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. This paper describes the model, gives a general learning method which enables the model to be learned from partially labeled data, and shows how the model can be applied within our research program on technologies for wellness. In particular, we show how the model is used in three tasks: assisted handwashing, health and safety monitoring, and wheelchair mobility. The paper gives an overview of ongoing work into each of these areas, and discusses future directions.
Welcome to the Intelligent Assistive Technology and Systems Lab (IATSL), located in the Department of Occupational Science and Occupational Therapy at the University of Toronto. We are a multi-disciplinary group of researchers with backgrounds in engineering, computer science, occupational therapy, speech-language pathology, and gerontology. Our goal is to develop zero-effort technologies that are adaptive, flexible, and intelligent, to enable users to participate fully in their daily lives. We have an opening for an enthusiastic post-doctoral fellow to work on computer vision, signal processing, and video analysis algorithms for applications in sleep monitoring and medical diagnosis. Please read more about this position here at http://www.cs.toronto.edu/
Cognitive impairments prevent older adults from using powered wheelchairs due to safety concerns, thus reducing their mobility and resulting in increased dependence on caregivers. An intelligent powered wheelchair is proposed to help restore mobility, while ensuring safety. Machine vision and learning techniques are described to help prevent collisions with obstacles, and provide reminders and navigation assistance through adaptive prompts.
In this paper we discuss the gap that exists between the caregivers of older adults attempting to age-in-place and sophisticated ”smart-home” systems that can sense the environment and provide assistance when needed. We argue that smart-home systems need to be customizable by end-users, and we present a general-purpose model for cognitive assistive technology that can be adapted to suit many different tasks, users and environments. Al- though we can provide mechanisms for engineers and designers to build and adapt smart-home systems based on this general-purpose model, these mechanisms are not easily understood by or sufficiently user-friendly for actual end users such as older adults and their care- givers. Our goal is therefore to study how to bridge the gap between the end-users and this technology. In this paper, we discuss our work on this problem from both sides: developing technology that is customizable and general-purpose, and studying user’s abilities and needs when it comes to building smart-home systems to help with activities of daily living. We show how a large gap still exists, and propose ideas for how to bridge the gap.
Cook, Diane J. (Washington State University) | Krishnan, Narayanan C. (Washington State University) | Rashidi, Parisa (University of Florida) | Skubic, Marjorie (University of Missouri-Columbia) | Mihailidis, Alex (University of Toronto)
The aging population, the increasing cost of formal health care, caregiver burden and the importance that older adults place on living independently in their own homes motivate the need for the development of patient-centric technologies that promote safe independent living. These patient-centric technologies need to address various aging related physical and cognitive health problems such as heart disease, diabetes, deterioration of physical function, falling, wandering, strokes, and memory problems, lack of medication adher- ence, cognitive decline and loneliness. Advances in the sensor and computing technology that allow for ambient unobtrusive and continuous home monitoring have opened new vistas for the development of such technologies.