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A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors

Authors: Tsanousa, Athina; Meditskos, Georgios; Vrochidis, Stefanos; Kompatsiaris, Ioannis;

A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors

Abstract

Following the technological advancement and the constantly emerging assisted living applications, sensor-based activity recognition research receives great attention. Until recently, the majority of relevant research involved extracting knowledge out of single modalities, however, when individual sensors performances are not satisfactory, combining information from multiple sensors can be of use and improve the activity recognition rate. Early and late fusion classifier strategies are usually employed to successfully merge multiple sensors. This paper proposes a novel framework for combining accelerometers and gyroscopes at decision level, in order to recognize human activity. More specifically, we propose a weighted late fusion framework that utilizes the detection rate of a classifier. Furthermore, we propose the modification of an already existing class-based weighted late fusion framework. Experimental results on a publicly available and widely used dataset demonstrated that the combination of accelerometer and gyroscope under the proposed frameworks improves the classification performance.

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Microsoft Academic Graph classification: Fusion Modalities business.industry Computer science Wearable computer Gyroscope Machine learning computer.software_genre Accelerometer law.invention Multiple sensors Activity recognition law Artificial intelligence business computer Classifier (UML)

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    Top 10%
    influence
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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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7
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60
786
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