Smart Pill Box
Published on Dec 05, 2020
Most of the elders have a couple of chronic illnesses, and they use capsules to stabilize their health status. Doctors urge the circle of relatives contributors have to be extra concerned on medicine protection of the patient. Now a day, most of the patients can also overlook to take their drug treatments as per the prescription due to mental stress. Sometimes, the aged patients are gulping tablets and their dosage stage incorrectly causing a severe hassle. Henceforth, it's far important to the patient to take right drugs at specific quantity and time. Technology has an important role to play on this area potentially, with digital gadgets prepared with reminder functionality. An android application is carried out to expose tablet’s every day agenda and pill taking notifications. It relieves the user of the mistake-prone obligations of administering wrong medication at incorrect time.
Hence, this Smart Pill Box will provide statistics to the affected person to take proper dosage of proper medication on the proper time. An extra characteristic of the Smart Pill Box is that it has cold storage for capsules that needs to be saved under bloodless temperature. The Smart tablet container can never move missing because it has an alternative, “Find my Box” in its android software. The principal goal of the proposed system is to layout a device that gives efficient medicine management, getting rid of the dependency of the patient on care takers or family members as it has many unique capabilities which emphasis on “patients fitness monitoring”, for that reason defining the scope.
Stress free remainder system to take tablets in time.
It is easy to store medicines, especially those medicine which must be kept in cold compartment.
The box will never go missing.
Alert messages which help to take medicines in time.
Box asks for refill when the pills in the box are empty by sending appropriate message.
The box has display and announcement which helps all kinds of patient.
In order to reduce the responsibility of family members of dividing the medications in the pill box, we assume that the medicine the patients need to take at particular times has been packed into the pill box. In this system we have to set the pill time for required medicine by using input system. We can set the different time for different pills. If the more than one pill is required at a time, give the box nos. to the system to get required pills. We also set the no. of pills we are inserting in the system. The real-time clock gives continuous time as an output. Monitor the time continuously using a Real-time clock to identify the pill time. If the system time matches with pill time, the system shows that that it is time to take a pill.
It is necessary to alert the user to take pills at a particular time. When the system time match with pill time, the buzzer start continuously until the push button is not pressed. When the push button pressed, the buzzer stops and the pills required to take at that time comes out to the user to avoid confusion among medicines. As pills removed by the user, it is necessary to put the no. of pills removed by the user. Multiple times a user required more than one pills of same medicine or more than one person are using the same system. So it is required that the no. of pills removed by the user.
The system counts no. of pills in the system by using the total no. of pills and the pills used by the patient. When the no. of pills remains less, the purchase order sends automatically to medical shop. On the basis of below attribute we are using the ID3 algorithm and to display result. The Architecture of the remind and confirm processes is shown in Fig. 2. At the medication time, the pill box will remind the elderly patients, to take their medication via alert sound, the sound is off only after push button is pressed.
1. ID 3 ((Iterative Dichotomiser 3) Algorithm ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node.
1) Establish Classification Attribute (in Table R)
2) Compute Classification Entropy.
3) For each attribute in R, calculate Information Gain using classification attribute.
4) Select Attribute with the highest gain to be the next Node in the tree (starting from the Root node).
5) Remove Node Attribute, creating reduced table RS.
6) Repeat steps 3-5 until all attributes have been used, or the same classification value remains for all rows in the reduced table.
In the future, we hope that the application can be to linked to med karts, if the tablets are empty it directly sends a prescription message to the med kart in which they can help us delivering the prescribed tablets to our door step. Scanning of prescription to load the app can be done using image processing technology.
The proposed system is suitable for all kinds of patients. It efficiently controls the time of patients to take medicine. It also reduces the ratio that patient misses and delays taking medicine. In addition, the box also has a cold storage for few precise medicine. If the tablets are empty in the box it sends an alert message to refill it. Find field helps in locating the box.
 G H.-W. Kuo, “Research and Implementation of Intelligent MedicalBox,” M.S.thesis, Department of Electrical Engineering, I-Shou University, Kaohsiung, TW, 2009
 S.-C. Huang, H.-Y. Chang,Y.-C. Jhu and G.-Y. Chen, “The intelligent pill box-design and implementation,” in proceedings of the IEEE International Conference on Consumer Electronics, May 26-28, Taiwan.
 T.L. Hayes, J.M. Hunt, A. Adami and J.A. Kaye, “An electronic pillbox for continuous monitoring of medication adherence,” in proceesings of the 28th IEEE EMBS Annual International Conference, Aug. 30-Sept. 3, 2006.
Project Done By Shashank Shinde, Tejas Kadaska, Pushpak Patil3, Rohit Barathe