Literature Survey
According to Yusuf Ilu et al. (2014) [1], various age-based
criteria, including cognition, literacy, feedback, and focus,
were considered in the literature at the time. These elements
have been discovered to be essential to creating
an application.
Rizwana Shaikh proposed and explored cloud computing aspects in order to promote transparency and security using existing technologies [3]. The cloud storage of data for patient health record management systems is the focus of most discussions. Compared to test results and other treatment histories, personal information is more delicate and needs comprehensive protection in the healthcare sector[14].
It is possible to avoid prescribing the wrong medication for common diseases including fever, cough, cold, and physical discomfort by developing virtual applications for voice-based pharmaceutical prescription[11]. The many kinds of neural networks used for voice recognition were explored by Mr. Hardik Dhudrejia et al. in 2010. Image processing is a crucial aspect of our work[4].
[1] S. Yusuf Ilu, M. Begum Mustafa, S. Salwah Salim, M. Malekzadeh, and M. B. Mustafa, “Age-based factors in the interface design of CAPT systems for children Non-Functional Requirements Prioritization View project Age-based factors in the interface design of CAPT systems for children,” no. September, 2014, [Online]. Available: https://www.researchgate.net/publication/272171763 [2] N. Hnoohom, S. Yuenyong, and P. Chotivatunyu, “MEDiDEN: Automatic Medicine Identification Using a Deep Convolutional Neural Network,” 2018 Int. Jt. Symp. Artif. Intell. Nat. Lang. Process. iSAI-NLP 2018 - Proc., pp. 0–4, 2018, doi: 10.1109/iSAINLP.2018.8692824. [3] R. Shaikh, “Blockchain Based Cloud Storage of Patients Health Records,” 2022 IEEE Delhi Sect. Conf. DELCON 2022, pp. 7–11, 2022, doi: 10.1109/DELCON54057.2022.9753574 [4] S. Deshmukh, P. Rede, S. Sharma, and S. Iyer, “Voice-Enabled Vision for the Visually Disabled,” 2021 7th IEEE Int. Conf. Adv. Comput. Commun. Control. ICAC3 2021, 2021, doi: 10.1109/ICAC353642.2021.9697125.
Research Gap
Many Researches
Numerous studies have been conducted on face recognition, image recognition, and voice recognition for the identification of medicines. They employ a wide range of technologies. Research on blockchain-based security is also prevalent
Number of mobile applications
Currently, there are numerous mobile applications available, like Doc990 and e-channeling. However, patients are not using them due to several use-related issues, and they are also not well-informed about the use and the benefits of using them
Results / Findings
In several studies, all of the results were obtained within certain constraints. Most of them achieve an accuracy level of 70% to 80% in image, facial, and voice recognition. Due to their limited datasets and insufficient available dataset, they cannot produce an accurate, conclusive outcome
Features for health application
These image, face, speech, and secure database recognition features cannot all be found within one smartphone application for health sector. These technologies and tools are used in various studies, however, the capabilities of current applications are limited
Research Problems
Unclear of medicines and handwritten prescriptions
In Sri Lanka, prescriptions written by doctors are typically used. Patients, however, are unable to correctly identify the medications due to the many and distinctive handwriting styles. It is also challenging to identify drugs because of different medical abbreviations
User details are not secure
Concerns regarding the application's security are prevalent among the general public. People are hesitant to enter their personal information since they are unsure because of the fact that applications ask for personal information
Interfaces are not user friendly with elder peoples
Mobile applications are not very familiar to the elderly. Always seeking simplicity and ease in all tasks, they do. However, all of the current programs have complicated user interfaces that an elderly person would find challenging to understand
Lack of knowledge in medicines
People are not well informed about all the different sorts of medications that are accessible at pharmacies or medications that doctors prescribe. Patients encounter numerous problems as a result of a lack of medical education. Many people experience various diseases, and some people even pass away
Main Objectives
mHealth has developed a variety of objectives to fully address the solution in order to address the various issues that patients may have when using a mobile health application. In this mobile application, there are several primary goals and secondary goals. All of these efforts are part of the system's effort to reduce the difficulties the patients face
Face Recognition
Age groups are idenitfied by the face recognition and redirect patiets into relavant interface
Sub Objectives
Flexible Interface
Patients can identify the prescribed medicines through image capturing and get relavant medicine details
Voice Recognition
Patients can use voice system to identify the medicines and get the medicine details
Pharmacy Details
Pharmacy details are saved in the system by phamacists. So patueints can get to know about the pharmacies
Report Management
Patinets reports are managed by the doctors and all the details saved in the database
Methodology
Pricing
Free
Rs0 / month
- Handwritten prescription identification
- Medicine box identification
- Get medicine details
- Blockchain based secure
- Voice recognition for medicine identification
- Divide the age groups of users by face recognition
- Flexible user interfaces for elders
Premium
Rs150 / month
- Handwritten prescription identification
- Medicine box identification
- Get medicine details
- Blockchain based secure
- Voice recognition for medicine identification
- Divide the age groups of users by face recognition
- Flexible user interfaces for elders
Our Technologies
research Project Milestones
-
Project Proposal
A Project Proposal is presented to potential sponsors or clients to receive funding or get your project approved
Mark Allocation : 6
-
Progress Presentation-1
Progress Presentation I reviews the 50% completetion status of the project. This reveals any gaps or inconsistencies in the design/requirements
Mark Allocation : 15
-
Progress Presentation-2
Progress Presentation II reviews the 90% completetion status demonstration of the project. Along with a Poster presesntation which describes the project as a whole
Mark Allocation : 18
-
Demo (Camera-ready Poster)
Poster clearly display all the details of the resaerch paper in aatrrative manner
Mark Allocation : 8
-
Final Assessment & Viva
Final Report evalutes the completed project done throughout the year. Marks mentioned below includes marks for Individual & group reports and also Final report
Mark Allocation : 20
Documents
Presentations (Slides)
OUR TEAM
Mr. Thusithanjana Thilakarthna
LECTURER FACULTY OF COMPUTING | COMPUTER SCIENCE & SOFTWARE ENGINEERING
Mr. Didula Chamara
LECTURER FACULTY OF COMPUTING | COMPUTER SCIENCE & SOFTWARE ENGINEERING
Dimusha Perera
Group Leader Software Engineering
Navodya Jayasinghe
Group Member Software Engineering
Tanya Gangegedara
Group Member Software Engineering
Anuradha Bandara
Group Member Software EngineeringContact
Our Address
SLIIT Malabe Campus, New Kandy Rd, Malabe
Email Us
info@mhealth.com
contact@mhealth.com
Call Us
+71 789 0525
+77 452 3234