An investigation into smartphone based weakly supervised activity recognition systems

William Duffy; Kevin Curran; Daniel Kelly; Tom Lunney

doi:10.1016/j.pmcj.2019.03.005

An investigation into smartphone based weakly supervised activity recognition systems

William Duffy
, Kevin Curran
, Daniel Kelly
, Tom Lunney

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

185 Downloads (Pure)

Abstract

With smart-devices becoming increasingly more commonplace, methods of capturing an individual’s activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition

Original language	English
Article number	56
Pages (from-to)	45-56
Number of pages	12
Journal	Pervasive and Mobile Computing
Volume	56
Early online date	29 Mar 2019
DOIs	https://doi.org/10.1016/j.pmcj.2019.03.005
Publication status	Published (in print/issue) - 1 May 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Gaussian means
Activity recognition
Multiple-instance learning
Random forest
Support vector machines

Access to Document

10.1016/j.pmcj.2019.03.005

An_investigation_into_smartphone_based_weakly_supervised_Pervasive_mobile_computingAuthor Accepted Manuscript, 630 KB

Cite this

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title = "An investigation into smartphone based weakly supervised activity recognition systems",

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author = "William Duffy and Kevin Curran and Daniel Kelly and Tom Lunney",

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AU - Duffy, William

AU - Curran, Kevin

AU - Kelly, Daniel

AU - Lunney, Tom

PY - 2019/5/1

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N2 - With smart-devices becoming increasingly more commonplace, methods of capturing an individual’s activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition

AB - With smart-devices becoming increasingly more commonplace, methods of capturing an individual’s activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition

KW - Gaussian means

KW - Activity recognition

KW - Multiple-instance learning

KW - Random forest

KW - Support vector machines

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UR - https://pure.ulster.ac.uk/en/publications/an-investigation-into-smartphone-based-weakly-supervised-activity

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DO - 10.1016/j.pmcj.2019.03.005

M3 - Article

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SP - 45

EP - 56

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

M1 - 56

ER -

An investigation into smartphone based weakly supervised activity recognition systems

Abstract

UN SDGs

Keywords

Access to Document

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Unobtrusive weakly supervised activity recognition using a single wearable sensor

Kevin Curran

Cite this

An investigation into smartphone based weakly supervised activity recognition systems

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Unobtrusive weakly supervised activity recognition using a single wearable sensor

Profiles

Kevin Curran

Cite this