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A monitoring, modeling, and interactive recommendation system for in-home caregivers: demo abstract

Authors:

Ye Gao,

John StankovicAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 587 - 588

https://doi.org/10.1145/3384419.3430422

Published: 16 November 2020 Publication History

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Abstract

Family caregivers often report increased anxiety and depression. In order to improve the interactions between in-home patients and caregivers, and reduce strain on caregivers, we build a monitoring, modeling, and interactive recommendation system for caregivers for in-home dementia patient care. The system includes monitoring for mood by speech, building classifiers that work in realistic home settings, and supporting an adaptive recommendation system to reduce stress of the caregiver. This demo shows how our system supports caregivers in practice through several scenarios.

References

[1]

A Alzheimer's. [n.d.]. Alzheimer's Association. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016; 12: 459--509.

Google Scholar

[2]

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on Worldwide web. 661--670.

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[3]

Meiyi Ma, Ridwan Alam, Brooke Bell, Kayla de la Haye, Donna Spruijt-Metz, John Lach, and John Stankovic. 2017. M^ 2G: a monitor of monitoring systems with ground truth validation features for research-oriented residential applications. In 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 10--18.

Google Scholar

[4]

Richard Schulz and Lynn M Martire. 2004. Family caregiving of persons with dementia: prevalence, health effects, and support strategies. The American journal of geriatric psychiatry 12, 3 (2004), 240--249.

Google Scholar

[5]

Qingyun Wu, Naveen Iyer, and Hongning Wang. 2018. Learning contextual bandits in a non-stationary environment. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 495--504.

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Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Gao YSalekin AGordon KRose KWang HStankovic J(2022)Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution DetectionACM Transactions on Computing for Healthcare10.1145/34923003:2(1-22)Online publication date: 30-Apr-2022
https://dl.acm.org/doi/10.1145/3492300
Gao YJabbour JSchlegel EMa MMcCall MWijayasingha LKo EGordon KRose KWang HStankovic J(2022)Out-of-the-Box Deployment to Support Research on In-Home Care of Alzheimer’s PatientsIEEE Pervasive Computing10.1109/MPRV.2021.310688521:1(37-47)Online publication date: 1-Jan-2022
https://doi.org/10.1109/MPRV.2021.3106885

Index Terms

A monitoring, modeling, and interactive recommendation system for in-home caregivers: demo abstract
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics

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SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 16 November 2020

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SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems

November 16 - 19, 2020

Virtual Event, Japan

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Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Gao YSalekin AGordon KRose KWang HStankovic J(2022)Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution DetectionACM Transactions on Computing for Healthcare10.1145/34923003:2(1-22)Online publication date: 30-Apr-2022
https://dl.acm.org/doi/10.1145/3492300
Gao YJabbour JSchlegel EMa MMcCall MWijayasingha LKo EGordon KRose KWang HStankovic J(2022)Out-of-the-Box Deployment to Support Research on In-Home Care of Alzheimer’s PatientsIEEE Pervasive Computing10.1109/MPRV.2021.310688521:1(37-47)Online publication date: 1-Jan-2022
https://doi.org/10.1109/MPRV.2021.3106885

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Caring for caregivers: designing for integrality

Monitoring Changes in Home Health Care: A Comparison of Two National Surveys

Bluetooth-enabled in-home patient monitoring system: early detection of Alzheimer's disease

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