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Recognizing daily living activity using embedded sensors in smartphones: A data-driven approach

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Publication date15/12/2016
Host publicationAdvanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings
EditorsJianxin Li, Xue Li, Shuliang Wang, Jinyan Li, Quan Z. Sheng
PublisherSpringer Verlag
Pages250-265
Number of pages16
ISBN (Print)9783319495859
<mark>Original language</mark>English
Externally publishedYes
Event12th International Conference on Advanced Data Mining and Applications, ADMA 2016 - Gold Coast, Australia
Duration: 12/12/201615/12/2016

Conference

Conference12th International Conference on Advanced Data Mining and Applications, ADMA 2016
Country/TerritoryAustralia
CityGold Coast
Period12/12/1615/12/16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10086 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Advanced Data Mining and Applications, ADMA 2016
Country/TerritoryAustralia
CityGold Coast
Period12/12/1615/12/16

Abstract

Smartphones are widely available commercial devices and using them as a basis to creates the possibility of future widespread usage and potential applications. This paper utilizes the embedded sensors in a smartphone to recognise a number of common human actions and postures. We group the range of all possible human actions into five basic action classes, namely walking, standing, sitting, crouching and lying. We also consider the postures pertaining to three of the above actions, including standing postures (backward, straight, forward and bend), sitting postures (lean, upright, slouch and rest) and lying postures (back, side and stomach). Training data was collected through a number of people performing a sequence of these actions and postures with a smartphone in their shirt pockets. We analysed and compared three classification algorithms, namely k Nearest Neighbour (kNN), Decision Tree Learning (DTL) and Linear Discriminant Analysis (LDA) in terms of classification accuracy and efficiency (training time as well as classification time). kNN performed the best overall compared to the other two and is believed to be the most appropriate classification algorithm to use for this task. The developed system is in the form of an Android app. Our system can real-time accesses the motion data from the three sensors and on-line classifies a particular action or posture using the kNN algorithm. It successfully recognizes the specified actions and postures with very high precision and recall values of generally above 96%.