# Design and Feasibility of a Safe Pill Bottle
## 4 Discussion  
Results from this preliminary study of 16 participants demonstrate the feasibility of the proposed concept of a \"safe bottle\" that can recognize the class of user as a child or an adult. Accuracy of the NN was approximately 97%, with an error rate of just 3%. Furthermore, we emphasize that the safe bottle is intended primarily to protect children 0-5 years of age from unintentional poisoning, and our pilot test used for pragmatic reasons (availability, developmental maturity to complete the task) children with an average age of children 7.5 years (range = 5-10). We greatly outperformed our initial goal of less than 10% of errors for children ages 0-5.  
Ultimately, we envision the use of \"smart bottles\" as devices that can replace or supplement current child-resistant lids on pill containers. We are unaware of any similar such initiatives in the field to improve the safety of children; the closest comparator we have located is a proposal, not yet prototyped to our knowledge, to use biometric characteristics such as fingerprints to restrict the use of firearms [16].  
Figure 7: The receiver operating characteristic (ROC) performance of NN trained and tested with all events.  
As our prototype moves into implementation, an embedded controller will be placed at the bottom or the side of the pill bottle, separate from pills in the bottle; this model replicates existing smart pill bottles from Adhere Tech [11]. Since medical or advertising labels can be taped over the sensors without changing their performance, safe pill bottles would look identical to currently-used standard pill bottles. Given the frequency of unintentional child poisoning, and the fact that the current technology of child-resistant caps is imperfect, smart bottles may offer an alternative that provides greater protection and higher efficacy than currently used strategies.  
Smart bottle technology is not without limitations. There will be an increase in price per bottle, primarily because of the price of the embedded controller. Capacitive sensors can be taped onto the bottle or integrated in the bottle during injection molding of the plastic bottle. Although costs are likely to decrease over time, and re-usable bottles could easily be manufactured (consider the precedent of reusable glass bottles in many eras and jurisdictions), some consumers and manufacturers are likely to continue to choose the lowest-cost legal strategies to reduce the child poisoning risk, which currently are child-resistant caps. As costs decrease, industry and consumer decisions may change, as evidenced by the early commercial success of the Adhere Tech bottle [11].  
Engineering limitations exist also. Current processing of the event and selection of the region of interest can be significantly improved. We currently use only 0.5 s at the beginning of the event to detect the average of values in the region of interest. However, sophisticated pre-processing can significantly improve noise immunity in recorded signals. Furthermore, our pilot test did not provide enough data for training of the neural network. We expect to conduct additional tests in the future.  
Finally, there is potential to improve our use of neural networks. NN allow analysts to set the thresholds for class separation. The results presented for our experiment used the default NN threshold of 0.5. Sensitivity analyses suggest that modified thresholds would reduce the number of events when children are detected as adults, but would also increase the number of errors when adults are detected as children, or \"false alarms.\" Increased false alarms where adults are incorrectly classified as children might be preferred in our system over errors where children are misidentified as adults. With larger datasets, NN could be retrained based on feedback from users, thus optimizing performance for a given group of users.