Building a framework for recognition of activities of daily living from depth images using fuzzy logic

TitleBuilding a framework for recognition of activities of daily living from depth images using fuzzy logic
Publication TypeConference Proceedings
Year of Publication2014
AuthorsTanvi Banerjee, James Keller, Marjorie Skubie
Conference Name2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Pagination540 - 547
Date Published07/2014
PublisherIEEE
Conference LocationBeijing, China
ISSN Number978-1-4799-2073-0
Accession Number14564984
Keywords3D feature extraction, Activities of daily living, behavior change measurement, bounding box parameters, data collection, Data Mining, depth image, Depth images, DH-HEMTs, feature extraction, foreground images, Fuzzy Logic, fuzzy reasoning, fuzzy rules, health change prediction, hierarchical fuzzy rule model, IADL, IADLS, instrumental activities-of-daily living recognition, Kinect depth data, learning (artificial intelligence), machine learning, Niobium, older adults, Sensors Three-dimensional displays, system model, three-layered FIS model, three-level fuzzy inference
Abstract

Complex activities such as instrumental activities of daily living (IADLs) can be identified by creating a hierarchical model of fuzzy rules. In this work, we present a framework to model a specific IADL - "making the bed". For this activity recognition, the need for a three level Fuzzy Inference System (FIS) model is shown. Simple features such as bounding box parameters were extracted from the foreground images and combined with 3D features extracted from the Kinect depth data. This was then fed as input to the three layered FIS for further analysis. Data collected from several participants were tested and evaluated. Such a framework can be used to model several other IADLS as well as basic activities of daily living (ADLs). Analysis of ADLs can be used to compare daily patterns in older adults to measure changes in behavior. This can then be used to predict health changes to assist older adults in leading independent lifestyles for longer time periods.

DOI10.1109/FUZZ-IEEE.2014.6891647
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