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Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning
Manurung B.E.a, Munggaran H.R.a, Ramadhan G.F.a, Koesoema A.P.a
a Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.According to International Diabetes Federation, Indonesia ranked 6th in country with highest number of diabetes patients in the world. Now, the measurement of blood glucose in Indonesia mostly done in invasive manner, which is expensive, painful, and impractical. Moreover, according to Indonesia’s Ministry of Health, there are more diabetes patient in rural areas, who have economic shortages and limited access to health care. So, a cheap and easy to use non-invasive blood glucose measurement system is needed to solve this problem. One of the current trend non-invasive blood glucose is the use of Near-Infrared Spectroscopy (NIR). In this paper, we discuss the development of a NIR based blood glucose monitor. The device’s sensor consists of a pair of LED and photodiode which transmit and receive light with wavelength of 940 nm. The light intensity reading from the sensor will be amplified and filtered to reduce noise, then transmitted to the smart-phone. In the smart-phone application, the reading result will be converted to blood glucose level using a machine learning model embedded in the application. The model used in this final project is a sequential, layer-based neural network model provided by Keras. The model was built and trained on top of TensorFlow, and then converted for mobile use with the help of TensorFlow Lite, The model achieved an acceptable result with Mean Absolute Error (MAE) of 5.855 mg/dL. The result then could be stored in the online database using Cloud Firestore.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Blood glucose level,Blood glucose measurements,Blood glucose monitoring,Blood-glucose monitors,Machine learning models,Mean absolute error,Neural network model,Smart-phone applications[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Blood glucose level,Diabetes,Internet of Things,Machine Learning,NIR Spectroscopy[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/R10-HTC47129.2019.9042479[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]