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Deteksi nyeri objektif merupakan tantangan dalam dunia medis, terutama bagi pasien yang tidak mampu mengungkapkan rasa sakit secara verbal. Dengan kemajuan teknologi sensor dan kecerdasan buatan, sistem otomatis untuk mendeteksi nyeri berbasis sinyal fisiologis dan ekspresi wajah mulai dikembangkan. Studi ini bertujuan mengidentifikasi tren, metode, dan kualitas metodologis dari penelitian yang menggunakan database publik seperti BioVid Heat Pain, UNBC-McMaster, dan SenseEmotion dalam pengembangan sistem deteksi nyeri berbasis sensor. Penelitian dilakukan dengan pendekatan Systematic Literature Review (SLR) berdasarkan protokol PRISMA 2020 melalui pencarian artikel di Google Scholar dalam rentang tahun 2015–2024. Setelah seleksi berdasarkan kriteria inklusi dan eksklusi, 26 studi dimasukkan ke dalam sintesis naratif. Data dianalisis berdasarkan jenis sensor, metode algoritma, akurasi, dan ukuran sampel, serta dievaluasi menggunakan pendekatan GRADE. Hasil menunjukkan bahwa BioVid dan UNBC-McMaster adalah database paling sering digunakan, dengan sensor EDA, EMG, dan ekspresi wajah sebagai modalitas dominan. Metode klasifikasi umum mencakup CNN, SVM, dan Random Forest. Studi menyimpulkan bahwa pendekatan multimodal dan deep learning meningkatkan akurasi deteksi nyeri, namun validasi klinis dan perhatian terhadap keragaman demografis masih dibutuhkan.

Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigations Remote sensing data is of such nature and volume as to require it to be compatible with processing and outputing by computers. They are the easiest, fastest, and most efficient way to produce images, extract data sets, and assist in decision making. One special function is to assist in manipulating other kinds of data about the spatial or locational aspects of areas in the world that are the subjects of interpretation and decision making. The bulk of the data in such systems have in common a geographical significance, that is, they are tied to definite locations on the Earth. In this sense, they are similar to or actually make up what has become a powerful tool in decision making and management. The Image-Based Information System (IBIS) was developed in 1975 at the Jet Propulsion Laboratory, and is designed to be a comprehensive geographic information system that performs operations on raster image, tabular, and graphics format data, using the Video Image Communication And Retrieval (VICAR) image processing system. This was accomplished by the creation of a new VICAR-based file format for tabulating raster format geographic information over multiple data planes.