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Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required.

This report proposed two semi-supervised methods and then investigated how well they perform on a partly labeled dataset, comparing to the state-of-the-art supervised method. One of these methods is designed based on the state-of-the-art supervised method, Deep-ConvLSTM, together with the semi-supervised learning concepts, self-training. Another one is modified based on a semi-supervised deep learning method, LSTM initialized by seq2seq autoencoder, which is firstly introduced for natural language processing. According to the experiments on a published dataset (Opportunity Activity Recognition dataset), both of these semi-supervised methods have better performance than the state-of-the-art supervised methods.

Abstract [sv]

Detta projekt fokuserar på halvövervakad Human Activity Recognition (HAR), där indata delvis är märkta tidsseriedata från sensorer som t.ex. accelerometrar, och utdata är fördefinierade mänskliga aktiviteter. De främsta arbetena inom HAR-området använder numera övervakade metoder, vilka bygger på fullt märkta dataset. Eftersom kostnaden för att märka de samlade instanserna ökar snabbt med den ökade omfattningen av data, föredras numera ofta halvövervakade metoder.

I denna rapport föreslås två halvövervakade metoder och det undersöks hur bra de presterar på ett delvis märkt dataset jämfört med den moderna övervakade metoden. En av dessa metoder utformas baserat på en högkvalitativ övervakad metod, DeepConvLSTM, kombinerad med självutbildning. En annan metod baseras på en halvövervakad djupinlärningsmetod, LSTM, initierad av seq2seq autoencoder, som först införs för behandling av naturligt språk. Enligt experimenten på ett publicerat dataset (Opportunity Activity Recognition dataset) har båda dessa metoder bättre prestanda än de toppmoderna övervakade metoderna.

 

Place, publisher, year, edition, pages
2019. , p. 58
Series
TRITA-EECS-EX ; 2019:9
Keywords [en]
Semi-supervised learning, Sequence learning, Human activity recognization, DeepConvLSTM, Seq2seq model
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-241366OAI: oai:DiVA.org:kth-241366DiVA, id: diva2:1280586
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Supervisors
Examiners
Available from: 2019-02-12 Created: 2019-01-20 Last updated: 2019-02-12Bibliographically approved

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