Machine Learning and Dynamic User Interfaces in a Con-text Aware Environment
Ham, Nathaniel (2013)
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Lataukset:
Ham, Nathaniel
Haaga-Helia ammattikorkeakoulu
2013
All rights reserved
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2015121721339
https://urn.fi/URN:NBN:fi:amk-2015121721339
Tiivistelmä
The increasing usage of smartphones in daily life has received considerable attention in academic and industry driven research to be utilized in the health sector. There has been development and dissemination of a variety of health-related smartphone applications. However, to date, there is few to none applications based on nurses’ historical or behavioral preferences. Mobile application development for health care sector requires extensive at-tention to security, reliability, and accuracy. In nursing applications, the users are often re-quired to navigate in hospital environments, select patients to support, read the patient histo-ry and set action points to assist the patient during their shift. Finally, they have to report their performance on patient related activities and other relevant information before they leave for the day. In a working day, a nurse often visits different locations such as the pa-tient’s room, different laboratories, and offices for filing reports.
There is still a limited capability to access context relevant information on a smartphone with minimal recourse such as Wi-Fi triangulation. The WI-FI triangulation signals fluctuate significantly for indoor location positioning. Therefore, providing relevant location based services to a mobile subscriber has become challenging.
This paper addresses this gap by applying machine learning and behavior analysis to antici-pate the potential location of the nurse and provide the required services. The application concept was already presented in IMCOM 2015. This paper focuses on the process to ascertain a user’s context, the process of analyzing and predicting user behavior, and final-ly, the process of displaying the information through a dynamically generated UI. The case study application is designed, developed, and assessed in Finland based on the mobile learning usability and user experience (mLUX) framework.
To make the machine learning and behavior analysis anticipate or confirm the nurse’s loca-tion more efficiently, we have applied various technological solutions. In the first attempt, we utilized Knime server and in latest implementation we applied Python with Scikit-Learn to increase the speed and to use primarily open source solutions. This paper describes the development process, applied methodology, our technological experiment results and the user evaluation experience. This research result is applicable to software development methodology, practitioners, and academicians.
There is still a limited capability to access context relevant information on a smartphone with minimal recourse such as Wi-Fi triangulation. The WI-FI triangulation signals fluctuate significantly for indoor location positioning. Therefore, providing relevant location based services to a mobile subscriber has become challenging.
This paper addresses this gap by applying machine learning and behavior analysis to antici-pate the potential location of the nurse and provide the required services. The application concept was already presented in IMCOM 2015. This paper focuses on the process to ascertain a user’s context, the process of analyzing and predicting user behavior, and final-ly, the process of displaying the information through a dynamically generated UI. The case study application is designed, developed, and assessed in Finland based on the mobile learning usability and user experience (mLUX) framework.
To make the machine learning and behavior analysis anticipate or confirm the nurse’s loca-tion more efficiently, we have applied various technological solutions. In the first attempt, we utilized Knime server and in latest implementation we applied Python with Scikit-Learn to increase the speed and to use primarily open source solutions. This paper describes the development process, applied methodology, our technological experiment results and the user evaluation experience. This research result is applicable to software development methodology, practitioners, and academicians.