hkr.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Human Activity Recognition: Deep learning techniques for an upper body exercise classification system
Högskolan Kristianstad, Fakulteten för naturvetenskap.
2019 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
Abstract [en]

Most research behind the use of Machine Learning models in the field of Human Activity Recognition focuses mainly on the classification of daily human activities and aerobic exercises. In this study, we focus on the use of 1 accelerometer and 2 gyroscope sensors to build a Deep Learning classifier to recognise 5 different strength exercises, as well as a null class. The strength exercises tested in this research are as followed: Bench press, bent row, deadlift, lateral rises and overhead press. The null class contains recordings of daily activities, such as sitting or walking around the house. The model used in this paper consists on the creation of consecutive overlapping fixed length sliding windows for each exercise, which are processed separately and act as the input for a Deep Convolutional Neural Network. In this study we compare different sliding windows lengths and overlap percentages (step sizes) to obtain the optimal window length and overlap percentage combination. Furthermore, we explore the accuracy results between 1D and 2D Convolutional Neural Networks. Cross validation is also used to check the overall accuracy of the classifiers, where the database used in this paper contains 5 exercises performed by 3 different users and a null class. Overall the models were found to perform accurately for window’s with length of 0.5 seconds or greater and provided a solid foundation to move forward in the creation of a more robust fully integrated model that can recognize a wider variety of exercises.

sted, utgiver, år, opplag, sider
2019. , s. 27
Emneord [en]
Human Activity Recognition, Deep Learning, Machine Learning, Exercise Classification, Convolutional Neural Network
HSV kategori
Identifikatorer
URN: urn:nbn:se:hkr:diva-19410OAI: oai:DiVA.org:hkr-19410DiVA, id: diva2:1323470
Utdanningsprogram
Bachelor programme in Computer Science and Engineering
Presentation
2019-06-05, 17-327, Elmetorpsvägen 15, Kristianstad, 11:44 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-06-13 Laget: 2019-06-12 Sist oppdatert: 2019-06-13bibliografisk kontrollert

Open Access i DiVA

fulltext(1904 kB)22 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1904 kBChecksum SHA-512
1d5e9c591df6e031d53c4540b37d702273f273a02f6cf115eaef1cf703f53df4247abddc9ac088dfc36d6f600fcb459662990b2a21709692e783f0247bc88a4d
Type fulltextMimetype application/pdf

Søk i DiVA

Av forfatter/redaktør
Nardi, Paolo
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 22 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 84 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf