PillSense: Medication Adherence Monitoring System
The purpose of the study is to determine if a suite of few sensors embedded in a 3D printed medication bottle can accurately determine the time and capture the motion associated with a medication-taking person. The circuit that combines all sensors is small in size 3x3 inches. The system sends acceleration, weight, and open/close measurements several times a second to a nearby computer. A goal of the study is to understand the performance of the sensors or any combination of them as a building block for medication taking activity monitoring.
Recent years have seen the size, cost and energy consumption of small wireless sensor decrease by several orders of magnitude. Indeed, today, wireless sensors can be bought for an affordable price. These improvements have made it possible to connect everyday objects to the Internet, resulting in the visionary concept of the Internet of Things (IoT). While there are many possible uses of the IoT for art, education, and security, measuring everyday activity to improve the health and wellbeing of persons is rapidly becoming an active area of research. Medication adherence is an important component of health and wellbeing, with voluminous studies showing the important of adequate medication adherence. Current quantification of medication adherence is enormously obstructive and resource-consuming, were patients must use wearable sensors continuously. This work seeks to quantify the ability of few styles of IoT sensors to enable accurate measurements and monitoring of medication taking activity at a much lower cost than was previously possible and without invasiveness. If medication adherence can be determined using IoT style sensing, new forms of self-management and clinical care would be enabled.
We built a pill bottle using a 3D printer and equipped it with a combination of digital and analog sensors for medication adherence monitoring applications. We aim at developing a low-energy sensor system based on the PIP tag as the platform.
We aim at using the data from the wireless sensors embedded in the 3D printed pill bottle for the following:
1. Studying the hand gestures associated with medication taking. From these gestures we try to recognize the actions of twisting the bottle cap, pouring pill into hand and hand to mouth.
2. Pill bottle user authentication application. We aim at using the data obtained from the system, especially the acceleration data, for user recognition and authentication (who took the medicine and when?)