# MedSensor: Medication Adherence Monitoring Using Neural Networks on Smartwatch Accelerometer Sensor Data

arXiv: https://arxiv.org/abs/2105.08907 · HTML: https://mlsanity.com/paper/2105.08907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 1 of 1)

- [Recognition of Smoking Gesture Using Smart Watch Technology](https://mlsanity.com/paper/2003.02735/) (2020)

## Built on by (most cited first, 1 of 1)

- [Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks](https://mlsanity.com/paper/2206.07654/) (2022)

## Abstract (arXiv)

Poor medication adherence presents serious economic and health problems including compromised treatment effectiveness, medical complications, and loss of billions of dollars in wasted medicine or procedures. Though various interventions have been proposed to address this problem, there is an urgent need to leverage light, smart, and minimally obtrusive technology such as smartwatches to develop user tools to improve medication use and adherence. In this study, we conducted several experiments on medication-taking activities, developed a smartwatch android application to collect the accelerometer hand gesture data from the smartwatch, and conveyed the data collected to a central cloud database. We developed neural networks, then trained the networks on the sensor data to recognize medication and non-medication gestures. With the proposed machine learning algorithm approach, this study was able to achieve average accuracy scores of 97% on the protocol-guided gesture data, and 95% on natural gesture data.