Smart Automatic Medical Dispenser with Personal Health Care Using IOT

Abstract

The " Smart Automatic Medical Dispenser with Personal Health Care Using IoT” project aims to revolutionize healthcare technology by developing an innovative, user-friendly, precise, and affordable AMD. This project addresses the limitations of existing AMDs by focusing on increased capacity, accuracy, reliability, and affordability. It aims to accommodate a wide range of medication forms, including pills and capsules, empowering individuals to manage their treatment regimens effectively. The envisioned AMD stands out for its intuitive design, precise dispensing, and affordability, aiming to improve medication adherence and ultimately enhance the quality of life for patients.

Specification

Description:FIELD OF INVENTION
[001] Invention in the field of medical device and Internet of things.
BACKGROUND AND PRIOR ART
[002] The management of medication intake poses significant challenges, particularly among the elderly population. Adherence to prescribed medication regimens is crucial for maintaining health and managing chronic conditions effectively. However, factors such as forgetfulness, confusion, and physical limitations often hinder individuals' ability to adhere to medication schedules, leading to missed doses and potential health risks. Recognizing the importance of addressing this issue, our research aims to develop an innovative solution to support medication management among the elderly. The primary objective of our project is to create a medication dispenser system that empowers elderly individuals to take their medications independently and on time.
SUMMARY OF THE INVENTION
[003] In summary, the development of the medicine dispenser system represents a significant achievement in the realm of personalized healthcare technology. Through the integration of various components including a web application, face recognition module, and IoT dispensing module, the system offers a comprehensive solution for streamlining medication administration processes and enhancing patient care. Leveraging technologies such as Java, Python, and hardware components like NodeMCU and ESP32 WiFi camera, the system enables seamless patient identification and precise medication dispensing tailored to individual needs. By providing a user-friendly interface for administrators to manage patient details and medication information, the web application serves as the backbone of the system, facilitating efficient data exchange and synchronization.
[004] Looking ahead, there are several avenues for further research and development to enhance the capabilities and effectiveness of the medicine dispenser system. Exploring advanced machine learning techniques, such as deep learning algorithms, offers promise for improving the system's predictive capabilities and adaptability, enabling more personalized medication recommendations. Additionally, expanding the scope of the system to encompass broader healthcare services beyond medication management presents opportunities for addressing the diverse needs of patients and healthcare providers. Integrating modules for chronic disease management, telemedicine, and remote patient monitoring could further enhance the system's impact on patient care and overall health outcomes.
[005] Furthermore, the implementation of patient feedback mechanisms and continuous monitoring technologies offers avenues for ongoing optimization and refinement of the system. By soliciting feedback from patients and healthcare providers, the system can identify areas for improvement and enhance user satisfaction. Continuous monitoring technologies such as wearable devices and remote sensors enable real-time tracking of patient health metrics and medication adherence, facilitating proactive intervention strategies and ultimately improving patient outcomes. Collaborative research initiatives with academic institutions, research organizations, and industry partners offer opportunities for innovation and knowledge sharing, driving impactful changes in personalized healthcare delivery. In conclusion, the medicine dispenser system has the potential to revolutionize medication administration processes and improve healthcare outcomes for patients globally, with continued research and development efforts playing a pivotal role in realizing this vision.
BRIEF DESCRIPTION OF DRAWINGS
[006] Figure 1 Shows the system architecture of the dispenser, it consists of a power module which provides the power to all the units of the device, the node mc is used to receive signals from the server and control the buzzer display and the servo motors the ESP 32 camera is an independent Wi-Fi module which sends the image to the server for face recognition.
[007] Figure 2 The flowchart illustrates the seamless operation of our medicine dispenser system. It begins with the web application, where administrators input patient details and medication information. This data is stored in a database and utilized by the Face Recognition module. Patient faces are trained and stored as datasets for future recognition. When the server detects the scheduled medication time, it triggers the IoT Dispensing module. This module receives instructions from the server to dispense medication to the identified patient. The process repeats for subsequent patients, ensuring timely and accurate medication administration.
DETAILED DESCRIPTION OF THE INVENTION
[008] The system implementation involves the development of several key modules to effectively communicate between the server and the hardware and effectively utilize the face recognition for proper dispensing of the medicine in the timely manner.
[009] Module for Web Application Implementation: The web application is developed using Java with the Apache Tomcat server for hosting and Navicat SQL Lite database for storing patient and medication details. The implementation involves creating separate interfaces for admin and patient logins. Administrators can add patient details and their corresponding medication information through a user-friendly interface. This data is securely stored in the database for future reference. The application is designed to efficiently handle data retrieval and manipulation, ensuring smooth interaction with other modules.
[010] Module for Face Recognition Implementation: Face Recognition module is implemented using Python and Anaconda Prompt. It consists of two main processes: training and detection. During the training phase, patient face images are captured multiple times and stored as datasets, labeled with the respective patient IDs. These datasets are used to train the recognition model. The detection process involves using the trained model to match detected faces with the stored datasets. This module is crucial for identifying patients and ensuring accurate medication dispensing based on facial recognition.
[011] Module for IoT Dispensing Implementation: The IoT Dispensing module utilizes NodeMCU (WiFi version) for communication with the server and ESP32 WiFi camera for image capture. The module also includes servo motors for dispensing medication, a buzzer for alerting, and a LED 7-segment display for visual feedback. The implementation involves establishing communication with the server to receive instructions for medication dispensing. When triggered, the module activates the servo motors to dispense the prescribed medication. The ESP32 WiFi camera captures images during the process for verification. Once dispensing is complete, the module signals the server for the next patient, ensuring a continuous and efficient workflow.
[012] Each "Module for" plays a distinct role in the medicine dispenser system and is implemented using specific technologies and components tailored to its functionality. Integration of these modules ensures the seamless operation of the system, from patient registration to medication administration, while prioritizing accuracy, efficiency, and patient safety. , C , Claims:Claim 1: A comprehensive user interface designed for both patients and administrators, facilitating easy access to medication information and management tasks.
Claim 2: The first medication dispensing system that incorporates facial recognition technology for enhanced patient security and accurate identification.
Claim 3: No sensitive patient data is stored on the server; all interactions are processed in real-time without retaining any personal information.
Claim 4: An innovative IoT integration that achieves seamless communication between the dispensing unit and the server, ensuring efficient medication delivery with minimal latency.

Documents