2021/05/19 by Chrisogonas O. Odhiambo, Chrisogonas Odhiambo, Pamela J. Wright +7
Computer Science · Health Professions · Medicine · #Accelerometer #Android (operating system) #Artificial Intelligence (cs.AI) #Artificial intelligence #Artificial neural network #Cloud computing #Computer science #Embedded system #FOS: Computer and information sciences #Gesture #Human-Computer Interaction (cs.HC) #Human–computer interaction #Leverage (statistics) #Machine Learning (cs.LG) #Machine learning #Medication Adherence and Compliance #Mobile Health and mHealth Applications #Operating system #Pharmaceutical studies and practices #Smartwatch #Wearable computer #cs.AI #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.08907
arxiv created 2021/05/19 · openalex publication_date 2021/05/19 · arxiv updated 2021/05/20 · openalex created_date 2021/05/24 · openalex updated_date 2026/07/28
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.