ArticleSensors (Basel, Switzerland)2020
Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.Current medical science · 2024Review
- Technologies in Home-Based Digital Rehabilitation: Scoping Review.JMIR rehabilitation and assistive technologies · 2023Article
- Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL.Medicine, health care, and philosophy · 2023Article
- In the era of long COVID, can we seek new techniques for better rehabilitation?Chronic diseases and translational medicine · 2022Article
- Rehabilitation in long COVID-19: A mini-review.World journal of methodology · 2022Review
- Smartphone-based DNA malaria diagnostics using deep learning for local decision support and blockchain technology for security.Nature electronics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.