Imperial College London
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ProfessorPayamBarnaghi

Faculty of Medicine, Department of Brain Sciences

Chair in Machine Intelligence Applied to Medicine
 
 
 
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Contact

 

p.barnaghi Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Raposo:2023:10.1109/JIOT.2023.3290833,
author = {Raposo, de Lima M and Vaidyanathan, R and Barnaghi, P},
doi = {10.1109/JIOT.2023.3290833},
journal = {IEEE Internet of Things Journal},
pages = {18537--18552},
title = {Discovering behavioural patterns using conversational technology for in-home health and well-being monitoring},
url = {http://dx.doi.org/10.1109/JIOT.2023.3290833},
volume = {10},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Advancements in conversational AI have createdunparalleled opportunities to promote the independence andwell-being of older adults, including people living with dementia(PLWD). However, conversational agents have yet to demonstratea direct impact in supporting target populations at home,particularly with long-term user benefits and clinical utility. Weintroduce an infrastructure fusing in-home activity data capturedby Internet of Things (IoT) technologies with voice interactionsusing conversational technology (Amazon Alexa). We collect 3103person-days of voice and environmental data across 14 households with PLWD to identify behavioural patterns. Interactionsinclude an automated well-being questionnaire and 10 topics ofinterest, identified using topic modelling. Although a significantdecrease in conversational technology usage was observed afterthe novelty phase across the cohort, steady state data acquisitionfor modelling was sustained. We analyse household activitysequences preceding or following Alexa interactions throughpairwise similarity and clustering methods. Our analysis demonstrates the capability to identify individual behavioural patterns,changes in those patterns and the corresponding time periods.We further report that households with PLWD continued usingAlexa following clinical events (e.g., hospitalisations), which offersa compelling opportunity for proactive health and well-beingdata gathering related to medical changes. Results demonstratethe promise of conversational AI in digital health monitoringfor ageing and dementia support and offer a basis for trackinghealth and deterioration as indicated by household activity, whichcan inform healthcare professionals and relevant stakeholdersfor timely interventions. Future work will use the bespokebehavioural patterns extracted to create more personalised AIconversations.
AU  - Raposo,de Lima M
AU  - Vaidyanathan,R
AU  - Barnaghi,P
DO  - 10.1109/JIOT.2023.3290833
EP  - 18552
PY  - 2023///
SN  - 2327-4662
SP  - 18537
TI  - Discovering behavioural patterns using conversational technology for in-home health and well-being monitoring
T2  - IEEE Internet of Things Journal
UR  - http://dx.doi.org/10.1109/JIOT.2023.3290833
UR  - https://ieeexplore.ieee.org/document/10168160
UR  - http://hdl.handle.net/10044/1/105222
VL  - 10
ER  -
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