based on SEnsing as Service (SEAS) model, which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. Thus, reducing the cost of data transfer and CPU usage. We also propose a classification algorithm as a part of the system to recognize Activities of Daily Living (ADL). The results indicate that our proposed system recognizes ADLs with considerable accuracy and flexibility.Clinical relevance- Measurement of Activities of Daily Living has a high correlation with independent living measures for elderly people [1] and post-event rehabilitation where an event may be heart-attack [2], stroke [3], surgical intervention [4], or trauma [5] etc.
    Mesh-Begriff(e) Humans ; Aged ; Activities of Daily Living ; Stroke ; Stroke Rehabilitation ; Independent Living
    Sprache Englisch
    Erscheinungsdatum 2023-12-11
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10340230
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Detection of Counterfeit Medicines Using Hyperspectral Sensing.

    Shinde, Sujit R / Bhavsar, Karan / Kimbahune, Sanjay / Khandelwal, Sundeep / Ghose, Avik / Pal, Arpan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2020  Band 2020, Seite(n) 6155–6158

    Abstract: Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated ... ...

    Abstract Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated lab and relevant methods. However, such methods require time, sample preparation and technical expertise with lab setup. These methods are not feasible and scalable to be used in the field by the general public. The objective of our research work was to detect counterfeit medicines using simpler and faster method using hyperspectral sensing. In this experiment, a visible - near infrared (350nm - 1050nm) hyperspectral device was used to capture spectral signature of the medicines. We used 24 medicine tablets of different companies. To imitate counterfeit medicines, tablet powders were adulterated by adding different levels of calcium carbonate. Spectral signatures were captured from original stage to all stages of adulterations and analyzed using machine learning (multilayer perceptron classifier). Result shows that we are able to achieve more than 90% classification accuracy. Portable hyperspectral sensing combined with medicines spectral database can be a good field level test method for detection of counterfeit medicines, as it is very fast, easy to use and does not require technical expertise.
    Mesh-Begriff(e) Counterfeit Drugs ; Drug Contamination ; Powders ; Tablets
    Chemische Substanzen Counterfeit Drugs ; Powders ; Tablets
    Sprache Englisch
    Erscheinungsdatum 2020-08-14
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC44109.2020.9176419
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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