Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation

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.

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“…This is evidenced through both the accuracy of the model and the specific misclassifications. The misclassification cases were effectively leading to appropriate actions and thus to a fully trustworthy system, in accordance with the ART principles 24 .…”

“…Interpretation of the AI output provides a prompt that supports the practitioners' ability to understand what care pathway or treatment/action is suitable for each result in all possible eventualities. There is no further interpretation required by the practitioner to estimate possible misclassification probabilities or errors, enforcing high explainability with no need for transparency in the decision taken by the algorithm, so providing dimensions that translate to accountable reliable and trustworthy (ART) principles 24 .…”

“…By applying this architecture, we were able to integrate three distinct layers of trustworthiness within our diagnostic system: trust in data accuracy at the sensor layer, supporting the accountability and reliability of AI; trust in the decisions generated from the data, within the application layer, supporting accountability and explainability; trust in the security of the whole system, within the networking and processing layer, addressing confidentiality, integrity and availability. By doing so, we propose that our system addresses the full scope of trust, required in AI systems, as defined by the ART principles 24 .…”

In the era of long COVID, can we seek new techniques for better rehabilitation?

“…Initially, the environment and assessment are essential to support engagement and motivation. Thus, Vourganas et al 6 demonstrated a patient‐centric individualized home‐based rehabilitation support system with the help of accountability, responsibility, and transparency (ART) AI. They used the timed up and go (TUG) and five‐time sit to stand (FTSTS) tests to evaluate patients’ daily living activity performance in the presence or development of comorbidities.…”

Artificial intelligence in musculoskeletal conditions

“…Patients must often continue rehabilitation exercises at home after being discharged. To improve compliance with these exercise programs, patient motivation and involvement need to be strengthened [48]. Resources to assist patients in home rehabilitation are often generic and not well adapted to the individual's needs and preferences [49].…”