SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings using Locomotive Signatures

SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings using Locomotive Signatures

We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our...

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Bibliographic Details
Main Authors: YAN, Zhixian, CHAKRABORTY, Dipanjan, MISRA, Archan, JEUNG, Hoyoung, ABERER, Karl
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
Subjects:
activity recognition
semantic activities
accelerometer
NCCR-MICS
NCCR-MICS/ESDM
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/1521
https://ink.library.smu.edu.sg/context/sis_research/article/2520/viewcontent/MisraAiswc_2tier.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our findings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over directly using statistical features.

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