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22
POMDP Models for Assistive Technology
, 2005
"... This paper presents a general decision theoretic model of interactions between users and cognitive assistive technologies for various tasks of importance to the elderly population. The model is a partially observable Markov decision process (POMDP) whose goal is to work in conjunction with a us ..."
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This paper presents a general decision theoretic model of interactions between users and cognitive assistive technologies for various tasks of importance to the elderly population. The model is a partially observable Markov decision process (POMDP) whose goal is to work in conjunction with a user towards the completion of a given activity or task. This requires the model to monitor and assist the user, to maintain indicators of overall user health, and to adapt to changes. The key strengths of the POMDP model are that it is able to deal with uncertainty, it is easy to specify, it can be applied to different tasks with little modification, and it is able to learn and adapt to changing tasks and situations.
Naturalistic Assessment of Everyday Activities and Prompting Technologies in Mild Cognitive Impairment
"... Older adults with mild cognitive impairment (MCI) often have difficulty performing complex instrumental activities of daily living (IADLs), which are critical to independent living. In this study, amnestic multi-domain MCI (N 5 29), amnestic single-domain MCI (N 5 18), and healthy older participants ..."
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Older adults with mild cognitive impairment (MCI) often have difficulty performing complex instrumental activities of daily living (IADLs), which are critical to independent living. In this study, amnestic multi-domain MCI (N 5 29), amnestic single-domain MCI (N 5 18), and healthy older participants (N 5 47) completed eight scripted IADLs (e.g., cook oatmeal on the stove) in a smart apartment testbed. We developed and experimented with a graded hierarchy of technology-based prompts to investigate both the amount of prompting and type of prompts required to assist individuals with MCI in completing the activities. When task errors occurred, progressive levels of assistance were provided, starting with the lowest level needed to adjust performance. Results showed that the multi-domain MCI group made more errors and required more prompts than the single-domain MCI and healthy older adult groups. Similar to the other two groups, the multi-domain MCI group responded well to the indirect prompts and did not need a higher level of prompting to get back on track successfully with the tasks. Need for prompting assistance was best predicted by verbal memory abilities in multi-domain amnestic MCI. Participants across groups indicated that they perceived the prompting technology to be very helpful. (JINS, 2013, 19, 442–452)
An Automated Prompting System for Smart Environments
"... Abstract. The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to a user for timely reminders or a brief in ..."
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Abstract. The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to a user for timely reminders or a brief instruction describing the way a task should be done for successful completion. This technology is in high demand with the desire for people who have physical or cognitive limitations to live independently in their homes. In this paper, we take the approach to fully automating a prompting system without any predefined rule set or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that is collected with volunteer participants in our smart home testbed.
A personalized time management assistant: Research directions
- Persistant Assistants: Living and working with AI, workshop at the AAAI Spring Symposium 2005
, 2005
"... This paper presents ongoing work to build the Personalized Time Manager (PTIME) system, a persistent assistant that builds on our previous work on a personalized calendar agent (PCalM) (Berry et al. 2004). PCalM was an early test of the hypothesis that in order to persist and be useful, an intellige ..."
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Cited by 3 (1 self)
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This paper presents ongoing work to build the Personalized Time Manager (PTIME) system, a persistent assistant that builds on our previous work on a personalized calendar agent (PCalM) (Berry et al. 2004). PCalM was an early test of the hypothesis that in order to persist and be useful, an intelligent agent must learn and adapt to the user’s changing needs. PTIME extends this idea to include more general time management, soft constraint satisfaction, richer learning, persistence, and steps towards adjustable autonomy.
Data Mining Challenges in Automated Prompting Systems, presented at the
- Proceedings of 2011 Internatuional Conference on Intelligent User Interfaces Workshop on Interaction with Smart Objects
, 2011
"... With the rising cost of medical treatment and majority of the aging population preferring an independent lifestyle, the need for assistive technologies and smarted devices are increasing like never before. A prompting system is a technique that provides interventions to a smart home inhabitant in or ..."
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With the rising cost of medical treatment and majority of the aging population preferring an independent lifestyle, the need for assistive technologies and smarted devices are increasing like never before. A prompting system is a technique that provides interventions to a smart home inhabitant in order to ensure successful completion of an activity. Machine learning techniques could be used to automate this system but it comes with the challenge of imbalanced class distribution naturally occurring in the data. In this paper, we comparatively analyze two techniques (sampling and cost sensitive learning) to deal with this challenge. Author Keywords Automated prompting, smart environments, data mining.
Application of Cognitive Rehabilitation Theory to the Development of Smart Prompting Technologies
, 2011
"... Abstract—Older adults with cognitive impairments often have difficulty performing instrumental activities of daily living (IADLs). Prompting technologies have gained popularity over the last decade and have the potential to assist these individuals with IADLs in order to live independently. Although ..."
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Abstract—Older adults with cognitive impairments often have difficulty performing instrumental activities of daily living (IADLs). Prompting technologies have gained popularity over the last decade and have the potential to assist these individuals with IADLs in order to live independently. Although prompting techniques are routinely used by caregivers and health care providers to aid individuals with cognitive impairment in maintaining their independence with everyday activities, there is no clear consensus or gold standard regarding prompt content, method of instruction, timing of delivery, or interface of prompt delivery in the gerontology or technology literatures. In this paper, we demonstrate how cognitive rehabilitation principles can inform and advance the development of more effective assistive prompting technologies that could be employed in smart environments. We first describe cognitive rehabilitation theory (CRT) and show how it provides a useful theoretical foundation for guiding the development of assistive technologies for IADL completion. We then use the CRT framework to critically review existing smart prompting technologies to answer questions that will be integral to advancing development of effective smart prompting technologies. Finally, we raise questions for future exploration as well as challenges and suggestions for future directions in this area of research. Index Terms—aging, cognitive impairment, assistive technology, instrumental activities of daily living T I.
Automated Prompting in a Smart Home Environment
"... Abstract — With more older adults and people with cognitive disorders preferring to stay independently at home, prompting systems that assist with Activities of Daily Living (ADLs) are in demand. In this paper, with the introduction of “The PUCK”, we take the very first approach to automate a prompt ..."
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Abstract — With more older adults and people with cognitive disorders preferring to stay independently at home, prompting systems that assist with Activities of Daily Living (ADLs) are in demand. In this paper, with the introduction of “The PUCK”, we take the very first approach to automate a prompting system without any predefined rule set or user feedback. We statistically analyze realistic prompting data and devise a classifier from statistical outlier detection methods. Further, we devise a sampling technique to help with skewed and scanty data sets. We empirically find a class distribution that would be suitable for our work and validate our claims with the help of three classical machine learning algorithms. Keywords-smart environments; automated prompting; prompting systems; machine learning I.
PUCK: An Automated Prompting System for Smart Environments Towards achieving automated prompting; Challenges
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When Did You Start Doing That Thing That You Do? Interactive Activity Recognition and Prompting
"... www.brainaid.com We present 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 ..."
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www.brainaid.com We present 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 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. Novel aspects of the model include (i) the introduction of adaptive options, which employ a lightweight user model and are able to provide near-optimal performance with little exploration; (ii) a restricted-inquiry dual-control algorithm that can appeal for help from the user when sensor data is ambiguous; and (iii) a combined filtering / most likely-sequence algorithm for activities determining the beginning and ending time points of the user’s activities. Experiments show that each of these features contributes to the robustness of the model. 1
