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Unobstrusive human activity recognition using smartphones and Hidden Markov Models

Unobstrusive human activity recognition using smartphones and Hidden Markov Models
Unobstrusive human activity recognition using smartphones and Hidden Markov Models
Accelerometer data is sufficient to compute human activity recognition, even with only a single accelerometer in use. Such data can be used for many pervasive computing applications, user activity being interpreted as real-time contextual information. This paper investigates activity recognition on smartphones, as they are a suitable platform for the implementation of context-aware pervasive systems. Many machine learning algorithms are suitable for this purpose, but Hidden Markov Models (HMMs) are particularly appropriate for their ability to exploit the sequential and temporal nature of data. This paper evaluates HMMs in unobstrusive activity recognition with the added restrictions resulting from the use of the smartphone platform.
1868-5137
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Streeting, Robert
a85cbf7c-10b3-4c41-acc3-94f98ef5c0c5
Zaluska, Ed
43f6a989-9542-497e-bc9d-fe20f03cad35
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Streeting, Robert
a85cbf7c-10b3-4c41-acc3-94f98ef5c0c5
Zaluska, Ed
43f6a989-9542-497e-bc9d-fe20f03cad35

Wilde, Adriana, Streeting, Robert and Zaluska, Ed (2013) Unobstrusive human activity recognition using smartphones and Hidden Markov Models. Journal of Ambient Intelligence and Humanized Computing. (Submitted)

Record type: Article

Abstract

Accelerometer data is sufficient to compute human activity recognition, even with only a single accelerometer in use. Such data can be used for many pervasive computing applications, user activity being interpreted as real-time contextual information. This paper investigates activity recognition on smartphones, as they are a suitable platform for the implementation of context-aware pervasive systems. Many machine learning algorithms are suitable for this purpose, but Hidden Markov Models (HMMs) are particularly appropriate for their ability to exploit the sequential and temporal nature of data. This paper evaluates HMMs in unobstrusive activity recognition with the added restrictions resulting from the use of the smartphone platform.

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HMM-ARphones-JAIHC.pdf - Author's Original
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More information

Submitted date: 12 June 2013
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 377443
URI: http://eprints.soton.ac.uk/id/eprint/377443
ISSN: 1868-5137
PURE UUID: 0735933e-2e0e-48e4-b989-fb4afbc47621
ORCID for Adriana Wilde: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 15 Jun 2015 11:48
Last modified: 08 Feb 2020 01:34

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Contributors

Author: Adriana Wilde ORCID iD
Author: Robert Streeting
Author: Ed Zaluska

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