Interactive activity recognition and prompting to assist people with cognitive disabilities
Open accessAbstract
This paper presents a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user's state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin, resume, or end tasks. The objective of the system is to help the user maintain a daily schedule of activities while minimizing interruptions from questions or prompts. The model is built upon an option-based hierarchical POMDP. Options can be programmed and customized to specify complex routines for prompting or questioning. The paper proposes a heuristic approach to solving the POMDP based on a dual control algorithm using selective-inquiry that can appeal for help from the user explicitly when the sensor data is ambiguous. The dual control algorithm is working effectively in the unified control model which features the adaptive option and robust state estimation. Simulation results show that the unified dual control model achieves the best performance and efficiency comparing with various alternatives. To further demonstrate the system's performance, lab experiments have been carried out with volunteer actors performing a series of carefully designed scenarios with different kinds of interruption cases. The results show that the system is able to successfully guide the agent through the sample schedule by delivering correct prompts while efficiently dealing with ambiguous situations.
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In the interactive activity recognition software, the concern was that patients would reject an overly intrusive technology, hence a balance was sought.
“Here, the concern was that patients would reject an overly intrusive technology; hence a balance was sought.”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
In the interactive activity recognition software, only important interventions are made, and all interventions are partially optional because declining one prompt leads to the same prompt later on.
“Moreover, only important interventions are made, and yet all interventions are partially optional because declining one prompt leads to the same prompt later on.”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
Interactive activity recognition software for people with cognitive disabilities is designed to prompt patients to maintain a daily schedule while minimising interruptions, and each intervention is contextualised so that the software learns the timing of future interventions from responses to past interventions.
“Take, for example, interactive activity recognition software for people with cognitive disabilities (Chu et al. 2012). The software is designed to prompt patients to maintain a daily schedule of activities (e.g., taking medication), whilst minimising interruptions to their wider goals. Each intervention is contextualised in such a way that the software learns the timing of future interventions”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
The system to prompt people with cognitive disabilities to take their medication is not designed to coerce patients into a given behaviour, nor to resemble a human being, and patients have autonomy not to interact with it.
“Consider, for example, the development of AI solutions to prompt people with cognitive disabilities to take their medication (Chu et al. 2012). On its face, this application may seem invasive, involving vulnerable users, limiting the effectiveness of receiver-conceptualised explanation. However, the system is not designed to coerce the patients into a given behaviour, nor is it designed to resemb”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
The researchers designed the system so that caregivers can spend most of their time providing empathic support and preserving a meaningful relationship with the patient.
“The researchers designed the system so that caregivers can spend most of their time providing empathic support and preserving a meaningful relationship with the patient.”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
Interactive activity recognition and prompting to assist people with cognitive disabilities is a representative AI4SG example in disability and public health with relevant factors (3), (4), and (7).
“Interactive activity recognition and prompting to assist people with cognitive disabilities | Chu et al. (2012) | Disability, public health | (3), (4), (7)”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. (2012)
Researchers have developed AI software that balances reminding the patient against annoying the patient.
“Consequently, researchers have developed AI software that balances reminding the patient against annoying the patient (Chu et al. 2012).”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Chu et al. 2012
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List of Most Common Abbreviations and Acronyms
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0011
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0011
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0010
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0009
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0005
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0013
- Authors
- Chu, Y., Chol Song, Y., Levinson, R., & Kautz, H.
- Affiliations
- University of Rochester (US)
- Department of Computer Science, University of Rochester, Rochester, NY 14627, USA
- BrainAid (United States) (US)
- ORCID
- Kautz, Henry
- Venue
- Journal of Ambient Intelligence and Smart Environments
- Publisher
- SAGE Publications
- Published
- 2012-09
- Volume
- 4
- Issue
- 5
- Pages
- 443-459
- ISSN
- 1876-1364 (ISSN-L)
- 1876-1372
- OpenAlex
- W1603731631
- Cited by
- 47
- References
- 49
- Citations by year
- 2023: 22 · 2022: 2 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 7 · 2013: 3
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Ambient Intelligence and Smart Environments (publishedVersion)
- Funders
- National Institutes of Health (1r21hd062898-01, W81XWH)
- Defense Advanced Research Projects Agency (W31P4Q)
- Office of the Secretary of Defense
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- activity recognition, prompting, assistive technology, cognitive disability, options framework, sensor data interpretation, robust state estimation
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