Literature DB >> 30483400

Automated emergency paramedical response system.

Mashrin Srivastava1, Saumya Suvarna1, Apoorva Srivastava1, S Bharathiraja1.   

Abstract

With the evolution of technology, the fields of medicine and science have also witnessed numerous advancements. In medical emergencies, a few minutes can be the difference between life and death. The obstacles encountered while providing medical assistance can be eliminated by ensuring quicker care and accessible systems. To this effect, the proposed end-to-end system-automated emergency paramedical response system (AEPRS) is semi-autonomous and utilizes aerial distribution by drones, for providing medical supplies on site in cases of paramedical emergencies as well as for patients with a standing history of diseases. Security of confidential medical information is a major area of concern for patients. Confidentiality has been achieved by using decentralised distributed computing to ensure security for the users without involving third-party institutions. AEPRS focuses not only on urban areas but also on semi-urban and rural areas. In urban areas where access to internet is widely available, a healthcare chatbot caters to the individual users and provides a diagnosis based on the symptoms provided by the patients. In semi-urban and rural areas, community hospitals have the option of providing specialised healthcare in spite of the absence of a specialised doctor. Additionally, object recognition and face recognition by using the concept of edge AI enables deep neural networks to run on the edge, without the need for GPU or internet connectivity to connect to the cloud. AEPRS is an airborne emergency medical supply delivery system. It uses the data entered by the user to deduce the best possible solution, in case of an alerted emergency situation and responds to the user accordingly.

Entities:  

Keywords:  AEPRS; Deep learning in healthcare; Drones; Edge AI; GLCM; Healthcare analytics; Healthcare chatbot; Internet of medical things; Neural computing stick; Stroke

Year:  2018        PMID: 30483400      PMCID: PMC6233315          DOI: 10.1007/s13755-018-0061-1

Source DB:  PubMed          Journal:  Health Inf Sci Syst        ISSN: 2047-2501


  7 in total

1.  Computational and performance aspects of PCA-based face-recognition algorithms.

Authors:  H Moon; P J Phillips
Journal:  Perception       Date:  2001       Impact factor: 1.490

2.  The world health report 2002 - reducing risks, promoting healthy life.

Authors:  J J Guilbert
Journal:  Educ Health (Abingdon)       Date:  2003-07

3.  Face recognition using LDA-based algorithms.

Authors:  Juwei Lu; K N Plataniotis; A N Venetsanopoulos
Journal:  IEEE Trans Neural Netw       Date:  2003

4.  Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning.

Authors:  Wei He; Yiting Dong
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2017-02-27       Impact factor: 10.451

5.  Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

Authors:  K Fukushima
Journal:  Biol Cybern       Date:  1980       Impact factor: 2.086

6.  Face recognition by independent component analysis.

Authors:  M S Bartlett; J R Movellan; T J Sejnowski
Journal:  IEEE Trans Neural Netw       Date:  2002

7.  Different approaches for extracting information from the co-occurrence matrix.

Authors:  Loris Nanni; Sheryl Brahnam; Stefano Ghidoni; Emanuele Menegatti; Tonya Barrier
Journal:  PLoS One       Date:  2013-12-26       Impact factor: 3.240

  7 in total
  1 in total

Review 1.  Nanosystems, Edge Computing, and the Next Generation Computing Systems.

Authors:  Ali Passian; Neena Imam
Journal:  Sensors (Basel)       Date:  2019-09-19       Impact factor: 3.576

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.