# Design and Feasibility of a Safe Pill Bottle
## 1 Introduction and Background  
Unintentional child poisoning of children under six years of age represents nearly 50% of the 2.4 million poisoning exposures reported to US poison control centers annually [1; 2]. Moreover, not all exposures of children ages 0-5 years are reported to poison control centers, so existing numbers are believed to be underestimates. Currently used child-resistant caps have proven to be an efficient method of saving young children's lives, but they are not foolproof. Federal regulations permit child-resistant caps to be used if they are opened by 20% of children ages 42-51 months within 10 min (16 CFR 1700.20). Furthermore, child-resistant caps are frustrating to many consumers, including elderly and disabled individuals. There are at least two primary reasons for high child poisoning rates. First, medications are more available than ever in our homes. The presence of prescription drugs, over-the-counter medicines, vitamins, herbs, and dietary supplements near young children is extremely common, and the simple presence of dangerous items in the home increases the odds of young children encountering them and unintentionally poisoning themselves [3]. Second, because of our fast-paced lifestyles, adults might forget to properly store medicine away from children's reach, or a parent or grandparent might forget a pill bottle is in a handbag or briefcase. Young children who discover medicine while adults were not looking comprise 95% of unintentional medication overdose ER visits [4]. New sensing technologies and biometric identification may detect a variety of hazardous events. Biometric authentication could ensure that the right person is accessing the right data. The most popular biometric authentication methods include fingerprint authentication based on 2D image processing techniques to extract unique physiological features of the user [5]. Palm features, such as palm prints and palm-vein networks have been proposed [6; 7]. [PERSON] et al. proposed a technique using 3D surface curvature maps for feature extraction [8]. All methods demonstrate that hand geometry and surface curvature influence the contact area with an object and allow for unique identification of the user. Changes in contact area and pressure influence capacitance of the skin to object interface [9]. We propose a new method of identification of the class of users and detection of critical events when a child is trying to open a pill bottle. The Internet of Things (IoT) introduced a paradigm where everyday objects feature identification, sensing, networking, and processing capabilities that allows them to communicate with one another and with other devices and services over the Internet [10]. Ubiquity of IoT sensors and services provides an opportunity for new devices and services. Smart pill bottles, such as the AdhereTech smart pill bottle [11], are increasingly used for monitoring of drug compliance. We plan to expand the existing functionality of connectivity of the smart pill bottle with detection of critical events to prevent child poisoning. The same technology can be used to reduce child poisoning from other household products. Our preliminary results demonstrated feasibility of use of capacitive measurement to identify the class of user (child vs. adult) [12]. Widespread use of capacitance-based human-computer interfaces motivates the integration of capacitance measurement interfaces in a number of commercially available microcontrollers. As an example, the microcontroller NXP MKL26Z64 VFT4 used in the Teensy LC sensor platform [13] supports capacitive measurements from up to 11 pins with a default accuracy of 0.02 pF and a measurement time in the order of 1 millisecond. The microcontroller features low power consumption, a small 48 QFN package, and a low price, thus making it suitable for embedded IoT platforms. Herein, we describe a pilot study with the prototype safe bottle [12] to demonstrate the effectiveness of our system design for the real-time generation of warnings.