A lightweight hierarchical activity recognition framework using smartphone sensors

Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2014-09, Vol.14 (9), p.16181-16195
Main Authors: Han, Manhyung, Bang, Jae Hun, Nugent, Chris, McClean, Sally, Lee, Sungyoung
Format: Article
Language:English
Subjects:
Accelerometry - instrumentation
Accuracy
Actigraphy - instrumentation
activity recognition
Algorithms
Artificial Intelligence
Cell Phone
Cellular telephones
Classification
Computer engineering
Equipment Design
Equipment Failure Analysis
Humans
Information Storage and Retrieval - methods
life-log
Machine learning
Monitoring, Ambulatory - instrumentation
multimodal sensors
naïve Bayes
Pattern Recognition, Automated - methods
Sensors
smartphone
Smartphones
Software
Support vector machines
Transducers
User-Computer Interface
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Summary:Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s140916181