Authors:
Bingchuan Yuan
;
John Herbert
and
Yalda Emamian
Affiliation:
University College Cork, Ireland
Keyword(s):
ADLs, Smartphone, Wearable Wireless Sensor, Machine Learning, Cloud Infrastructure, Unsupervised Learning, Real-time Activity Recognition.
Related
Ontology
Subjects/Areas/Topics:
Context
;
Context-Aware Applications
;
Detection and Estimation
;
Digital Signal Processing
;
Mobile and Pervasive Computing
;
Mobile Computing
;
Paradigm Trends
;
Pervasive Health
;
Software Engineering
;
Telecommunications
Abstract:
Learning and recognizing the activities of daily living (ADLs) of an individual is vital when providing an
individual with context-aware at-home healthcare. In this work, unobtrusive detection of inhabitants’ activities
in the home environment is implemented through the smartphone and wearable wireless sensor belt solution.
A hybrid classifier is developed by combining threshold-based methods and machine learning mechanisms.
Features extracted from the raw inertial sensor data are collected from a Body Area Network (BAN) (consisting
of the Zephyr BioHarness sensor and an Android smartphone), and are used to build classification models
using different machine learning algorithms. A cloud-based data analytics framework is developed to process
different classification models in parallel and to select the most suitable model for each user. The evolving
machine learning mechanism makes the model become customizable and self-adaptive by utilizing a cloud
infrastructure which also overcomes
the limitation of the computing power and storage of a smartphone.
Furthermore, we investigate methods for adapting a universal model, which is trained using the data set of all
users, to an individual user through an unsupervised learning scheme. The evaluation results of the experiments
conducted on eight participants indicate that the proposed approach can robustly identify activities in real-time
across multiple individuals: the highest recognition rate achieved 98% after a few runs.
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