Individualised responsible Artificial Intelligence for home-based rehabilitation

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Abstract

Socioeconomic reasons post COVID-19 demand unsupervised home-based rehabilitation and specifically, Artificial Ambient Intelligence with individualisation to support engagement and motivation. Artificial Intelligence must also comply with Accountability, Responsibility, and Transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras.
Original languageEnglish
Article number2
Number of pages25
JournalSensors
Volume21
Issue number1
DOIs
Publication statusPublished - 22 Dec 2020

Keywords

  • accountable artificial intelligence
  • responsible artificial intelligence
  • transparent artificial intelligence
  • hybrid ensemble learning
  • home-based rehabilitation
  • patient-centric individualised rehabilitation
  • automated timed up and go test
  • automated five time sit to stand test

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