Recognizing complex instrumental activities of daily living using scene information and fuzzy logic

TitleRecognizing complex instrumental activities of daily living using scene information and fuzzy logic
Publication TypeJournal Article
Year of Publication2015
AuthorsTanvi Banerjee, James Keller, Mihail Popescu, Marjorie Skubic
JournalComputer Vision and Image Understanding
Volume140
Pagination68–82
Date Published11/2015
KeywordsActivities of daily living, Activity analysis, Depth images, Eldercare, Fuzzy Logic, Image features, Scene understanding
Abstract

We describe a novel technique to combine motion data with scene information to capture activity characteristics of older adults using a single Microsoft Kinect depth sensor. Specifically, we describe a method to learn activities of daily living (ADLs) and instrumental ADLs (IADLs) in order to study the behavior patterns of older adults to detect health changes. To learn the ADLs, we incorporate scene information to provide contextual information to build our activity model. The strength of our algorithm lies in its generalizability to model different ADLs while adding more information to the model as we instantiate ADLs from learned activity states. We validate our results in a controlled environment and compare it with another widely accepted classifier, the hidden Markov model (HMM) and its variations. We also test our system on depth data collected in a dynamic unstructured environment at TigerPlace, an independent living facility for older adults. An in-home activity monitoring system would benefit from our algorithm to alert healthcare providers of significant temporal changes in ADL behavior patterns of frail older adults for fall risk, cognitive impairment, and other health changes.

DOI10.1016/j.cviu.2015.04.005
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