Authors
Henrik Hautop Lund*, Yan-Xin Liu, Massimiliano Leggieri
Center for Playware, Technical University of Denmark, Building 326, 2800 Kgs., Lyngby, Denmark
*Corresponding author. Email: [email protected]
Corresponding Author
Henrik Hautop Lund
Received 25 November 2019, Accepted 5 December 2019, Available Online 29 February 2020.
DOI
https://doi.org/10.2991/jrnal.k.200222.007How to use a DOI?
Keywords
Playware; Moto Tiles; Big Data; AI
Abstract
We developed a Big Data and AI approach for the screening and early detection of health risks among seniors. The approach is based on seniors performing playful activities on the Moto Tiles. The activities are organized in a Body & Brain Age Test, which is composed on four games of 30 s each on the Moto Tiles. A whole population of individuals take the Body & Brain Age Test, and the performance data is collected for each game in the test. The Big Data approach allows the system to identify the nominal score for each age. The system can automatically generate a personalized training protocol based on the score in the Body & Brain Age Test. This is done by using the performance score to identify which physical and/or cognitive abilities are in need of training, and then generate a protocol based on Moto Tiles games, which tend to increase those particular skills as verified in clinical effect studies. The suitability of the method was tested in a small effect test with seniors with mild dementia at a care institution in Denmark. The results show that the seniors with dementia who were screened to be at high risk of falling, within the short period of training with the automatically generated personalized protocol increased their skills to no longer be at risk of falling.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).