Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation

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) requi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ioannis Vourganas [verfasserIn] Vladimir Stankovic [verfasserIn] Lina Stankovic [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	accountable artificial intelligence responsible artificial intelligence transparent artificial intelligence hybrid ensemble learning home-based rehabilitation patient-centric individualised rehabilitation

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2020), 1, p 2
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1, p 2

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21010002

Katalog-ID:	DOAJ034166653

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allfieldsSound	10.3390/s21010002 doi (DE-627)DOAJ034166653 (DE-599)DOAJ161ab88f58d243f7bacae47f2fd4d538 DE-627 ger DE-627 rakwb eng TP1-1185 Ioannis Vourganas verfasserin aut Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 <i<k</i<-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. accountable artificial intelligence responsible artificial intelligence transparent artificial intelligence hybrid ensemble learning home-based rehabilitation patient-centric individualised rehabilitation Chemical technology Vladimir Stankovic verfasserin aut Lina Stankovic verfasserin aut In Sensors MDPI AG, 2003 21(2020), 1, p 2 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2020 number:1, p 2 https://doi.org/10.3390/s21010002 kostenfrei https://doaj.org/article/161ab88f58d243f7bacae47f2fd4d538 kostenfrei https://www.mdpi.com/1424-8220/21/1/2 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1, p 2
language	English
source	In Sensors 21(2020), 1, p 2 volume:21 year:2020 number:1, p 2
sourceStr	In Sensors 21(2020), 1, p 2 volume:21 year:2020 number:1, p 2
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institution	findex.gbv.de
topic_facet	accountable artificial intelligence responsible artificial intelligence transparent artificial intelligence hybrid ensemble learning home-based rehabilitation patient-centric individualised rehabilitation Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Ioannis Vourganas @@aut@@ Vladimir Stankovic @@aut@@ Lina Stankovic @@aut@@
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callnumber-first	T - Technology
author	Ioannis Vourganas
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topic_title	TP1-1185 Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation accountable artificial intelligence responsible artificial intelligence transparent artificial intelligence hybrid ensemble learning home-based rehabilitation patient-centric individualised rehabilitation
topic	misc TP1-1185 misc accountable artificial intelligence misc responsible artificial intelligence misc transparent artificial intelligence misc hybrid ensemble learning misc home-based rehabilitation misc patient-centric individualised rehabilitation misc Chemical technology
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title	Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation
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title_sort	individualised responsible artificial intelligence for home-based rehabilitation
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title_auth	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 <i<k</i<-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.
abstractGer	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 <i<k</i<-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.
abstract_unstemmed	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 <i<k</i<-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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title_short	Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation
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